Harriet L. Mills

The role of rapid diagnostics in managing Ebola epidemics

Ebola emerged in West Africa around December 2013 and swept through Guinea, Sierra Leone and Liberia, giving rise to 27,748 confirmed, probable and suspected cases reported by 29 July 2015. Case diagnoses during the epidemic have relied on polymerase chain reaction-based tests. Owing to limited laboratory capacity and local transport infrastructure, the delays from sample collection to test results being available have often been 2 days or more. Point-of-care rapid diagnostic tests offer the potential to substantially reduce these delays. We review Ebola rapid diagnostic tests approved by the World Health Organization and those currently in development. Such rapid diagnostic tests could allow early triaging of patients, thereby reducing the potential for nosocomial transmission. In addition, despite the lower test accuracy, rapid diagnostic test-based diagnosis may be beneficial in some contexts because of the reduced time spent by uninfected individuals in health-care settings where they may be at increased risk of infection; this also frees up hospital beds. We use mathematical modelling to explore the potential benefits of diagnostic testing strategies involving rapid diagnostic tests alone and in combination with polymerase chain reaction testing. Our analysis indicates that the use of rapid diagnostic tests with sensitivity and specificity comparable with those currently under development always enhances control, whether evaluated at a health-care-unit or population level. If such tests had been available throughout the recent epidemic, we estimate, for Sierra Leone, that their use in combination with confirmatory polymerase chain-reaction testing might have reduced the scale of the epidemic by over a third.This article has not been written or reviewed by Nature editors. Nature accepts no responsibility for the accuracy of the information provided.

A review of epidemiological parameters from Ebola outbreaks to inform early public health decision-making

The unprecedented scale of the Ebola outbreak in West Africa has, as of 29 April 2015, resulted in more than 10,884 deaths among 26,277 cases. Prior to the ongoing outbreak, Ebola virus disease (EVD) caused relatively small outbreaks (maximum outbreak size 425 in Gulu, Uganda) in isolated populations in central Africa. Here, we have compiled a comprehensive database of estimates of epidemiological parameters based on data from past outbreaks, including the incubation period distribution, case fatality rate, basic reproduction number (R0), effective reproduction number (Rt) and delay distributions. We have compared these to parameter estimates from the ongoing outbreak in West Africa. The ongoing outbreak, because of its size, provides a unique opportunity to better understand transmission patterns of EVD. We have not performed a meta-analysis of the data, but rather summarize the estimates by virus from comprehensive investigations of EVD and Marburg outbreaks over the past 40 years. These estimates can be used to parameterize transmission models to improve understanding of initial spread of EVD outbreaks and to inform surveillance and control guidelines.

Community-Wide Isoniazid Preventive Therapy Drives Drug-Resistant Tuberculosis: A Model-Based Analysis

Community-wide isoniazid preventive therapy may lead to long-term rises in resistance at the population level. A Deeper Look at Drug Resistance Although some things may seem obvious at first glance, looking in more depth may paint a different picture. In some complex situations, asking questions in different ways may lead to very different answers. One example is the use of isoniazid preventive therapy (IPT) for tuberculosis (TB) in HIV-prevalent communities. Because HIV-infected individuals are much more likely to develop TB than immunocompetent people, the World Health Organization has recommended the use of IPT in HIV-infected individuals that are symptom-free for TB co-infection. The use of IPT has raised the specter of drug resistance; however, to date, studies have not observed an increase in drug-resistant TB in individuals on IPT. Now, Mills et al. use mathematical modeling to show that even if IPT does not increase drug resistance in infected individuals, community-wide IPT can drive increases in drug resistance at the population level. The authors developed mathematical models to identify the conditions under which community-wide IPT could increase the burden of drug-resistant TB. They found that community-wide IPT increases selective suppression of drug-sensitive infection, thus indirectly conferring an advantage to drug-resistant strains. These data should be considered when determining policy for preventive therapy. Tuberculosis (TB) control is especially difficult in settings of high HIV prevalence; HIV co-infection erodes host immunity and increases risk of progression to active TB. Studies have demonstrated that a 6-month (or longer) course of monotherapy with isoniazid [isoniazid preventive therapy (IPT)] can reduce this risk. The World Health Organization endorses IPT for symptom-free individuals with HIV/TB co-infection and has recommended expanding IPT to entire communities (community-wide IPT). Although previous reviews have not found a statistically significant elevated risk of isoniazid-resistant TB among individuals previously treated with IPT, community-wide IPT programs may nonetheless generate substantial selective pressure and increase the burden of drug-resistant TB (DRTB). We developed mathematical models to identify the conditions under which community-wide IPT interventions could increase the burden of isoniazid-resistant Mycobacterium tuberculosis, even when we assumed that IPT does not select for resistance among those treated with IPT. We found that in models that included any mechanism of interstrain competition (such as partial immunity conferred by a previous M. tuberculosis infection), community-wide IPT interventions conferred an indirect benefit to drug-resistant strains through selective suppression of drug-sensitive infections. This result suggests that the absence of an observed elevation in the risk of DRTB among those receiving IPT in small-scale studies of limited duration does not imply that the selective pressure imposed by community-wide IPT will not be substantial. Community-wide IPT may play an important role in TB control in these settings, and its rollout should be accompanied by interventions to detect and treat drug-resistant disease.

