Alex Perkins

Preliminary results of models to predict areas in the Americas with increased likelihood of Zika virus transmission in 2017.

Numerous Zika virus vaccines are being developed. However, identifying sites to evaluate the efficacy of a Zika virus vaccine is challenging due to the general decrease in Zika virus activity. We compare results from three different modeling approaches to estimate areas that may have increased relative risk of Zika virus transmission during 2017. The analysis focused on eight priority countries (i.e., Brazil, Colombia, Costa Rica, Dominican Republic, Ecuador, Mexico, Panama, and Peru). The models projected low incidence rates during 2017 for all locations in the priority countries but identified several subnational areas that may have increased relative risk of Zika virus transmission in 2017. Given the projected low incidence of disease, the total number of participants, number of study sites, or duration of study follow-up may need to be increased to meet the efficacy study endpoints.

DTK-Dengue: A new agent-based model of dengue virus transmission dynamics

Dengue virus (DENV) is a pathogen spread by Aedes mosquitoes that has a considerable impact on global health. Agent-based models can be used to explicitly represent factors that are difficult to measure empirically, by focusing on specific aspects of DENV transmission dynamics that influence spread in a particular location. We present a new agent-based model for DENV dynamics, DTK-Dengue, that can be readily applied to new locations and to a diverse set of goals. It extends the vector-borne disease module in the Institute for Disease Modelling’s Epidemiological Modeling Disease Transmission Kernel (EMOD-DTK) to model DENV dynamics. There are three key modifications present in DTK-Dengue: 1) modifications to how climatic variables influence vector development for Aedes mosquitoes, 2) updates to adult vector behavior to make them more similar to Aedes, and 3) the inclusion of four DENV serotypes, including their effects on human immunity and symptoms. We demonstrate DTK-Dengue’s capabilities by fitting the model to four interrelated datasets: total and serotype-specific dengue incidences between January 2007 and December 2008 from San Juan, Puerto Rico; the age distribution of reported dengue cases in Puerto Rico during 2007; and the number of adult female Ae. aegypti trapped in two neighborhoods of San Juan between November 2007 and December 2008. The model replicated broad patterns in the reference data, including a correlation between vector population dynamics and rainfall, appropriate seasonality in the reported incidence, greater circulation of DENV-3 than any other serotype, and an inverse relationship between age and the proportion of cases associated with each age group over 20 years old. This exercise demonstrates the potential for DTK-Dengue to assimilate multiple types of epidemiologic data into a realistic portrayal of DENV transmission dynamics. Due to the open availability of the DTK-Dengue software and the availability of numerous other modules for modeling disease transmission and control from EMOD-DTK, this new model has potential for a diverse range of future applications in a wide variety of settings.

Statistical and biological uncertainties associated with vaccine efficacy estimates and their implications for dengue vaccine impact projections

Given the limited effectiveness of strategies based solely on vector control to reduce dengue virus transmission, it is expected that an effective vaccine could play a pivotal role in reducing the global disease burden of dengue. Dengvaxia® from Sanofi Pasteur recently became the first dengue vaccine to become licensed in select countries and to achieve WHO recommendation for use in certain settings, despite the fact that a number of uncertainties about the vaccines efficacy and mode of action complicate projections of its potential impact on public health. We used a new stochastic individual-based model for dengue transmission to perform simulations of the impact of Dengvaxia® in light of two key uncertainties: statistical uncertainty about the numerical value of the vaccines efficacy against disease, and biological uncertainty about the extent to which its efficacy against disease derives from the amelioration of symptoms, blocking of dengue infection, or some combination thereof. Our results suggest that projections of the vaccines public health impact may be far more sensitive to biological details of how the vaccine protects against disease than to statistical details of the extent to which it protects against disease. Under the full range of biological uncertainty that we considered, there was nearly three-fold variation in the population-wide number of disease episodes averted. These differences owe to variation in indirect effects of vaccination arising from uncertainty about the extent of onward transmission of dengue from vaccine recipients. These results demonstrate important limitations associated with the use of symptomatic disease as the primary endpoint of dengue vaccine trials and highlight the importance of considering multiple forms of uncertainty in projections of a vaccines impact on public health.Given the limited effectiveness of strategies based solely on vector control to reduce dengue virus (DENV) transmission, it is expected that an effective vaccine could play a pivotal role in reducing the global disease burden of dengue. Of several dengue vaccines under development, Dengvaxia® from Sanofi Pasteur recently became the first to become licensed in select countries and to achieve WHO recommendation for use in certain settings, despite the fact that a number of uncertainties about its profile complicate projections of its public health impact. We used a stochastic, agent-based model for DENV transmission to perform simulations of the public health impact of dengue vaccines in light of two key uncertainties: (1) “statistical uncertainty” about the numerical value of the vaccine’s efficacy against disease, and (2) “biological uncertainty” about the extent to which its efficacy against disease derives from the amelioration of symptoms, blocking of DENV infection, or some combination thereof. Simulations of a generic dengue vaccine showed that the proportion of disease episodes averted following 20 years of routine vaccination of nine-year olds at 80% coverage was sensitive to both the numerical value of vaccine efficacy and to the extent to which efficacy derives from blocking of DENV infection. Simulations of a vaccine resembling Dengvaxia® took into account that vaccine trial results substantially reduced statistical uncertainty but did not address biological uncertainty, resulting in the proportion of disease episodes averted being more sensitive to biological uncertainty than to statistical uncertainty. Taken together, our results indicate limitations associated with the use of symptomatic disease as the primary endpoint of dengue vaccine trials and highlight the importance of considering multiple forms of uncertainty in projections of a vaccine’s public health impact.

