Golchin, Maryam, Di Marco, Moreno, Horwood, Paul F., Paini, Dean R., Hoskins, Andrew J., and Hickson, R.I. (2024) Prediction of viral spillover risk based on the mass action principle. One Health, 18. 100737.

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Abstract

Infectious zoonotic disease emergence, through spillover events, is of global concern and has the potential to cause significant harm to society, as recently demonstrated by COVID-19. More than 70% of the 400 infectious diseases that emerged in the past five decades have a zoonotic origin, including all recent pandemics. There have been several approaches used to predict the risk of spillover through some of the known or suspected infectious disease emergence drivers, largely using correlative approaches. Here, we predict the spatial distribution of spillover risk by approximating general transmission through animal and human interactions. These mass action interactions are approximated through the multiplication of the spatial distribution of zoonotic virus diversity and human population density. Although our results indicate higher risk in regions along the equator and in Southeast Asia where both virus diversity and human population density are high, it should be noted that this is primarily a conceptual exercise. We compared our spillover risk map to key factors, including the model inputs of zoonotic virus diversity estimate map, human population density map, and the spatial distribution of species richness. Despite the limitations of this approach, this viral spillover map is a step towards developing a more comprehensive spillover risk prediction system to inform global monitoring.

Item ID:	82711
Item Type:	Article (Research - C1)
ISSN:	2352-7714
Copyright Information:	© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/bync/4.0/).
Date Deposited:	11 Jun 2024 01:52
FoR Codes:	30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3009 Veterinary sciences > 300914 Veterinary virology @ 100%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280101 Expanding knowledge in the agricultural, food and veterinary sciences @ 100%
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