Exposure Patterns Driving Ebola Transmission in West Africa: A Retrospective Observational Study.

Background The ongoing West African Ebola epidemic began in December 2013 in Guinea, probably from a single zoonotic introduction. As a result of ineffective initial control efforts, an Ebola outbreak of unprecedented scale emerged. As of 4 May 2015, it had resulted in more than 19,000 probable and confirmed Ebola cases, mainly in Guinea (3,529), Liberia (5,343), and Sierra Leone (10,746). Here, we present analyses of data collected during the outbreak identifying drivers of transmission and highlighting areas where control could be improved. Methods and Findings Over 19,000 confirmed and probable Ebola cases were reported in West Africa by 4 May 2015. Individuals with confirmed or probable Ebola (“cases”) were asked if they had exposure to other potential Ebola cases (“potential source contacts”) in a funeral or non-funeral context prior to becoming ill. We performed retrospective analyses of a case line-list, collated from national databases of case investigation forms that have been reported to WHO. These analyses were initially performed to assist WHO’s response during the epidemic, and have been updated for publication. We analysed data from 3,529 cases in Guinea, 5,343 in Liberia, and 10,746 in Sierra Leone; exposures were reported by 33% of cases. The proportion of cases reporting a funeral exposure decreased over time. We found a positive correlation (r = 0.35, p < 0.001) between this proportion in a given district for a given month and the within-district transmission intensity, quantified by the estimated reproduction number (R). We also found a negative correlation (r = −0.37, p < 0.001) between R and the district proportion of hospitalised cases admitted within ≤4 days of symptom onset. These two proportions were not correlated, suggesting that reduced funeral attendance and faster hospitalisation independently influenced local transmission intensity. We were able to identify 14% of potential source contacts as cases in the case line-list. Linking cases to the contacts who potentially infected them provided information on the transmission network. This revealed a high degree of heterogeneity in inferred transmissions, with only 20% of cases accounting for at least 73% of new infections, a phenomenon often called super-spreading. Multivariable regression models allowed us to identify predictors of being named as a potential source contact. These were similar for funeral and non-funeral contacts: severe symptoms, death, non-hospitalisation, older age, and travelling prior to symptom onset. Non-funeral exposures were strongly peaked around the death of the contact. There was evidence that hospitalisation reduced but did not eliminate onward exposures. We found that Ebola treatment units were better than other health care facilities at preventing exposure from hospitalised and deceased individuals. The principal limitation of our analysis is limited data quality, with cases not being entered into the database, cases not reporting exposures, or data being entered incorrectly (especially dates, and possible misclassifications). Conclusions Achieving elimination of Ebola is challenging, partly because of super-spreading. Safe funeral practices and fast hospitalisation contributed to the containment of this Ebola epidemic. Continued real-time data capture, reporting, and analysis are vital to track transmission patterns, inform resource deployment, and thus hasten and maintain elimination of the virus from the human population.