Inter-annual variation in seasonal dengue epidemics driven by multiple interacting factors in Guangzhou, China

Vector-borne diseases display wide inter-annual variation in seasonal epidemic size due to their complex dependence on temporally variable environmental conditions and other factors. In 2014, Guangzhou, China experienced its worst dengue epidemic on record, with incidence exceeding the historical average by two orders of magnitude. To disentangle contributions from multiple factors to inter-annual variation in epidemic size, we fitted a semi-mechanistic model to time series data from 2005-2015 and performed a series of factorial simulation experiments in which seasonal epidemics were simulated under all combinations of year-specific patterns of four time-varying factors: imported cases, mosquito density, temperature, and residual variation in local conditions not explicitly represented in the model. Our results indicate that while epidemics in most years were limited by unfavorable conditions with respect to one or more factors, the epidemic in 2014 was made possible by the combination of favorable conditions for all factors considered in our analysis.

Model-based assessment of public health impact and cost-effectiveness of dengue vaccination following screening for prior exposure

The tetravalent dengue vaccine CYD-TDV (Dengvaxia®) is the first licensed vaccine against dengue, but recent findings indicate an elevated risk of severe disease among vaccinees without prior dengue virus (DENV) exposure. The World Health Organization currently recommends CYD-TDV only for individuals with serological confirmation of past DENV exposure. Our objective was to evaluate the potential health impact and cost-effectiveness of vaccination following serological screening. To do so, we used an agent-based model to simulate DENV transmission with and without vaccination over a 10-year timeframe. Across a range of values for the proportion of vaccinees with prior DENV exposure, we projected the proportion of symptomatic and hospitalized cases averted as a function of the sensitivity and specificity of serological screening. Scenarios about the cost-effectiveness of screening and vaccination were chosen to be representative of Brazil and the Philippines. We found that public health impact depended primarily on sensitivity in high-transmission settings and on specificity in low-transmission settings. Cost-effectiveness could be achievable from the perspective of a public payer provided that sensitivity and the value of a disability-adjusted life-year were both high, but only in high-transmission settings. Requirements for reducing relative risk and achieving cost-effectiveness from an individual perspective were more restricted, due to the fact that those who test negative pay for screening but receive no benefit. Our results predict that cost-effectiveness could be achieved only in high-transmission areas of dengue-endemic countries with a relatively high per capita GDP, such as Panamá (13,680 USD), Brazil (8,649 USD), México (8,201 USD), or Thailand (5,807 USD). In conclusion, vaccination with CYD-TDV following serological screening could have a positive impact in some high-transmission settings, provided that screening is highly specific (to minimize individual harm), at least moderately sensitive (to maximize population benefit), and sufficiently inexpensive (depending on the setting). AUTHOR SUMMARY Among several viral diseases transmitted by Aedes aegypti mosquitoes, dengue imposes the greatest and most persistent burden on global health. Efforts to curb its spread would benefit greatly from the availability of an effective vaccine. Currently, the only licensed dengue vaccine, known as CYD-TDV or by the brand name Dengvaxia®, is only recommended for use in people who are known to have been exposed to dengue virus in the past. Because symptoms of dengue can range from severe to mild to imperceptible, using clinical history alone to assess whether a person was previously exposed is unreliable. Instead, serological assays, which measure a person’s immune response to dengue virus, are necessary to confirm whether a person was previously exposed. Because serological assays can be subject to substantial error, we used a simulation model to assess how impactful CYD-TDV vaccination would be under different scenarios about the accuracy of a serological assay and the intensity of transmission in a given area. We found that the health impact and cost-effectiveness of CYD-TDV vaccination depended on the accuracy of the serological assay, its cost, and the setting in which it is deployed.

