Estimating SARS-CoV-2 Infections from Deaths, Confirmed Cases, Tests, and Random Surveys
EEstimating SARS-CoV-2 Infections from Deaths,Confirmed Cases, Tests, and Random Surveys
Nicholas J. Irons ∗ and Adrian E. Raftery ∗ †‡ University of WashingtonFebruary 23, 2021
Abstract
There are many sources of data giving information about the number of SARS-CoV-2infections in the population, but all have major drawbacks, including biases and delayedreporting. For example, the number of confirmed cases largely underestimates the numberof infections, deaths lag infections substantially, while test positivity rates tend to greatlyoverestimate prevalence. Representative random prevalence surveys, the only putativelyunbiased source, are sparse in time and space, and the results come with a big delay. Reliableestimates of population prevalence are necessary for understanding the spread of the virusand the effects of mitigation strategies. We develop a simple Bayesian framework to estimateviral prevalence by combining the main available data sources. It is based on a discrete-time SIR model with time-varying reproductive parameter. Our model includes likelihoodcomponents that incorporate data of deaths due to the virus, confirmed cases, and thenumber of tests administered on each day. We anchor our inference with data from randomsample testing surveys in Indiana and Ohio. We use the results from these two states tocalibrate the model on positive test counts and proceed to estimate the infection fatalityrate and the number of new infections on each day in each state in the USA. We estimatethe extent to which reported COVID cases have underestimated true infection counts, whichwas large, especially in the first months of the pandemic. We explore the implications of ourresults for progress towards herd immunity.
SARS-CoV-2 test data are fraught with biases that obscure the true rate of infection in thepopulation. Lack of access to viral tests, which was particularly pronounced in the early ∗ Department of Statistics † Department of Sociology ‡ To whom correspondence should be addressed. E-mail: [email protected] a r X i v : . [ s t a t . A P ] F e b ays of the pandemic, in conjunction with selection bias due to asymptomatic and mildinfections yield case counts that tend to underestimate the true number of infections in thepopulation. By the same token, test positivity rates tend to overestimate viral prevalence.Hospitalization rates and emergency room visits do not estimate the overall infection rate,and are not comparable between states or counties, or over time. Reported deaths due toCOVID are considered less problematic as an estimate of the true death count and providea more accurate reflection of the course of the pandemic [22].We combine several of the main sources of data relevant to the number of infectionsusing a simple Bayesian model that accounts for the biases and delays in the data. Ourmodel relies on data on deaths due to COVID, confirmed cases, and testing reported bythe COVID Tracking Project [28]. We use a modified Susceptible-Infected-Removed (SIR)model, a compartmental epidemiological model widely used to simulate the spread of diseasein a population. We combine this with a Poisson likelihood for death counts and a normallikelihood for estimates of viral and seroprevalence from random sample testing surveysconducted in Indiana and Ohio [20, 15].With these data we infer the infection fatality rate (IFR) and obtain statistically prin-cipled estimates of the number of new infections on each day since March 2020 in Indianaand Ohio. We then leverage our results from these states to build a model for confirmedcases that accounts for preferential testing as a function of the cumulative number of testsadministered in each state. This allows us to pin down the IFR and infection counts for thevast majority of states that have not conducted representative testing surveys.Our simple Bayesian model takes inspiration from Johndrow et al. [12], although it differsin significant ways. Whereas Johndrow et al. model the effect of social distancing measuresby allowing the SIR contact parameter to change pre- and post-lockdown, we allow it to varyin time to account for fluctuation in the tightening and loosening of restrictions, as well asin adherence to the restrictions. Furthermore, we incorporate testing data, develop a novelpreferential testing model, and include the IFR as a parameter in the model to be estimated,rather than a fixed constant. Finally, to simplify model implementation we use a discretetime SIR model, rather than a continuous time model based on differential equations. We first define our discrete-time SIR model for infections in each state. Let S t denote thenumber of susceptible people in the population on day t , I t the number of infections, and R t the number removed. The number removed includes those who have died of the diseaseand those who have recovered, and are assumed immune for the rest of the period of ourstudy. With N denoting the state population, these quantities evolve in time according to2he equations S t +1 − S t = − β t N I t S t ,I t +1 − I t = β t N I t S t − γI t ,R t +1 − R t = γI t . (1)Note that ν t = S t − − S t is the number of new infections on day t . We allow theparameters β t , interpreted as the mean number of contacts per person on day t , to vary overtime. This accounts for variation in exposure due to implementation or loosening of socialdistancing and other policy measures over time. We model β t as a random walk with stepsize σ estimated from the data, β t +1 ∼ Normal( β t , σ ) . We assume that γ − , the averagelength in days of the infectious period, is determined by the disease and is therefore constantover time. Let τ = { τ , τ , . . . , τ m } denote the distribution of time to death for those infected individualswho die from the disease, i.e., τ s is the probability of death s days after infection, conditionalon death occurring. Similar to Johndrow et al., who calibrate τ by matching quantiles of anegative binomial distribution to case data from China [34, 17], we assume that τ followsa NegativeBinomial( α, / ( β + 1)) distribution with parameters α = 21 , β = 1 .