Unraveling the drivers of MERS-CoV transmission

Significance Since it was discovered in 2012, Middle East respiratory syndrome coronavirus (MERS-CoV) has infected more than 1,700 persons, one-third of whom died, essentially in the Middle East. Persons can get infected by direct or indirect contact with dromedary camels, and although human-to-human transmission is not self-sustaining in the Middle East, it can nonetheless generate large outbreaks, particular in hospital settings. Overall, we still poorly understand how infections from the animal reservoir, the different levels of mixing, and heterogeneities in transmission have contributed to the buildup of MERS-CoV epidemics. Here, we quantify the contribution of each of these factors from detailed records of MERS-CoV cases from the Kingdom of Saudi Arabia, which has been the most affected country. With more than 1,700 laboratory-confirmed infections, Middle East respiratory syndrome coronavirus (MERS-CoV) remains a significant threat for public health. However, the lack of detailed data on modes of transmission from the animal reservoir and between humans means that the drivers of MERS-CoV epidemics remain poorly characterized. Here, we develop a statistical framework to provide a comprehensive analysis of the transmission patterns underlying the 681 MERS-CoV cases detected in the Kingdom of Saudi Arabia (KSA) between January 2013 and July 2014. We assess how infections from the animal reservoir, the different levels of mixing, and heterogeneities in transmission have contributed to the buildup of MERS-CoV epidemics in KSA. We estimate that 12% [95% credible interval (CI): 9%, 15%] of cases were infected from the reservoir, the rest via human-to-human transmission in clusters (60%; CI: 57%, 63%), within (23%; CI: 20%, 27%), or between (5%; CI: 2%, 8%) regions. The reproduction number at the start of a cluster was 0.45 (CI: 0.33, 0.58) on average, but with large SD (0.53; CI: 0.35, 0.78). It was >1 in 12% (CI: 6%, 18%) of clusters but fell by approximately one-half (47% CI: 34%, 63%) its original value after 10 cases on average. The ongoing exposure of humans to MERS-CoV from the reservoir is of major concern, given the continued risk of substantial outbreaks in health care systems. The approach we present allows the study of infectious disease transmission when data linking cases to each other remain limited and uncertain.

HIV transmission from drug injectors to partners who do not inject, and beyond: Modelling the potential for a generalized heterosexual epidemic in St. Petersburg, Russia

BACKGROUND HIV infection is prevalent among drug injectors in St. Petersburg and their non-injecting heterosexual partners (PIDUs). There are fears that sexual transmission of HIV from IDUs to PIDUs may portend a self-sustaining, heterosexual epidemic in Russia. METHODS Our model combines a network model of sexual partnerships of IDUs and non-IDUs to represent sexual transmission of HIV and a deterministic model for parenteral transmission among IDUs. Behavioural parameters were obtained from a survey of St. Petersburg IDUs and their sexual partners. We based our model fits on two scenarios for PIDU prevalence in 2006 (5.6% and 15.1%, calculated excluding and including HCV co-infected PIDUs respectively) and compared predictions for the general population HIV prevalence. RESULTS Results indicate that sexual transmission could sustain a non-IDU HIV epidemic. The model indicates that general population prevalence may be greater than current estimates imply. Parenteral transmission drives the epidemic and the PIDU bridge population plays a crucial role transferring infection to non-IDUs. The model indicates that the high PIDU prevalence is improbable because of the high risk behaviour this implies; the lower prevalence is possible. CONCLUSION The model implies that transmission through PIDUs will sustain a heterosexual epidemic, if prevalence among IDUs and PIDUs is as high as survey data suggest. We postulate that current estimates of population prevalence underestimate the extent of the HIV epidemic because they are based on the number of registered cases only. Curtailing transmission among injectors and PIDUs will be vital in controlling heterosexual transmission.

Respondent driven sampling and community structure in a population of injecting drug users, Bristol, UK.

BACKGROUND A 2006 respondent driven sampling (RDS) survey of injecting drug users (IDUs) in Bristol, UK, estimated 40 per 100 person years HCV incidence but in 2009 another RDS survey estimated only 10 per 100 person years incidence amongst the same population. Estimated increases in intervention exposure do not fully explain the decrease in risk. We investigate whether the underlying contact network structure and differences in the structure of the RDS trees could have contributed to the apparent change in incidence. METHOD We analyse the samples for evidence that individuals recruit participants who are like themselves (assortative recruiting). Using an assortativity measure, we develop a Monte Carlo approach to determine whether the RDS data exhibit significantly more assortativity than is expected for that sample. Motivated by these findings, a network model is used to investigate how much assortativity and the structure of the RDS tree impacts sample estimates of prevalence and incidence. RESULTS The samples suggest there is some assortativity on injecting habits or markers of injecting risk. The 2009 sample has lower assortativity than 2006. Simulations of RDS confirm that assortativity influences the estimated incidence in a population and the structure of RDS samples can result in bias. Our simulations suggest that RDS incidence estimates have considerable variance, making them difficult to use for monitoring trends. CONCLUSIONS We suggest there was likely to have been a decline in risk between 2006 and 2009 due to increased intervention coverage, but the bias and variance in the estimates prevents accurate estimation of the incidence.

