Spatiotemporal Modeling of Zoonotic Arbovirus Transmission in Northeastern Florida Using Sentinel Chicken Surveillance and Earth Observation Data

Abstract

The irregular timing and spatial variation in the zoonotic arbovirus spillover from vertebrate hosts to humans and livestock present challenges to predicting spillover occurrence over time and across broader geographic areas, compromising effective prevention and control strategies. The objective of this study was to quantify the effects of the landscape composition and configuration and dynamic weather events on the 2018 spatiotemporal distribution of eastern equine encephalitis virus (EEEV) (Togaviridae, Alphavirus) and West Nile virus (WNV) (Flaviviridae, Flavivirus) sentinel chicken seroconversion in northeastern Florida. We used a modeling framework that explicitly accounts for joint spatial and temporal effects and incorporates key EO (Earth Observation) information on the climate and landscape in order to more accurately quantify the environmental effects on the transmission to sentinel chickens. We investigated the environmental effects using Bernoulli generalized linear mixed effects models (GLMMs), including a site-level random effect, and then added spatial random effects and spatiotemporal random effects in subsequent runs. The models were executed using an integrated nested Laplace approximation (INLA) and a stochastic partial differential equation (SPDE) approach in R-INLA. The GLMMs that included a spatiotemporal random effect performed better relative to models that included only spatial random effects and also performed better than non-spatial models. The results indicated a strong spatiotemporal structure in the seroconversion for both viruses, but EEEV exhibited a more punctuated and compact structure at the beginning of the sampling season, while WNV exhibited a more gradual and diffuse structure across the study area toward the end of the sampling season. The percentage of cypress–tupelo wetland land cover within 3500 m of coop sites and the edge density of the forest land cover within 500 m had a strong positive effect on the EEEV seroconversion, while the best fitting model for WNV was the intercept-only model with spatiotemporal random effects. The lagged climatic variables included in our study did not have a strong effect on the seroconversion for either virus when accounting for temporal autocorrelation, demonstrating the utility of capturing this structure to avoid type I errors. The predictive accuracy for out-of-sample data for the EEEV seroconversion demonstrates the potential to develop a framework that incorporates temporal dynamics in order to better predict arbovirus transmission.