1, and wetruncate the distribution at the 99th percentile, or m = 40 days, to rule out extremelydelayed deaths. We denote by D t the reported deaths due to COVID on day t , whichwe obtain from the COVID Tracking Project [28]. We link the daily new infection counts ν = ( ν t ) t to reported deaths via the likelihood D t ind. ∼ Poisson (cid:0)
IFR (cid:80) tk =1 ν k τ t − k (cid:1) . To pin down the IFR, we add likelihood components incorporating the Indiana and Ohioprevalence survey data [20, 15]. Active viral prevalence in Indiana in the period April 25–29,2020 was estimated as ˆ θ v = 1 . θ v ∼ Normal (cid:16) θ v , θ v (1 − θ v ) n v (cid:17) , where θ v = ( (cid:80) T t = T I t ) /N ( T − T )is the average viral prevalence between days T = April 25 and T = April 29. Here n v =3 ,
605 is the number of viral tests administered. Similarly, the estimated seroprevalencein the testing period, ˆ θ s = 1 . θ s ∼ Normal (cid:16) θ s , θ s (1 − θ s ) n s (cid:17) , where θ s = (cid:80) T t = T R t /N ( T − T ) and n s = 3518. These results come from the first phase of the Indianaprevalence survey described in Menachemi et al. [20]. Due to low response rates – less than8% in the second and third phases – we do not include data from the subsequent phasesof the study in our analysis. The response rates reported in Ohio and in the first phase inIndiana were significantly higher at 18.5% and 23.4%, respectively.The likelihood for the prevalence survey data from Ohio is analogous. As reported in[15], the estimated seroprevalence in the state is ˆ θ s = 1 .
3% in the period July 9–28, witha sample size of n s = 667. Results from the PCR tests in the same study were reported3n a press conference on October 1 available on YouTube [29]. The viral prevalence in thatperiod is estimated as ˆ θ v = 0 .
9% with sample size n v = 727. To the best of our knowledge,these numbers have not yet been published. As shown in Figures 2 and 3, the undercount curve ( I t + R t ) / ( (cid:80) k ≤ t C k ) has a common shapein Indiana and Ohio. Here, I t and R t are the SIR parameters on day t and C t are the positivetests in the state on day t , as reported by the COVID Tracking Project [28]. We found thatthe reciprocal of the undercount is approximately linear when plotted against the squareroot of the cumulative number of tests administered in the state on each day, and that theslopes of these lines for the two states are similar; see Figure 1.This led to the following model for the test data: t (cid:88) k =1 C k ∼ Normal (cid:0) φ t ( I t + R t ) , η t (cid:1) . (2)Here the parameters φ t and η t are proportional to the square root of the fraction of thepopulation tested up to day t , φ t = φ (cid:115) (cid:80) tk =1 T k N , η t = η (cid:80) tk =1 T k N , so that φ t is the overall fraction of infections that appear in the cumulative number of positivetests. We assume that this fraction grows as the state’s test capacity ramps up and that thevariance in this relationship, η t , grows linearly with the total number of tests administered.To arrive at the distribution in (2), we can model the cases on each day independentlyas C t ind. ∼ Normal (cid:18) φ t ( I t + R t ) − φ t − ( I t − + R t − ) , η T t N (cid:19) . (3)Noting that ν t = ( I t + R t ) − ( I t − + R t − ) , we can write the mean of C t as φ t · ν t + ( φ t − φ t − )( I t − + R t − ) . Hence, in expectation C t can be decomposed as a fraction of the new infections on day t , ν t ,and a smaller fraction of the cumulative incidence on day t − I t − + R t − .In fitting the model, we do not use the likelihood on each day (3) due to inconsistentreporting of cases and tests, as well as weekly oscillations in these numbers due to reducedreporting on weekends. Rather, in each state we combine cases and tests into non-overlappingconsecutive L -day periods, where L is at least 7 to account for weekend effects, and modelthe counts in these periods independently.We first fit the model in Indiana and Ohio without the likelihood on cases described insection 2.4. That is, initially we used only deaths data and the random sample surveys in4ach state. With the resulting posterior samples of cumulative incidence I t + R t on eachday, we arrived at the likelihood on cases. Figure 1 demonstrates the relationships definedin equations (2) and (3). We refer to the normal means in (2) and (3) (divided by theparameter φ ) as the cumulative and marginal regression functions, respectively. The lowerpanels of Figure 1 reveal a comparable slope φ for Indiana and Ohio after a brief initialperiod when testing and cases were very low. The widening confidence intervals in the upperpanels exhibit the growth of the variance in (2) as a function of cumulative testing.A number of other models for case and test data have been proposed. Campbell et al.introduced a binomial likelihood on cases, C t ∼ Binomial( T t , − (1 − I t /N ) α ), where I t /N isthe infection rate on day t and α > − (1 − I t /N ) α ≈ αI t /N . An application of Bayes’ rule to thelatter model shows that α = P (tested | infected) /P (tested). This model has some limitationsin the context of our study. Firstly, the degree of preferential testing α is likely to decreaseas testing increases, and it is not obvious how one might parametrize α = α t to account forthis. Secondly, the model is not additive, as the test positivity relies on the active infectionrate. As a result, it is not well suited to handling state-level testing data, which can beunreliable on the daily level.Youyang Gu [8] and Peter Ellis [6] proposed similar models to correct case counts usingtest positivity rates. They take the form ν t = C t [ m · ( C t /T t ) k + b ] where m > , k ∈ [0 , , b ≥ t by the numberof positive tests on day t scaled by a multiplicative factor depending on the number of testsadministered on day t as a fraction of the state population. These models are susceptibleto the same issues as that of Campbell et al. They rely on daily test positivity rates, whichare reported inconsistently across states [14]. And as Youyang Gu notes, the parametersestimated at one point in time do not carry over to other time periods [8]. Furthermore, byassuming that new infections are a function only of cases and tests on that day, these modelsignore the lag between infections and their confirmation via testing. They also presume thatthere are no new infections on days in which no positive tests are reported. Our likelihoodon cases (3) allows for new infections to be reflected in case counts at a later date. Lastly, we specify prior distributions for the model parameters { IFR , β , σ, γ − , ( S , I ) , φ, η } .We use a weakly informative Uniform(0 , .
