Inference of Japanese encephalitis virus ecological and evolutionary dynamics from passive and active virus surveillance.

A comprehensive monitoring strategy is vital for tracking the spread of mosquito-borne Japanese encephalitis virus (JEV), the leading cause of viral encephalitis in Asia. Virus detection consists of passive surveillance of primarily humans and swine, and/or active surveillance in mosquitoes, which may be a valuable proxy in providing insights into ecological processes underlying the spread and persistence of JEV. However, it has not been well characterized whether passive surveillance alone can capture the circulating genetic diversity to make reasonable inferences. Here, we develop phylogenetic models to infer JEV host changes, spatial diffusion patterns, and evolutionary dynamics from data collected through active and passive surveillance. We evaluate the feasibility of using JEV sequence data collected from mosquitoes to estimate the migration histories of genotypes GI and GIII. We show that divergence times estimated from this dataset were comparable to estimates from all available data. Increasing the amount of data collected from active surveillance improved time of most recent common ancestor estimates and reduced uncertainty. Phylogenetic estimates using all available data and only mosquito data from active surveillance produced similar results, showing that GI epidemics were widespread and diffused significantly faster between regions than GIII. In contrast, GIII outbreaks were highly structured and unlinked suggesting localized, unsampled infectious sources. Our results show that active surveillance of mosquitoes can sufficiently capture circulating genetic diversity of JEV to confidently estimate spatial and evolutionary patterns. While surveillance of other hosts could contribute to more detailed disease tracking and evaluation, comprehensive JEV surveillance programs should include systematic surveillance in mosquitoes to infer the most complete patterns for epidemiology, and risk assessment.