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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月1日

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  • Soushieta Jagadesh,
  • Claudia Cataldo,
  • Wim Van Bortel,
  • Esther Van Kleef,
  • William Wint,
  • Annapaola Rizzoli,
  • Luca Busani,
  • Elena Arsevska

摘要

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.