03) prior distribution for the IFR in each state.For Indiana, we use a truncated normal prior for the mean infectious period, γ − ∼ Normal [5 . , . (8 . , . ). This is motivated by clinical data, which show that most infectedindividuals remain infectious no longer than 10 days after symptom onset [5, 31, 1, 3, 18, 24,16, 30], and that patients can be highly infectious several days before symptom onset [10].We assume that the removal rate γ is determined by the disease and so does not varybetween states. Therefore, after fitting the model to the data for Indiana, we use the posteriordistribution of γ for Indiana as the prior distribution of γ for Ohio. We then use the posteriordistribution from Ohio as the prior distribution for the remaining states, each of which5igure 1: Upper panels: Posterior median and 95% confidence bands for the cumulativeregression function in equation (2) plotted against cumulative cases in Indiana and Ohio.Lower panels: Positive tests on each day plotted against the posterior mean of the marginalregression function in equation (3). LOESS curves are plotted in red.6e model independently. The prior distributions of the remaining parameters are diffuseindependent uniform priors. To estimate φ , we use the same process as described for γ . We built the model in R and fit it with the RStan software package, which implements theNo-U-Turn-Sampler for Bayesian inference [25, 27, 11]. For each state, we ran 4 chains inparallel for 20,000 steps each with the first 10,000 as burn-in to obtain 40,000 samples fromthe posterior distribution of the model parameters.
Here we present detailed results for Indiana, Ohio, and Connecticut – which, to our knowl-edge, are the only states that have conducted representative testing surveys – as well as NewYork, which has the highest number of reported deaths due to COVID. We also present ag-gregated estimates for the entire United States. Table 1 in the appendix includes estimatesof the IFR and the cumulative incidence (i.e, the percent of the state’s population havingbeen infected) and undercount factor as of January 6, 2021 for the 50 states and the Districtof Columbia. Results for the 50 states and DC are shown in the appendix.We have created an online dashboard where updated results can be found, includingestimated daily infections, the IFR, and the reproductive number r ( t ) in each state. We estimate an IFR of 0.73% (95% interval 0.61–0.88) and a cumulative incidence of 20.5%(17.1–24.5) in Indiana as of January 6, 2021. There have been 2.6 (2.1–3.1) infections forevery confirmed case in the state through this date. This suggests that a large majorityof infections in the course of the pandemic have gone unreported, although Figure 1 showsthat undercounting was most pronounced early on and has improved substantially overtime. Figure 1 exhibits posterior estimates of new infections on each day, ν t , as well asthe cumulative undercount factor, which is the ratio of estimated cumulative infections tocumulative confirmed cases. Figure 2 displays the viral prevalence, the cumulative incidence,and the reproductive number r ( t ) = β t /γ on each day.By the time that the first confirmed case was reported in Indiana on March 6, 2020, therehad likely been more than 1,000 infections in the state (95% interval 539–1,526). We estimatethat as of May 1 there were 272,000 cumulative infections (95% interval 228,000–323,000),compared to 18,630 confirmed cases by that date. This yields a cumulative incidence of 4.0%(3.4–4.8) and an undercount factor of 14.6 (12.3–17.4). This estimate is comparable to othersin the literature for that period [12, 32, 9]. Between the 16th and 19th of March, the state’sGovernor Eric Holcomb ordered a stop to indoor dining, declared a state of emergency, and https://rsc.stat.washington.edu/covid-dashboard ndiana Figure 2: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount in Indiana from March 2020 to January 2021.In the top left panel, deaths divided by the posterior median IFR are plotted in grey forcomparison. 8losed schools; on March 23rd he issued a stay-at-home order. According to our model, thefirst wave of infections reached its peak about two weeks later in early April. Ohio
Figure 3: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount in Ohio from March 2020 to January 2021. In thetop left panel, deaths divided by the posterior median IFR are plotted in grey for comparison.We estimate an IFR of 0.57% (95% interval 0.47–0.69) in Ohio. As of January 6, 2021,9he cumulative incidence in the state was 16.9% (14.2–20.2) and the cumulative undercountfactor was 2.7 (2.2–3.2).Ohio Governor Mike Dewine declared a state of emergency on March 9th and the state’sfirst stay-at-home order took effect on March 23rd. In mid-April, the Governor declaredthat businesses could begin to reopen on May 1st. Figure 3 shows that the first wave ofinfections, which picked up in March and likely peaked by late April, did not die out butrather leveled out to a sustained spread through the summer of 2020. The posterior medianof the reproductive number r ( t ) in the state hovered around 1 from early April throughmid-September and increased thereafter as the second wave of infections began in the fall. We estimate an IFR of 1.54% (95% interval 1.22–1.94) in Connecticut. Further, as of Jan-uary 6, 2021, 12.9% (10.4–16.1) of the state’s population has been infected, leading to anundercount factor of 2.3 (1.9–2.9).According to our model as of April 30, 2020, 5.6% (4.4–7.0) of the state’s population hadrecovered from COVID. In comparison, Havers et al. estimated a seroprevalence of 4.9%(95% interval 3.6–6.5) in the state in the period April 26–May 3 [9]. That study relied on aconvenience sample of residual blood specimens collected for clinical purposes, and so it mayhave been affected by selection bias, as well as imperfect sensitivity and specificity of theantibody test used. Nevertheless, their estimate agrees well with the result from our model.By July 5, 2020, our estimate of the recovered population increased to 7.9% (6.3–10.0). Bycomparison, in a random sample blood test survey, Mahajan et al. reported a seroprevalenceof 4.0% (90% interval 2.0–6.0) for the period June 10–July 29 [19], which is significantlylower. While our estimates disagree with those of Mahajan et al., we note that the surveyresponse rate was low at 7.8%, raising the possibility of nonresponse bias. For this reason,we did not include the Connecticut survey as a source of data in our analysis.
