Environmental-mechanistic modelling of the impact of global change on human zoonotic disease emergence: a case study of Lassa fever

Abstract

Human infectious diseases are a significant threat to global human health and economies (e.g. Ebola, SARs), with the majority of infectious diseases having an animal source (zoonotic). Despite their importance, the lack of a quantitative predictive framework hampers our understanding of how spillovers of zoonotic infectious diseases into the human population will be impacted by global environmental stressors. Here, we create an environmental-mechanistic model for understanding the impact of global change on the probability of zoonotic disease reservoir host–human spillover events. As a case study, we focus on Lassa fever virus (LAS). We first quantify the spatial determinants of LAS outbreaks, including the phylogeographic distribution of its reservoir host Natal multimammate rat (Mastomys natalensis) (LAS host). Secondly, we use these determinants to inform our environmental-mechanistic model to estimate present-day LAS spillover events and the predicted impact of climate change, human population growth and land use by 2070. We find phylogeographic evidence to suggest that LAS is confined to only one clade of LAS host (Western clade Mastomys natalensis) and that the probability of its occurrence was a major determinant of the spatial variation in LAS historical outbreaks (69·8%), along with human population density (20·4%). Our estimates for present-day LAS spillover events from our environmental-mechanistic model were consistent with observed patterns, and we predict an increase in events per year by 2070 from 195 125 to 406 725 within the LAS endemic western African region. Of the component drivers, climate change and human population growth are predicted to have the largest effects by increasing landscape suitability for the host and human–host contact rates, while land-use change has only a weak impact on the number of future events. LAS spillover events did not respond uniformly to global environmental stressors, and we suggest that understanding the impact of global change on zoonotic infectious disease emergence requires an understanding of how reservoir host species respond to environmental change. Our environmental-mechanistic modelling methodology provides a novel generalizable framework to understand the impact of global change on the spillover of zoonotic diseases.