Heterogeneous local dynamics revealed by classification analysis of spatially disaggregated time series data

Background: Temporal incidence patterns provide a crucial window into the dynamics of emerging infectious diseases, yet their utility is limited by the spatially aggregated form in which they are often presented. Weekly incidence data from the 2015-2016 Zika epidemic were available only at the national level for most countries in the Americas. One exception was Colombia, where data at departmental and municipal scales were made publicly available in real time, providing an opportunity to assess the degree to which national-level data are reflective of temporal patterns at local levels. Methods: To characterize differences in epidemic trajectories, our analysis centered on classifying proportional cumulative incidence curves according to six features at three levels of spatial aggregation. This analysis used the partitioning around medoids algorithm to assign departments and municipalities to groups based on these six characteristics. Examination of the features that differentiated these groups and exploration of their temporal and spatial patterns were performed. Simulations from a stochastic transmission model provided data that were used to assess the extent to which groups identified by the classification algorithm could be associated with differences in underlying drivers of transmission. Results: The timing of departmental-level epidemic peaks varied by three months, and departmental-level estimates of the time-varying reproduction number, R(t), showed patterns that were distinct from a national-level estimate. The classification algorithm identified moderate support for two to three clusters at the departmental level and somewhat stronger support for this at the municipal level. Variability in epidemic duration, the length of the tail of the epidemic, and the consistency of cumulative incidence data with a cumulative normal distribution function made the greatest contributions to distinctions across these groups. Applying the classification algorithm to simulated data showed that municipalities with basic reproduction number, R0, greater than 1 were consistently associated with a particular group. Municipalities with R0 < 1 displayed more diverse patterns, although in this case that may be due to simplifications of how the model represented spatial interaction among municipalities. Conclusions: The diversity of temporal incidence patterns at local scales uncovered by this analysis underscores the value of spatially disaggregated data and the importance of locally tailored strategies for responding to emerging infectious diseases.Time series data provide a crucial window into ecological dynamics, yet their utility is often limited by the spatially aggregated form in which they are presented. When working with time series data, violating the implicit assumption of homogeneous dynamics below the scale of aggregation could bias inferences about underlying processes. We tested this assumption in the context of the 2015-2016 Zika epidemic in Colombia, where time series of weekly case reports were available at national, departmental, and municipal scales. First, we performed a descriptive analysis, which showed that the timing of departmental-level epidemic peaks varied by three months and that departmental-level estimates of the time-varying reproduction number, R(t), showed patterns that were distinct from a national-level estimate. Second, we applied a classification algorithm to six features of cumulative incidence curves, which showed that variability in epidemic duration, the length of the epidemic tail, and consistency with a normal distribution function made the greatest contributions to distinguishing groups. Third, we applied this classification algorithm to data simulated with a stochastic transmission model, which showed that group assignments were consistent with simulated differences in the basic reproduction number, R0. This result, along with associations between spatial drivers of transmission and group assignments based on observed data, suggests that the classification algorithm is capable of detecting meaningful differences in temporal patterns that are associated with differences in underlying ecological drivers. Overall, this diversity of temporal patterns at local scales underscores the value of spatially disaggregated time series data.

