Mosquito and primate ecology predict human risk of yellow fever virus spillover in Brazil

Abstract

Many (re)emerging infectious diseases in humans arise from pathogen spillover from wildlife or livestock, and accurately predicting pathogen spillover is an important public health goal. In the Americas, yellow fever in humans primarily occurs following spillover from non-human primates via mosquitoes. Predicting yellow fever spillover can improve public health responses through vector control and mass vaccination. Here, we develop and test a mechanistic model of pathogen spillover to predict human risk for yellow fever in Brazil. Our environmental risk model, based on the ecology of mosquito vectors and non-human primate hosts, distinguished municipality-months with yellow fever spillover from 2001 to 2016 with high accuracy (AUC = 0.71). Incorporating hypothesized cyclical dynamics of infected primates improved accuracy (AUC = 0.79). Using boosted regression trees to identify gaps in the mechanistic model, we found that important predictors include current and lagged (mechanistic) environmental risk, vaccine coverage, population density, temperature, and precipitation. More broadly, we show that for a widespread human viral pathogen, the ecological interactions between environment, vectors, reservoir hosts, and humans can predict spillover with surprising accuracy, suggesting the potential to improve preventative action to reduce yellow fever spillover and prevent onward epidemics in humans. Many important (re)emerging infectious diseases in humans-including Ebola, sudden acute respiratory syndrome (SARS), influenza, Plasmodium knowlesi and other primate malarias, yellow fever, and leptospirosis-arise from spillover of pathogens from wildlife or livestock into human populations (1,2). While spillover is an important mechanism of human disease emergence, the drivers and dynamics of spillover are poorly understood and difficult to predict (3). Pathogen spillover requires favorable conditions to align in the reservoir (non-human animal), human, and pathogen populations and in the environment (3-5). Because these conditions interact, nonlinear relationships among the environment, host populations, and spillover probability are likely to emerge. Moreover, spillover is a probabilistic process that does not always occur, even when suitable conditions align. Despite these challenges, it is critical to predict pathogen spillover to enhance public health preparedness. Predicting spillover also provides an opportunity to test ecological approaches to solving globally important human health problems.Most previous attempts to predict pathogen spillover have used statistical models (6-8). These models may be locally accurate for within-sample prediction, but may struggle to detect multidimensional, nonlinear, and stochastic relationships among host populations, pathogens, the environment, and spillover. In contrast, mechanistic models can test our understanding of transmission ecology, reproduce the complex, nonlinear interactions emerging in disease systems, and potentially improve our ability to predict spillover. In parti...

Are we overestimating risk of enteric pathogen spillover from wild birds to humans?

“…Models integrating data across the entire process are no trivial undertaking (Plowright et al ., ; Childs et al ., ; Cross et al ., ; Washburne et al ., ). Cross et al .…”

“…Finally, Childs et al . () present an environmental risk model to examine spillover probability of yellow fever virus from non‐human primates to humans that may be successfully modified to model enteric pathogen spillover. However, the models presented by Childs et al .…”

Mathematically modeling spillovers of an emerging infectious zoonosis with an intermediate host

“…In an intermediate host species, a pathogen can gain more exposure to humans and mutate to a human-transmissible form, an evolution not previously studied. Childs et al (2019) [ 14 ] consider the risk of yellow fever spillover in Brazil, but do not investigate reservoir infection dynamics nor consider pathogen mutation over the course of an epidemic. Similarly, Washburne et al (2019) [ 15 ] introduce percolation models of pathogen spillover in an attempt to capture the complexity of multispecies diseases, but note that this type of model does not capture epidemiological feedback between nonhuman species.…”

Dynamic and integrative approaches to understanding pathogen spillover

“…Emphasizing that statistical modelling efforts may struggle to detect nonlinear and stochastic relationships inherent in pathogen spillover, Childs et al . [ 15 ] provide a strong test of theory governing how hierarchical barriers control cross-species transmission [ 9 ]. The authors focus their case study on yellow fever, a mosquito-borne viral disease of historical importance in South America that persists in the region largely in sylvatic cycles that occasionally spill over to infect humans [ 16 , 17 ].…”

“…Washburne et al [ 17 ] study the general statistical problems that can arise when aiming to forecast spillover risk. The authors highlight that any such statistical efforts will compile a dataset of explanatory variables expected to relate to pre-spillover processes (e.g., infection prevalence in reservoirs, human vaccination coverage) that are aligned with one of two response variables: the presence and absence of spillover or the number of spillover events at some spatial and temporal resolution (e.g., spatio-temporal counts of yellow fever spillovers [ 15 ]). The authors show how modelling cross-species transmission as a percolation process, in which pathogens move from infected reservoirs to recipient hosts along a graph representing various spillover pathways [ 18 , 19 ], reveals first principles for how such datasets will behave and how common statistical tools can produce misleading inferences and poor predictions.…”