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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2311.03654 |
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| _version_ | 1866909324554534912 |
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| author | Golchin, Maryam Di Marco, Moreno Horwood, Paul Paini, Dean Hoskins, Andrew Hickson, R. I. |
| author_facet | Golchin, Maryam Di Marco, Moreno Horwood, Paul Paini, Dean Hoskins, Andrew Hickson, R. I. |
| contents | 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 viral diversity and human population density. Although our results indicate higher risk in regions along the equator and in Southeast Asia where both viral 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 viral 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_03654 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Prediction of viral spillover risk based on the mass action principle Golchin, Maryam Di Marco, Moreno Horwood, Paul Paini, Dean Hoskins, Andrew Hickson, R. I. Populations and Evolution Physics and Society 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 viral diversity and human population density. Although our results indicate higher risk in regions along the equator and in Southeast Asia where both viral 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 viral 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. |
| title | Prediction of viral spillover risk based on the mass action principle |
| topic | Populations and Evolution Physics and Society |
| url | https://arxiv.org/abs/2311.03654 |