Coupling dynamic population immunity profiles and host behaviours to arboviral spread

ended

Arboviruses infect millions of people each year, however, mechanisms that drive viral emergence and maintenance remain largely unknown. A combination of host factors (e.g., human mobility), mosquito factors (e.g., abundance) and viral factors (e.g., transmissibility) interconnect to drive spread. Further, for endemic arboviruses, complex patterns of population immunity, built up over many years, appear key to the emergence of particular lineages. To disentangle the contribution of these different drivers, we need detailed data from the same pathogen system over a long time period from the same location. In addition, we need new methods, which can integrate these different data sources and allow appropriate mechanistic inferences. In this project, I will use the most globally prevalent arbovirus, dengue virus, as a case study. I will focus on Thailand where all four dengue serotypes have circulated endemically for decades and excellent long-term data and isolates exist, to address two fundamental questions: i) How do population-level patterns of immunity evolve over time and what is their impact on strain dynamics? I will use mechanistic models applied to historic serotype-specific case data to reconstruct the evolving immune profile of the population and explore the impact of immunity on viral diversity using sequences from archived isolates from each year over a 50-year period. ii) How do human behaviors, vector densities interact with immunity to dictate spread? I will work with geolocated full genome sequences from across Thailand and use detailed data on how people move, their contact patterns, their immunity profiles and mosquito distributions to study competing hypotheses of how arboviruses spread. I will compare the key drivers of dengue spread with that found for outbreaks of Zika and chikungunya. This proposal addresses fundamental questions about the mechanisms that drive arboviral emergence and spread that will be relevant across disease systems.