Global hotspots and correlates of emerging zoonotic diseases
Open accessAbstract
Zoonoses originating from wildlife represent a significant threat to global health, security and economic growth, and combatting their emergence is a public health priority. However, our understanding of the mechanisms underlying their emergence remains rudimentary. Here we update a global database of emerging infectious disease (EID) events, create a novel measure of reporting effort, and fit boosted regression tree models to analyze the demographic, environmental and biological correlates of their occurrence. After accounting for reporting effort, we show that zoonotic EID risk is elevated in forested tropical regions experiencing land-use changes and where wildlife biodiversity (mammal species richness) is high. We present a new global hotspot map of spatial variation in our zoonotic EID risk index, and partial dependence plots illustrating relationships between events and predictors. Our results may help to improve surveillance and long-term EID monitoring programs, and design field experiments to test underlying mechanisms of zoonotic disease emergence.
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Geographic comparisons through large-scale correlational studies based on the "total host diversity" model have tended to report a weak but positive effect of mammal species richness on zoonotic diseases, but these studies show much stronger positive correlations with other factors, such as human population density.
“Geographic comparisons through large-scale correlational studies (based on the “total host diversity” model in Fig. 1) have tended to report a weak but positive effect of mammal species richness on zoonotic diseases, but these studies show much stronger positive correlations with other factors, such as human population density (e.g., refs. 5 and 17).”
From Impacts of biodiversity and biodiversity loss on zoonotic diseases (2021) · cites this paper as [17]
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- Authors
- Allen, T., Murray, K. A., Zambrana-Torrelio, C., Morse, S. S., Rondinini, C., Di Marco, M., Breit, N., Olival, K. J., & Daszak, P.
- Affiliations
- EcoHealth Alliance (US)
- Imperial College London (GB)
- Columbia University (US)
- Sapienza University of Rome (IT)
- Corresponding author
- Daszak, Peter
- ORCID
- Venue
- Nature Communications
- Published
- 2017-10-24
- Volume
- 8
- Issue
- 1
- Pages
- 1124-1124
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC5654761
- OpenAlex
- W2765563228
- Cited by
- 1,124
- References
- 62
- Citations by year
- 2026: 103 · 2025: 136 · 2024: 139 · 2023: 175 · 2022: 168 · 2021: 190 · 2020: 140 · 2019: 49 · 2018: 20 · 2017: 1 · 2015: 1 · 2012: 1
- FWCI
- 53.93
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion, cc-by)
- Spiral (Imperial College London) (submittedVersion, cc-by)
- Funders
- United States Agency for International Development (AIDOAAA1400102)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Zoonotic diseases and public health
- Health Sciences › Medicine › Infectious Diseases › Viral Infections and Outbreaks Research
- Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
- Keywords
- zoonoses, emerging infectious diseases, tropical forests, land use change, mammal species richness, disease hotspots, disease surveillance, boosted regression trees, partial dependence
- MeSH terms
- Animals, Animals, Wild, Demography, Disease Reservoirs, Geography, Humans, Models, Theoretical, Regression Analysis, Risk, Tropical Climate, Global Health, Zoonoses, Area Under Curve, Communicable Diseases, Emerging, Biodiversity, Forests
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