We estimate an IFR of 1.26% (95% interval 0.97–1.67) for New York state. As of January6, 2021, 14.5% (11.1–18.8) of the state has been infected, yielding an undercount factor of2.7 (2.0–3.5) through that date.We know of no other estimates of the IFR in New York in the literature. However, Yanget al. estimated an IFR of 1.39% (95% interval 1.04–1.77) for the first wave in New YorkCity through June 6, 2020, based on available testing, mortality, and mobility data [33].According to NYC Health Department data [23], this period accounted for more than 85%of COVID deaths in the city and 57% of all confirmed COVID deaths (not including probabledeaths) in the state through the first week of January 2021. As such, we expect the IFR forthe state as a whole to have been similar to that of NYC during the spring of 2020, and ourresults are consistent with those of Yang et al.We estimate that by June 6, 10.2% of the state’s population (95% interval 7.7–13.2),or about 2 million people, had been infected with the novel coronavirus. Multiplying that10umber by the fraction of confirmed COVID deaths in the state occurring in NYC duringthat period yields 1.5 million infections, or 18% of the city’s population. This number iscompatible with that of Stadlbauer et al., who measured 20% seroprevalence in NYC at thattime based on randomly sampled residual plasma collected from patients at Mount SinaiHospital scheduled for routine care visits unrelated to COVID-19 [26].
We summed posterior samples of the SIR trajectories from all the states to obtain estimatesof viral prevalence in the United States on each day. The results are summarized in Figure4. For each sampled trajectory of the infection curve, we calculated an effective contactparameter β t for the entire country for each day from the SIR equations (1).As of January 6, 2021, we estimate that 16.4% (95% interval 15.8–17.2) of the US pop-ulation, or about 54 million people, had been infected with SARS-CoV-2. This suggeststhat the USA was far from reaching herd immunity and that it was unlikely to do so frominfections alone in the short term while state and local governments continue to implementlockdowns and other mitigations. Up to that date, we estimate that one out of every 2.6 in-fections (2.5–2.7) in the US had been confirmed via testing. This implies that approximately60% of all infections in the country have gone unreported.In the top left panel of Figure 4, which exhibits estimates of new infections on each dayin the US, we plot reported COVID deaths per 1000 population shifted back 23 days (whichis the mean of the time-to-death distribution τ ). In the plot, we divide deaths per 1000by 0.0068. This is the point estimate of IFR reported by Meyerowitz-Katz and Merone intheir meta-analysis of 24 IFR estimates from a wide range of countries published betweenFebruary and June 2020 [21]. The two curves have a substantial overlap, suggesting that theIFR implied by our estimates of true infections in the USA is consistent with their findings. We conducted a simulation study to assess the implications of our results for herd immunity inthe US. We project the SIR model for the US forward from January 6, 2021, and incorporatevaccine administration into the dynamics. We make the following strong assumptions:1. Recovered individuals are immune to the virus, i.e., reinfection does not occur.2. Immunity is conferred after receiving the second vaccine dose. The number of individ-uals receiving the second dose increases linearly from 0 to 500–750 thousand per dayfrom early January to late February, and remains at that level thereafter. This alignswith President Biden’s goal of 1.0–1.5 million doses per day in the first 100 days of hisadministration.3. Previously infected individuals who have tested positive for the virus do not receivethe vaccine. All others are equally likely to be vaccinated.11 SA Figure 4: Aggregated estimates of new infections, cumulative incidence, r ( t ), and cumulativeundercount for the United States from March 2020 to January 2021. In the top left panel,deaths (in thousands) divided by 0.0068 and shifted back 23 days are plotted in grey forcomparison. 12. The fraction of infections confirmed by testing (i.e., the reciprocal of the cumulativeundercount) does not change after January 6. Similarly, the reproductive number r ( t )remains fixed after January 6. USA
Figure 5: 95% credible intervals for new infections and cumulative immunity (viral incidenceand second vaccinations) in the US projected out to August 2021.The first point merits further discussion. Our projections that follow are particularlysensitive to this assumption. It may turn out that individuals who have been vaccinatedor previously infected are still susceptible to new variants of the virus that are croppingup and will continue to spread. It is also possible that the natural immunity conferred byasymptomatic and mild infections that elicited minimal immune response, which constitutea large portion of the total, will not last long enough to prevent widespread reinfection inthe next few months. In either case, if Assumption 1 is violated then we may experiencefurther waves of infection and delayed progress towards herd immunity.We project the 40,000 samples from the posterior distribution of the US infection trajec-tory forward under the modified SIR model described above. New infections and cumulativeimmunity (the percentage of the population previously infected or fully vaccinated) on eachday are plotted in Figure 5. Based on our simulation, we find that the number of new in-fections per day in the country would likely fall below 5,000, about one hundredth of thesecond wave peak, by July 2021, if our assumptions are valid. At this point, the virus’spread through the population will have been effectively suppressed. In getting there, it isplausible that we will incur another 30–50 million new infections, beginning from January 6.These numbers are obtained as the interquartile range of the projected cumulative incidence.Note that at that point, our model suggests that cumulative immunity will be 70% or less,although if our assumptions are violated, it could be higher.13o put this in perspective, there were about 360,000 confirmed COVID deaths and 54million infections (by our reckoning) as of January 6, 2021. Assuming an IFR of 0.68%, thiswould lead to an additional 200–350 thousand COVID deaths. (However, given that vaccineadministration is prioritized for high risk groups, the effective IFR in the coming monthscould decline significantly, which would lead to fewer deaths.) We find that the projectionsgiven here are not very sensitive to plausible modifications of Assumptions 2–4 (e.g., thatindividuals with confirmed cases can receive the vaccine).