Exploring scenarios of chikungunya mitigation with a data-driven agent-based model of the 2014–2016 outbreak in Colombia

New epidemics of infectious diseases can emerge any time, as illustrated by the emergence of chikungunya virus (CHIKV) and Zika virus (ZIKV) in Latin America. During new epidemics, public health officials face difficult decisions regarding spatial targeting of interventions to optimally allocate limited resources. We used a large-scale, data-driven, agent-based simulation model (ABM) to explore CHIKV mitigation strategies, including strategies based on previous DENV outbreaks. Our model represents CHIKV transmission in a realistic population of Colombia with 45 million individuals in 10.6 million households, schools, and workplaces. Our model uses high-resolution probability maps for the occurrence of the Ae. aegypti mosquito vector to estimate mosquito density in Colombia. We found that vector control in all 521 municipalities with mosquito populations led to 402,940 fewer clinical cases of CHIKV compared to a baseline scenario without intervention. We also explored using data about previous dengue virus (DENV) epidemics to inform CHIKV mitigation strategies. Compared to the baseline scenario, 314,437 fewer cases occurred when we simulated vector control only in 301 municipalities that had previously reported DENV, illustrating the value of available data from previous outbreaks. When varying the implementation parameters for vector control, we found that faster implementation and scale-up of vector control led to the greatest proportionate reduction in cases. Using available data for epidemic simulations can strengthen decision making against new epidemic threats.

The Basic Reproductive Number for Disease Systems with Multiple Coupled Heterogeneities

In mathematical epidemiology, a well-known formula describes the impact of heterogeneity on the basic reproductive number for situations in which transmission is separable and for which there is one source of variation in susceptibility and one source of variation in infectiousness. This formula is written in terms of the magnitudes of the heterogeneities, as quantified by their coefficients of variation, and the correlation between them. A natural question to ask is whether analogous results apply when there are multiple sources of variation in susceptibility and/or infectiousness. In this paper we demonstrate that under three or more coupled heterogeneities, the basic reproductive number depends on details of the distribution of the heterogeneities in a way that is not seen in the well-known simpler situation. We provide explicit results for the cases of multivariate normal and multivariate log-normal distributions, showing that the basic reproductive number can again be expressed in terms of the magnitudes of the heterogeneities and the pairwise correlations between them. The results, however, differ between the two multivariate distributions, demonstrating that no formula of this type applies generally when there are three or more coupled heterogeneities. We see that the results are approximately equal when heterogeneities are relatively small and show that an earlier result in the literature (Koella, 1991) should be viewed in this light. We provide numerical illustrations of our results.

Model-based analysis of experimental hut data elucidates multifaceted effects of a volatile chemical on Aedes aegypti mosquitoes

Background Insecticides used against Aedes aegypti and other disease vectors can elicit a multitude of dose-dependent effects on behavioral and bionomic traits. Estimating the potential epidemiological impact of a product requires thorough understanding of these effects and their interplay at different dosages. Volatile spatial repellent (SR) products come with an additional layer of complexity due to the potential for movement of affected mosquitoes or volatile particles of the product beyond the treated house. Here, we propose a statistical inference framework for estimating these nuanced effects of volatile SRs. Methods We fitted a continuous-time Markov chain model in a Bayesian framework to mark-release-recapture (MRR) data from an experimental hut study conducted in Iquitos, Peru. We estimated the effects of two dosages of transfluthrin on Ae. aegypti behaviors associated with human-vector contact: repellency, exiting, and knockdown in the treated space and in “downstream” adjacent huts. We validated the framework using simulated data. Results The odds of a female Ae. aegypti being repelled from a treated hut (HT) increased at both dosages (low dosage: odds = 1.64, 95% highest density interval (HDI) = 1.30-2.09; high dosage: odds = 1.35, HDI = 1.04-1.67). The relative risk of exiting from the treated hut was reduced (low: RR = 0.70, HDI = 0.62-1.09; high: RR = 0.70, HDI = 0.40-1.06), with this effect carrying over to untreated spaces as far as two huts away from the treated hut (H2) (low: RR = 0.79, HDI = 0.59-1.01; high: RR = 0.66, HDI = 0.50-0.87). Knockdown rates were increased in both treated and downstream huts, particularly under high dosage (HT: RR = 8.37, HDI = 2.11-17.35; H1: RR = 1.39, HDI = 0.52-2.69; H2: RR = 2.22, HDI = 0.96-3.86). Conclusions Our statistical inference framework is effective at elucidating multiple effects of volatile chemicals used in SR products, as well as their downstream effects. This framework provides a powerful tool for early selection of candidate SR product formulations worth advancing to costlier epidemiological trials, which are ultimately necessary for proof of concept of public health value and subsequent formal endorsement by health authorities.