Mapping global risk of bat and rodent borne disease outbreaks to anticipate emerging threats

Abstract

Future epidemics and/or pandemics may likely arise from zoonotic viruses with bat- and rodent-borne pathogens being among the prime candidates. To improve preparedness and prevention strategies, we predicted the global distribution of bat- and rodent-borne viral infectious disease outbreaks using geospatial modeling. We developed species distribution models based on published outbreak occurrence data, applying machine learning and Bayesian statistical approaches to assess disease risk. Our models demonstrated high predictive accuracy (TSS = 0.87 for bat-borne, 0.90 for rodent-borne diseases), identifying precipitation and bushmeat activities as key drivers for bat-borne diseases, while deforestation, human population density, and minimum temperature influenced rodent-borne diseases. The predicted risk areas for bat-borne diseases were concentrated in Africa, whereas rodent-borne diseases were widespread across the Americas and Europe. Our findings provide geospatial tools for policymakers to prioritize surveillance and resource allocation, enhance early detection and rapid response efforts. By improving reporting and data quality, predictive models can be further refined and strengthen public health preparedness against potential future emerging infectious disease threats.

Fig. 1

Global distribution of bat-borne (blue triangles) and rodent-borne (red diamonds) viral disease outbreak emergence events resulting from zoonotic transmission, identified through a systematic literature search. Each symbol represents a distinct event in space and time, with only the first reported case per pathogen within a 10 km radius included to ensure spatial independence.

Fig. 2

(A) Global distribution of the probability of bat-borne diseases outbreaks from an ensemble of three model predictions using a hierarchical binomial model with spatial autocorrelation. (B) Global distribution of the probability of rodent-borne diseases outbreaks from an ensemble of three model predictions using a hierarchical binomial model with spatial autocorrelation. Gray areas indicate regions where probability is below 0.01. The maps were generated using QGIS (version 3.34.0-Prizren), an open-source geographic information system, available at https://qgis.org.

Fig. 3

(A) The ensemble model, (B) the ensemble models with 10% and 25%, (C) the ensemble model with 50% reduction in uncertainty of the probability of bat-borne diseases at the scale of Europe; (D) The ensemble model, (E) the ensemble models with 10% and 25%, (F) the ensemble model with 50% reduction in uncertainty of the probability of rodent-borne diseases at the scale of Europe. The maps were generated using QGIS (version 3.34.0-Prizren), an open-source geographic information system, available at https://qgis.org.

Fig. 4

Methodological workflow for mapping the global distribution of bat- (BID in the figure) and rodent-borne (RID in the figure) infectious diseases. RF- Random Forest model and BRT- Boosted Regression Tree.