To craft and implement effective policy and mitigation strategies, policymakers need reliableassessments of the impact of previous non-pharmaceutical interventions on the transmissionrate of the disease. We have developed a simple Bayesian model of the dynamics of SARS-CoV-2 transmission incorporating readily available time series data tracking the virus, as wellas statewide representative point prevalence surveys conducted in Indiana and Ohio, whichare the highest quality random testing surveys carried out to date. We present estimatesof the infection fatality rate and the time-varying viral prevalence and reproductive number r ( t ) in each US state on each day. Our results indicate that a large majority of COVIDinfections go unreported. Even so, we find that the US was still far from reaching herdimmunity to the virus in early January 2021 from infections alone. This suggests thatcontinued mitigation and an aggressive vaccination effort are necessary to surpass the herdimmunity threshold without incurring many more deaths due to the disease. We hope thatthis work demonstrates the value of random sample COVID testing in our ongoing pandemicresponse.By incorporating testing and case data aggregated over any period of time, our additivemodel for positive tests in equation (2) allows us to avoid using data at the daily level, whichcan be very unreliable. For example, the reported cumulative number of tests administeredin a state may not be updated for up to two weeks at a time, or it may decrease from oneday to the next as data are deduplicated upon further review. The latter scenario frequentlyoccurs with reported cases as well. Working with data at the daily level generally requiresusing some kind of moving average, which washes out stochasticity in the data and leads tooversmoothing inconsistent with the high overdispersion of SARS-CoV-2 transmission [7].Our inference relies on daily reported deaths due to COVID in each state as opposedto excess deaths. Because of the possibility of death misclassification, excess death datarepresent a mix of confirmed COVID deaths and deaths from other causes. Nevertheless,relying on reported deaths is a potential source of bias, as they are affected by the accuracyof cause-of-death determinations. Their numbers can fall significantly below excess deathcounts and may undershoot the true number of deaths due to the disease [22]. Ascertainmentof COVID deaths may vary widely between states, with the cumulative excess death countsince the start of the pandemic exceeding reported COVID deaths by upwards of 50% in somestates, according to a New York Times analysis of CDC mortality data [13]. Consequently,our results may underestimate viral incidence in those states.14 cknowledgments This research was supported by NIH grant R01 HD-070936.
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The Lancet , 395(10229):1054–1062, 2020. https://doi.org/10.1016/S0140-6736(20)30566-3 . tate IFR (%) Cumulative Incidence (%) Undercount Alabama 0.48 (0.39–0.58) 25.9 (21.6–31.3) 3.3 (2.8–4.0)Alaska 0.31 (0.25–0.38) 10.6 (9.1–12.5) 1.6 (1.4–1.9)Arizona 0.84 (0.68–1.03) 19.7 (16.3–24.0) 2.5 (2.1–3.1)Arkansas 0.88 (0.72–1.07) 18.4 (15.4–22.3) 2.3 (1.9–2.8)California 0.62 (0.51–0.73) 14.8 (12.5–17.7) 2.4 (2.0–2.9)Colorado 0.64 (0.53–0.75) 14.9 (12.7–17.9) 2.5 (2.1–3.0)Connecticut 1.54 (1.22–1.94) 12.9 (10.4–16.1) 2.3 (1.9–2.9)Delaware 0.79 (0.64–0.96) 14.1 (11.8–17.1) 2.2 (1.9–2.7)Florida 0.76 (0.62–0.93) 15.5 (12.8–18.9) 2.5 (2.0–3.0)Georgia 0.82 (0.67–0.99) 15.2 (12.7–18.5) 2.7 (2.3–3.3)Hawaii 0.52 (0.41–0.65) 4.5 (3.7–5.5) 2.8 (2.3–3.4)Idaho 0.30 (0.25–0.35) 31.4 (27.0–37.5) 4.0 (3.4–4.7)Illinois 0.98 (0.81–1.17) 17.0 (14.3–20.6) 2.2 (1.8–2.6)Indiana 0.73 (0.61–0.88) 20.5 (17.1–24.5) 2.6 (2.1–3.1)Iowa 0.58 (0.48–0.71) 24.2 (20.0–29.4) 3.1 (2.6–3.8)Kansas 0.51 (0.41–0.63) 25.5 (20.9–31.1) 3.1 (2.6–3.8)Kentucky 0.45 (0.37–0.52) 16.7 (14.3–19.9) 2.6 (2.2–3.1)Louisiana 1.14 (0.92–1.38) 16.7 (13.9–20.5) 2.3 (1.9–2.9)Maine 0.94 (0.73–1.31) 4.9 (3.9–6.0) 2.4 (1.9–3.0)Maryland 0.98 (0.79–1.19) 11.5 (9.5–14.1) 2.4 (2.0–2.9)Massachusetts 1.89 (1.50–2.38) 11.5 (9.2–14.4) 2.0 (1.6–2.5)Michigan 1.27 (1.00–1.66) 13.0 (10.2–16.3) 2.4 (1.9–3.0)Minnesota 0.63 (0.53–0.75) 16.8 (14.2–20.1) 2.2 (1.9–2.7)Mississippi 0.79 (0.65–0.95) 25.3 (21.3–30.5) 3.3 (2.8–4.0)Missouri 0.60 (0.50–0.72) 18.0 (15.1–21.7) 2.7 (2.3–3.3)Montana 0.53 (0.45–0.63) 19.3 (16.7–22.9) 2.5 (2.2–3.0)Nebraska 0.50 (0.41–0.59) 18.8 (16.0–22.7) 2.1 (1.8–2.6)Nevada 0.62 (0.51–0.72) 19.8 (17.0–23.7) 2.6 (2.2–3.1)New Hampshire 0.84 (0.69–1.01) 9.5 (8.1–11.4) 2.7 (2.3–3.2)New Jersey 1.44 (1.11–1.90) 17.3 (13.3–22.2) 2.8 (2.1–3.6)New Mexico 0.90 (0.75–1.04) 16.1 (13.9–19.1) 2.2 (2.0–2.7)New York 1.26 (0.97–1.67) 14.5 (11.1–18.8) 2.7 (2.0–3.5)North Carolina 0.58 (0.48–0.68) 14.1 (11.9–16.8) 2.6 (2.2–3.1)North Dakota 0.88 (0.73–1.03) 20.6 (17.8–24.5) 1.7 (1.4–2.0)Ohio 0.57 (0.47–0.69) 16.9 (14.2–20.2) 2.7 (2.2–3.2)Oklahoma 0.40 (0.34–0.47) 20.6 (17.8–24.3) 2.6 (2.3–3.1)Oregon 0.56 (0.47–0.66) 7.4 (6.4–8.8) 2.7 (2.3–3.2)Pennsylvania 0.82 (0.67–0.98) 21.2 (17.8–25.6) 4.0 (3.3–4.8)Rhode Island 1.34 (1.09–1.63) 15.0 (12.5–18.3) 1.7 (1.4–2.0)South Carolina 0.78 (0.64–0.95) 16.9 (14.1–20.7) 2.6 (2.2–3.2)South Dakota 0.52 (0.43–0.62) 35.7 (30.5–42.8) 3.2 (2.7–3.8)Tennessee 0.74 (0.60–0.90) 19.1 (15.9–23.2) 2.1 (1.8–2.6)Texas 0.80 (0.62–1.11) 16.4 (12.5–20.5) 2.6 (2.0–3.2)Utah 0.21 (0.17–0.24) 23.2 (20.1–27.5) 2.6 (2.2–3.1)Vermont 1.01 (0.76–1.37) 2.8 (2.2–3.5) 2.2 (1.7–2.7)Virginia 0.57 (0.47–0.68) 12.9 (10.9–15.6) 3.0 (2.5–3.6)Washington 0.56 (0.43–0.78) 10.1 (7.6–13.0) 3.0 (2.3–3.9)West Virginia 0.97 (0.81–1.14) 11.8 (10.2–14.0) 2.2 (1.9–2.6)Wisconsin 0.52 (0.43–0.61) 19.9 (17.0–23.9) 2.2 (1.9–2.6)Wyoming 0.51 (0.42–0.61) 18.0 (15.5–21.4) 2.2 (1.9–2.6)District of Columbia 1.16 (0.91–1.48) 10.2 (8.1–12.9) 2.4 (1.9–3.0)
Table 1: Posterior median and 95% intervals for IFR, cumulative incidence as of January 6,2021, and undercount factor as of January 6, 2021.19 laska
Figure 6: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.20 labama Figure 7: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.21 rkansas Figure 8: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.22 rizona Figure 9: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.23 alifornia Figure 10: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.24 olorado Figure 11: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.25 onnecticut Figure 12: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.26 istrict of Columbia Figure 13: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.27 elaware Figure 14: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.28 lorida Figure 15: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.29 eorgia Figure 16: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt
Figure 6: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.20 labama Figure 7: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.21 rkansas Figure 8: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.22 rizona Figure 9: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.23 alifornia Figure 10: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.24 olorado Figure 11: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.25 onnecticut Figure 12: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.26 istrict of Columbia Figure 13: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.27 elaware Figure 14: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.28 lorida Figure 15: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.29 eorgia Figure 16: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.30 awaii Figure 17: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.31 owa Figure 18: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.32 daho Figure 19: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.33 llinois Figure 20: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.34 ndiana Figure 21: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.35 ansas Figure 22: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.36 entucky Figure 23: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt
