Identifying Hotspots in the Distribution of Human Infectious Diseases Using a Bayesian Framework: A Lead to Drivers, Prevention, and Surveillance of Disease Emergence

Abstract Background The ongoing COVID-19 pandemic underscores the need of surveillance system to detect threats and regions at high risk from emerging infectious diseases (EIDs). With the human-driven perturbations to the human-animal-pathogen interface at an ecological scale, the integration of these environmental drivers is essential. We propose robust mathematical models to map, detect, and identify significant drivers of EID outbreaks for three viral EID groups: Filoviridae, Coronaviridae, and Henipaviruses.MethodsWe modeled the presence-absence data in a spatially explicit, binomial and zero-inflation binomial (ZIB) logistic regression with and without autoregression (iCAR). The presence data were extracted from WHO and Promed archives for the three EID groups and we generated pseudoabsence points within the spatial distribution of the mammalian reservoirs. Various environmental and demographical raster were used to explain the distribution of EIDs. True Skill Statistic and deviance parameters were used to compare the accuracy of the different models.ResultsWe used hierarchical SDM binomial, ZIB with and without iCAR models to map the predictive risk of viral EIDs. ZIB models with autoregression were found to be near perfect in detecting the distribution of EID outbreaks with 70% of the models explained by environmental and demographic drivers. The common influencing drivers amongst the three groups of EIDs analyzed were climatic covariates minimum temperature and rainfall, and human-driven land modifications.ConclusionsOur study results conclude that using SDMs in a Bayesian structure is near perfect detecting hotspots and significant drivers of EID outbreaks. It also maps the sites needing active surveillance, which essential in epidemic prevention, and highlights the influence of human-driven modifications to environment on disease emergence.