Modelling the performance of isoniazid preventive therapy for reducing tuberculosis in HIV endemic settings: the effects of network structure

Individuals living with HIV experience a much higher risk of progression from latent M. tuberculosis infection to active tuberculosis (TB) disease relative to individuals with intact immune systems. A several-month daily course of a single drug during latent infection (i.e. isoniazid preventive therapy (IPT)) has proved in clinical trials to substantially reduce an HIV-infected individuals risk of TB disease. As a result of these findings and ongoing studies, the World Health Organization has produced strong guidelines for implementing IPT on a community-wide scale for individuals with HIV at risk of TB disease. To date, there has been limited use of IPT at a community-wide level. In this paper, we present a new co-network model for HIV and TB co-epidemics to address questions about how the population-level impact of community-wide IPT may differ from the individual-level impact of IPT offered to selected individuals. In particular, we examine how the effect of clustering of contacts within high-TB incidence communities may affect the rates of re-infection with TB and how this clustering modifies the expected population-level effects of IPT. We find that populations with clustering of respiratory contacts experience aggregation of TB cases and high numbers of re-infection events. While, encouragingly, the overall population-level effects of community-wide IPT appear to be sustained regardless of network structure, we find that in populations where these contacts are highly clustered, there is dramatic heterogeneity in the impact of IPT: in some sub-regions of these populations, TB is nearly eliminated, while in others, repeated re-infection almost completely undermines the effect of IPT. Our findings imply that as IPT programmes are brought to scale, we should expect local heterogeneity of effectiveness as a result of the complex patterns of disease transmission within communities.

Distinguishing Between Reservoir Exposure and Human-to-Human Transmission for Emerging Pathogens Using Case Onset Data

Pathogens such as MERS-CoV, influenza A/H5N1 and influenza A/H7N9 are currently generating sporadic clusters of spillover human cases from animal reservoirs. The lack of a clear human epidemic suggests that the basic reproductive number R0 is below or very close to one for all three infections. However, robust cluster-based estimates for low R0 values are still desirable so as to help prioritise scarce resources between different emerging infections and to detect significant changes between clusters and over time. We developed an inferential transmission model capable of distinguishing the signal of human-to-human transmission from the background noise of direct spillover transmission (e.g. from markets or farms). By simulation, we showed that our approach could obtain unbiased estimates of R0, even when the temporal trend in spillover exposure was not fully known, so long as the serial interval of the infection and the timing of a sudden drop in spillover exposure were known (e.g. day of market closure). Applying our method to data from the three largest outbreaks of influenza A/H7N9 outbreak in China in 2013, we found evidence that human-to-human transmission accounted for 13% (95% credible interval 1%–32%) of cases overall. We estimated R0 for the three clusters to be: 0.19 in Shanghai (0.01-0.49), 0.29 in Jiangsu (0.03-0.73); and 0.03 in Zhejiang (0.00-0.22). If a reliable temporal trend for the spillover hazard could be estimated, for example by implementing widespread routine sampling in sentinel markets, it should be possible to estimate sub-critical values of R0 even more accurately. Should a similar strain emerge with R0>1, these methods could give a real-time indication that sustained transmission is occurring with well-characterised uncertainty.

Estimating Potential Incidence of MERS-CoV Associated with Hajj Pilgrims to Saudi Arabia, 2014

Between March and June 2014 the Kingdom of Saudi Arabia (KSA) had a large outbreak of MERS-CoV, renewing fears of a major outbreak during the Hajj this October. Using KSA Ministry of Health data, the MERS-CoV Scenario and Modeling Working Group forecast incidence under three scenarios. In the expected incidence scenario, we estimate 6.2 (95% Prediction Interval [PI]: 1–17) pilgrims will develop MERS-CoV symptoms during the Hajj, and 4.0 (95% PI: 0–12) foreign pilgrims will be infected but return home before developing symptoms. In the most pessimistic scenario, 47.6 (95% PI: 32–66) cases will develop symptoms during the Hajj, and 29.0 (95% PI: 17–43) will be infected but return home asymptomatic. Large numbers of MERS-CoV cases are unlikely to occur during the 2014 Hajj even under pessimistic assumptions, but careful monitoring is still needed to detect possible mass infection events and minimize introductions into other countries.