Figure 6: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.20 labama Figure 7: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.21 rkansas Figure 8: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.22 rizona Figure 9: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.23 alifornia Figure 10: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.24 olorado Figure 11: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.25 onnecticut Figure 12: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.26 istrict of Columbia Figure 13: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.27 elaware Figure 14: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.28 lorida Figure 15: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.29 eorgia Figure 16: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.30 awaii Figure 17: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.31 owa Figure 18: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.32 daho Figure 19: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.33 llinois Figure 20: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.34 ndiana Figure 21: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.35 ansas Figure 22: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.36 entucky Figure 23: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.37 ouisiana Figure 24: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.38 assachusetts Figure 25: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.39 aryland Figure 26: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt
Figure 6: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.20 labama Figure 7: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.21 rkansas Figure 8: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.22 rizona Figure 9: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.23 alifornia Figure 10: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.24 olorado Figure 11: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.25 onnecticut Figure 12: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.26 istrict of Columbia Figure 13: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.27 elaware Figure 14: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.28 lorida Figure 15: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.29 eorgia Figure 16: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.30 awaii Figure 17: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.31 owa Figure 18: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.32 daho Figure 19: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.33 llinois Figure 20: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.34 ndiana Figure 21: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.35 ansas Figure 22: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.36 entucky Figure 23: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.37 ouisiana Figure 24: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.38 assachusetts Figure 25: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.39 aryland Figure 26: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.40 aine Figure 27: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.41 ichigan Figure 28: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt
Figure 6: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.20 labama Figure 7: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.21 rkansas Figure 8: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.22 rizona Figure 9: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.23 alifornia Figure 10: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.24 olorado Figure 11: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.25 onnecticut Figure 12: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.26 istrict of Columbia Figure 13: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.27 elaware Figure 14: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.28 lorida Figure 15: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.29 eorgia Figure 16: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.30 awaii Figure 17: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.31 owa Figure 18: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.32 daho Figure 19: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.33 llinois Figure 20: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.34 ndiana Figure 21: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.35 ansas Figure 22: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.36 entucky Figure 23: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.37 ouisiana Figure 24: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.38 assachusetts Figure 25: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.39 aryland Figure 26: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.40 aine Figure 27: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.41 ichigan Figure 28: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.42 innesota Figure 29: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.43 issouri Figure 30: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.44 ississippi Figure 31: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt
Figure 6: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.20 labama Figure 7: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.21 rkansas Figure 8: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.22 rizona Figure 9: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.23 alifornia Figure 10: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.24 olorado Figure 11: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.25 onnecticut Figure 12: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.26 istrict of Columbia Figure 13: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.27 elaware Figure 14: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.28 lorida Figure 15: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.29 eorgia Figure 16: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.30 awaii Figure 17: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.31 owa Figure 18: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.32 daho Figure 19: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.33 llinois Figure 20: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.34 ndiana Figure 21: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.35 ansas Figure 22: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.36 entucky Figure 23: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.37 ouisiana Figure 24: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.38 assachusetts Figure 25: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.39 aryland Figure 26: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.40 aine Figure 27: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.41 ichigan Figure 28: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.42 innesota Figure 29: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.43 issouri Figure 30: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.44 ississippi Figure 31: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.45 ontana Figure 32: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.46 orth Carolina Figure 33: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.47 orth Dakota Figure 34: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.48 ebraska Figure 35: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.49 ew Hampshire Figure 36: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt
