Predictors of human-infective RNA virus discovery in the United States, China and Africa, an ecological study
Background: The variation in the pathogen type as well as the spatial heterogeneity of predictors make the generality of any associations with pathogen discovery debatable. Our previous work confirmed that the association of a group of predictors differed across different types of RNA viruses, yet there have been no previous comparisons of the specific predictors for RNA virus discovery in different regions. The aim of the current study was to close the gap by investigating whether predictors of discovery rates within three regions-the United States, China, and Africa-differ from one another and from those at the global level.
Methods: Based on a comprehensive list of human-infective RNA viruses, we collated published data on first discovery of each species in each region. We used a Poisson boosted regression tree (BRT) model to examine the relationship between virus discovery and 33 predictors representing climate, socio-economics, land use, and biodiversity across each region separately. The discovery probability in three regions in 2010-2019 was mapped using the fitted models and historical predictors.
Results: The numbers of human-infective virus species discovered in the United States, China, and Africa up to 2019 were 95, 80 and 107 respectively, with China lagging behind the other two regions. In each region, discoveries were clustered in hotspots. BRT modelling suggested that in all three regions RNA virus discovery was better predicted by land use and socio-economic variables than climatic variables and biodiversity, though the relative importance of these predictors varied by region. Map of virus discovery probability in 2010-2019 indicated several new hotspots outside historical high-risk areas. Most new virus species since 2010 in each region (6/6 in the United States, 19/19 in China, 12/19 in Africa) were discovered in high-risk areas as predicted by our model.
Conclusions: The drivers of spatiotemporal variation in virus discovery rates vary in different regions of the world. Within regions virus discovery is driven mainly by land-use and socio-economic variables; climate and biodiversity variables are consistently less important predictors than at a global scale. Potential new discovery hotspots in 2010-2019 are identified. Results from the study could guide active surveillance for new human-infective viruses in local high-risk areas.
Funding: FFZ is funded by the Darwin Trust of Edinburgh (https://darwintrust.bio.ed.ac.uk/). MEJW has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 874735 (VEO) (https://www.veo-europe.eu/).
The authors confirm that all data or the data sources are provided in the paper and its Supplementary Materials. The final datasets and codes used for the analyses are available via figshare at https://doi.org/10.6084/m9.figshare.15101979.
Article and author information
Darwin Trust of Edinburgh
- Feifei Zhang
European Union's Horizon 2020 research and innovation programme (874735)
- Mark EJ Woolhouse
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
- Ben S Cooper, Mahidol University, Thailand
- Received: July 12, 2021
- Preprint posted: September 15, 2021 (view preprint)
- Accepted: May 31, 2022
- Accepted Manuscript published: June 6, 2022 (version 1)
- Version of Record published: July 13, 2022 (version 2)
© 2022, Zhang et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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