1. Spatiotemporal dynamics of pathogen transmission and infectious disease incidence
Mosquito-borne pathogens are notorious for the extent to which their transmission is heterogeneous across space, over time, and among individuals. Because of these and other challenges associated with interpreting available data, developing and applying modeling approaches is essential for advancing capabilities to address the public health challenges posed by mosquito-borne diseases.
Mosquito-borne pathogens are notorious for the extent to which their transmission is heterogeneous across space, over time, and among individuals. Because of these and other challenges associated with interpreting available data, developing and applying modeling approaches is essential for advancing capabilities to address the public health challenges posed by mosquito-borne diseases.
Predicting spatial risk at geographic scales: We have published papers that relate spatial and temporal heterogeneity in temperature, economic prosperity, and other factors to the transmission of Zika, chikungunya, and other mosquito-borne diseases. Most notably, we recently published a paper in Nature Microbiology that provides one of very few projections to date of how many people – and in particular women of childbearing age who are likely to give birth – were at risk of becoming infected during the first wave of the Zika epidemic. Ongoing work in the lab is building on these more basic results to advance more applied interests, such as infectious disease forecasting and vaccine trial site selection.
Better understanding heterogeneous transmission at fine scales: We have developed two distinct platforms for individual-based modeling of dengue transmission. The first of these efforts has been conducted in conjunction with an NIH-funded P01 project in Iquitos, Peru. This work addresses questions about multiple forms of heterogeneity in the transmission of viruses vectored by Aedes aegypti mosquitoes, including human mobility, infectiousness, and disease severity. The second of these efforts has involved collaboration between us and the Institute for Disease Modeling to adapt their modeling framework for malaria to dengue and other arboviruses.
Developing tools to inform malaria elimination efforts: We are involved in two Gates Foundation projects on malaria. The first involves the development of new modeling tools that are enabling more detailed strategic assessments for malaria elimination in settings where that goal is feasible. The second involves the development of a novel Bayesian statistical framework for inferring transmission linkages between individual malaria cases based on a combination of epidemiological and parasite genetic data.
2. Model-guided assessment of interventions for infectious disease prevention
Due to the inadequacy of many existing interventions and because of the growing number of infectious disease threats, the evaluation of novel interventions is an increasingly important enterprise. Mathematical modeling has a key role to play in designing and interpreting studies to assess intervention efficacy and to make projections of their potential impact when deployed at population scales.
Due to the inadequacy of many existing interventions and because of the growing number of infectious disease threats, the evaluation of novel interventions is an increasingly important enterprise. Mathematical modeling has a key role to play in designing and interpreting studies to assess intervention efficacy and to make projections of their potential impact when deployed at population scales.
Dengue vaccines: We are involved in work on multiple fronts to assess the potential impact of new and forthcoming dengue vaccines. We recently contributed one of eight dengue models to a working group organized by the WHO to project the public health impact of the recently licensed Dengvaxia vaccine. This work directly informed the WHO’s policy position on this vaccine and is the subject of a paper that was recently published at PLOS Medicine. Regarding forthcoming dengue vaccine candidates, we are collaborating with a dengue vaccine developer to incorporate individual-based simulation models into vaccine trial design.
Zika and chikungunya vaccines: To assess the efficacy of any vaccine candidate, it is essential that a trial be conducted in an area with an appreciable level of transmission. Identifying such areas is a challenging task for Zika and chikungunya, because their complex spatial and temporal patterns of transmission are driven by spatiotemporal heterogeneity in weather conditions that affect their mosquito vector and by the rapidly changing landscape of immunity in human populations. We are contributing to ongoing efforts to select vaccine trial sites using a combination of model-based projections of infection attack rates and empirical estimates of infection attack rates to date.
Vector control: Interventions targeting the Aedes aegypti mosquito that vectors Zika, dengue, and other viral pathogens have had very limited success. Moreover, there are inherent challenges to accurately quantifying the impacts of novel interventions, due in part to the fact that many interventions have complex effects on mosquito behavioral and life-history traits. We are developing new statistical approaches for quantifying these nuanced and interrelated effects of novel interventions (particularly spatial repellent products deployed in homes) on multiple mosquito behavioral and life-history traits and furthermore linking those estimates to projections of the epidemiological impact of those interventions should they be deployed at a given coverage level across an area.
3. Infectious disease dynamics in the context of global change
As our research develops, we continually find that the increasingly precise mechanistic understanding of the dynamics of transmission and control that we are cultivating has the potential to provide insights about how infectious diseases will respond in the future to the many drivers of global change that our planet is facing.
As our research develops, we continually find that the increasingly precise mechanistic understanding of the dynamics of transmission and control that we are cultivating has the potential to provide insights about how infectious diseases will respond in the future to the many drivers of global change that our planet is facing.
Climate change and vector-borne disease risk: There has been extensive speculation and debate about the impact of climate change, particularly increased temperatures, on vector-borne diseases. Our research is showing that while many of these effects are inevitable, their real and perceived impact may only be properly understood in the correct theoretical light. For example, in one recent paper, we showed that temperature has the potential to increase not only the magnitude of arbovirus transmission (R0) but also the speed of transmission (generation interval), resulting in potentially more intense epidemics (r) of dengue that may strain the capacity of healthcare systems in many dengue-endemic areas in the future.
Interaction between multiple drivers of global change: We increasingly find that transmission dynamics cannot be understood by considering one factor alone but that interactions between multiple drivers must be accounted for explicitly. In particular, three dominant factors that we often find useful to focus on are pathogen importation (more generally, propagule pressure), weather-mediated transmission potential (more generally, extrinsic forcing), and dynamic population immunity (more generally, intrinsic forcing). We are currently bringing this perspective to bear on settings where there is wide variation in interannual transmission patterns, using models to dissect how these three dominant factors interact to drive the variability we are interested in explaining and ultimately predicting. Our philosophy is that this level of understanding is essential to understand how shifts in any one of those factors may precipitate associated shifts in patterns of pathogen transmission and disease incidence under a variety of future scenarios.