Figure 6: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.20 labama Figure 7: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.21 rkansas Figure 8: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.22 rizona Figure 9: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.23 alifornia Figure 10: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.24 olorado Figure 11: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.25 onnecticut Figure 12: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.26 istrict of Columbia Figure 13: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.27 elaware Figure 14: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.28 lorida Figure 15: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.29 eorgia Figure 16: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.30 awaii Figure 17: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.31 owa Figure 18: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.32 daho Figure 19: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.33 llinois Figure 20: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.34 ndiana Figure 21: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.35 ansas Figure 22: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.36 entucky Figure 23: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.37 ouisiana Figure 24: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.38 assachusetts Figure 25: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.39 aryland Figure 26: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.40 aine Figure 27: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.41 ichigan Figure 28: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.42 innesota Figure 29: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.43 issouri Figure 30: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.44 ississippi Figure 31: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.45 ontana Figure 32: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.46 orth Carolina Figure 33: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.47 orth Dakota Figure 34: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.48 ebraska Figure 35: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.49 ew Hampshire Figure 36: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.50 ew Jersey Figure 37: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.51 ew Mexico Figure 38: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.52 evada Figure 39: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.53 ew York Figure 40: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.54 hio Figure 41: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.55 klahoma Figure 42: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.56 regon Figure 43: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.57 ennsylvania Figure 44: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt
Figure 6: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.20 labama Figure 7: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.21 rkansas Figure 8: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.22 rizona Figure 9: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.23 alifornia Figure 10: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.24 olorado Figure 11: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.25 onnecticut Figure 12: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.26 istrict of Columbia Figure 13: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.27 elaware Figure 14: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.28 lorida Figure 15: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.29 eorgia Figure 16: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.30 awaii Figure 17: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.31 owa Figure 18: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.32 daho Figure 19: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.33 llinois Figure 20: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.34 ndiana Figure 21: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.35 ansas Figure 22: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.36 entucky Figure 23: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.37 ouisiana Figure 24: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.38 assachusetts Figure 25: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.39 aryland Figure 26: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.40 aine Figure 27: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.41 ichigan Figure 28: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.42 innesota Figure 29: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.43 issouri Figure 30: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.44 ississippi Figure 31: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.45 ontana Figure 32: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.46 orth Carolina Figure 33: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.47 orth Dakota Figure 34: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.48 ebraska Figure 35: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.49 ew Hampshire Figure 36: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.50 ew Jersey Figure 37: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.51 ew Mexico Figure 38: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.52 evada Figure 39: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.53 ew York Figure 40: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.54 hio Figure 41: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.55 klahoma Figure 42: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.56 regon Figure 43: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.57 ennsylvania Figure 44: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.58 hode Island Figure 45: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.59 outh Carolina Figure 46: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.60 outh Dakota Figure 47: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.61 ennessee Figure 48: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.62 exas Figure 49: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.63 tah Figure 50: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.64 irginia Figure 51: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt
Figure 6: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.20 labama Figure 7: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.21 rkansas Figure 8: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.22 rizona Figure 9: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.23 alifornia Figure 10: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.24 olorado Figure 11: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.25 onnecticut Figure 12: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.26 istrict of Columbia Figure 13: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.27 elaware Figure 14: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.28 lorida Figure 15: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.29 eorgia Figure 16: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.30 awaii Figure 17: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.31 owa Figure 18: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.32 daho Figure 19: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.33 llinois Figure 20: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.34 ndiana Figure 21: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.35 ansas Figure 22: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.36 entucky Figure 23: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.37 ouisiana Figure 24: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.38 assachusetts Figure 25: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.39 aryland Figure 26: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.40 aine Figure 27: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.41 ichigan Figure 28: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.42 innesota Figure 29: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.43 issouri Figure 30: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.44 ississippi Figure 31: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.45 ontana Figure 32: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.46 orth Carolina Figure 33: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.47 orth Dakota Figure 34: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.48 ebraska Figure 35: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.49 ew Hampshire Figure 36: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.50 ew Jersey Figure 37: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.51 ew Mexico Figure 38: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.52 evada Figure 39: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.53 ew York Figure 40: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.54 hio Figure 41: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.55 klahoma Figure 42: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.56 regon Figure 43: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.57 ennsylvania Figure 44: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.58 hode Island Figure 45: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.59 outh Carolina Figure 46: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.60 outh Dakota Figure 47: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.61 ennessee Figure 48: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.62 exas Figure 49: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.63 tah Figure 50: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.64 irginia Figure 51: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.65 ermont Figure 52: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.66 ashington Figure 53: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt
Figure 6: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.20 labama Figure 7: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.21 rkansas Figure 8: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.22 rizona Figure 9: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.23 alifornia Figure 10: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.24 olorado Figure 11: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.25 onnecticut Figure 12: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.26 istrict of Columbia Figure 13: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.27 elaware Figure 14: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.28 lorida Figure 15: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.29 eorgia Figure 16: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.30 awaii Figure 17: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.31 owa Figure 18: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.32 daho Figure 19: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.33 llinois Figure 20: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.34 ndiana Figure 21: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.35 ansas Figure 22: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.36 entucky Figure 23: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.37 ouisiana Figure 24: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.38 assachusetts Figure 25: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.39 aryland Figure 26: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.40 aine Figure 27: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.41 ichigan Figure 28: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.42 innesota Figure 29: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.43 issouri Figure 30: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.44 ississippi Figure 31: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.45 ontana Figure 32: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.46 orth Carolina Figure 33: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.47 orth Dakota Figure 34: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.48 ebraska Figure 35: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.49 ew Hampshire Figure 36: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.50 ew Jersey Figure 37: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.51 ew Mexico Figure 38: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.52 evada Figure 39: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.53 ew York Figure 40: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.54 hio Figure 41: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.55 klahoma Figure 42: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.56 regon Figure 43: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.57 ennsylvania Figure 44: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.58 hode Island Figure 45: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.59 outh Carolina Figure 46: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.60 outh Dakota Figure 47: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.61 ennessee Figure 48: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.62 exas Figure 49: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.63 tah Figure 50: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.64 irginia Figure 51: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.65 ermont Figure 52: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.66 ashington Figure 53: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.67 isconsin Figure 54: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.68 est Virginia Figure 55: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt
Figure 6: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.20 labama Figure 7: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.21 rkansas Figure 8: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.22 rizona Figure 9: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.23 alifornia Figure 10: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.24 olorado Figure 11: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.25 onnecticut Figure 12: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.26 istrict of Columbia Figure 13: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.27 elaware Figure 14: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.28 lorida Figure 15: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.29 eorgia Figure 16: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.30 awaii Figure 17: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.31 owa Figure 18: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.32 daho Figure 19: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.33 llinois Figure 20: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.34 ndiana Figure 21: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.35 ansas Figure 22: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.36 entucky Figure 23: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.37 ouisiana Figure 24: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.38 assachusetts Figure 25: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.39 aryland Figure 26: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.40 aine Figure 27: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.41 ichigan Figure 28: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.42 innesota Figure 29: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.43 issouri Figure 30: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.44 ississippi Figure 31: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.45 ontana Figure 32: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.46 orth Carolina Figure 33: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.47 orth Dakota Figure 34: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.48 ebraska Figure 35: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.49 ew Hampshire Figure 36: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.50 ew Jersey Figure 37: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.51 ew Mexico Figure 38: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.52 evada Figure 39: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.53 ew York Figure 40: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.54 hio Figure 41: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.55 klahoma Figure 42: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.56 regon Figure 43: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.57 ennsylvania Figure 44: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.58 hode Island Figure 45: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.59 outh Carolina Figure 46: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.60 outh Dakota Figure 47: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.61 ennessee Figure 48: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.62 exas Figure 49: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.63 tah Figure 50: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.64 irginia Figure 51: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.65 ermont Figure 52: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.66 ashington Figure 53: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.67 isconsin Figure 54: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( t ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.68 est Virginia Figure 55: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt ), and cumulative undercount from March 2020 to January 2021. In the topleft panel, deaths divided by the posterior median IFR are plotted in grey for comparison.69 yoming Figure 56: Posterior median and middle 95% intervals for daily new infections, cumulativeincidence, r ( tt