Predicting disease risk areas through co-production of spatial models: The example of Kyasanur Forest Disease in India's forest landscapes.
Zoonotic diseases affect resource-poor tropical communities disproportionately, and are linked to human use and modification of ecosystems. Disentangling the socio-ecological mechanisms by which ecosystem change precipitates impacts of pathogens is critical for predicting disease risk and designing...
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ftdoajarticles:oai:doaj.org/article:7d5e183f2ab94efba0cdaeb468128498 2023-05-15T15:15:01+02:00 Predicting disease risk areas through co-production of spatial models: The example of Kyasanur Forest Disease in India's forest landscapes. Bethan V Purse Narayanaswamy Darshan Gudadappa S Kasabi France Gerard Abhishek Samrat Charles George Abi T Vanak Meera Oommen Mujeeb Rahman Sarah J Burthe Juliette C Young Prashanth N Srinivas Stefanie M Schäfer Peter A Henrys Vijay K Sandhya M Mudassar Chanda Manoj V Murhekar Subhash L Hoti Shivani K Kiran 2020-04-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0008179 https://doaj.org/article/7d5e183f2ab94efba0cdaeb468128498 EN eng Public Library of Science (PLoS) https://doi.org/10.1371/journal.pntd.0008179 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0008179 https://doaj.org/article/7d5e183f2ab94efba0cdaeb468128498 PLoS Neglected Tropical Diseases, Vol 14, Iss 4, p e0008179 (2020) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2020 ftdoajarticles https://doi.org/10.1371/journal.pntd.0008179 2022-12-31T07:49:42Z Zoonotic diseases affect resource-poor tropical communities disproportionately, and are linked to human use and modification of ecosystems. Disentangling the socio-ecological mechanisms by which ecosystem change precipitates impacts of pathogens is critical for predicting disease risk and designing effective intervention strategies. Despite the global "One Health" initiative, predictive models for tropical zoonotic diseases often focus on narrow ranges of risk factors and are rarely scaled to intervention programs and ecosystem use. This study uses a participatory, co-production approach to address this disconnect between science, policy and implementation, by developing more informative disease models for a fatal tick-borne viral haemorrhagic disease, Kyasanur Forest Disease (KFD), that is spreading across degraded forest ecosystems in India. We integrated knowledge across disciplines to identify key risk factors and needs with actors and beneficiaries across the relevant policy sectors, to understand disease patterns and develop decision support tools. Human case locations (2014-2018) and spatial machine learning quantified the relative role of risk factors, including forest cover and loss, host densities and public health access, in driving landscape-scale disease patterns in a long-affected district (Shivamogga, Karnataka State). Models combining forest metrics, livestock densities and elevation accurately predicted spatial patterns in human KFD cases (2014-2018). Consistent with suggestions that KFD is an "ecotonal" disease, landscapes at higher risk for human KFD contained diverse forest-plantation mosaics with high coverage of moist evergreen forest and plantation, high indigenous cattle density, and low coverage of dry deciduous forest. Models predicted new hotspots of outbreaks in 2019, indicating their value for spatial targeting of intervention. Co-production was vital for: gathering outbreak data that reflected locations of exposure in the landscape; better understanding contextual socio-ecological ... Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic PLOS Neglected Tropical Diseases 14 4 e0008179 |
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English |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 Bethan V Purse Narayanaswamy Darshan Gudadappa S Kasabi France Gerard Abhishek Samrat Charles George Abi T Vanak Meera Oommen Mujeeb Rahman Sarah J Burthe Juliette C Young Prashanth N Srinivas Stefanie M Schäfer Peter A Henrys Vijay K Sandhya M Mudassar Chanda Manoj V Murhekar Subhash L Hoti Shivani K Kiran Predicting disease risk areas through co-production of spatial models: The example of Kyasanur Forest Disease in India's forest landscapes. |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
Zoonotic diseases affect resource-poor tropical communities disproportionately, and are linked to human use and modification of ecosystems. Disentangling the socio-ecological mechanisms by which ecosystem change precipitates impacts of pathogens is critical for predicting disease risk and designing effective intervention strategies. Despite the global "One Health" initiative, predictive models for tropical zoonotic diseases often focus on narrow ranges of risk factors and are rarely scaled to intervention programs and ecosystem use. This study uses a participatory, co-production approach to address this disconnect between science, policy and implementation, by developing more informative disease models for a fatal tick-borne viral haemorrhagic disease, Kyasanur Forest Disease (KFD), that is spreading across degraded forest ecosystems in India. We integrated knowledge across disciplines to identify key risk factors and needs with actors and beneficiaries across the relevant policy sectors, to understand disease patterns and develop decision support tools. Human case locations (2014-2018) and spatial machine learning quantified the relative role of risk factors, including forest cover and loss, host densities and public health access, in driving landscape-scale disease patterns in a long-affected district (Shivamogga, Karnataka State). Models combining forest metrics, livestock densities and elevation accurately predicted spatial patterns in human KFD cases (2014-2018). Consistent with suggestions that KFD is an "ecotonal" disease, landscapes at higher risk for human KFD contained diverse forest-plantation mosaics with high coverage of moist evergreen forest and plantation, high indigenous cattle density, and low coverage of dry deciduous forest. Models predicted new hotspots of outbreaks in 2019, indicating their value for spatial targeting of intervention. Co-production was vital for: gathering outbreak data that reflected locations of exposure in the landscape; better understanding contextual socio-ecological ... |
format |
Article in Journal/Newspaper |
author |
Bethan V Purse Narayanaswamy Darshan Gudadappa S Kasabi France Gerard Abhishek Samrat Charles George Abi T Vanak Meera Oommen Mujeeb Rahman Sarah J Burthe Juliette C Young Prashanth N Srinivas Stefanie M Schäfer Peter A Henrys Vijay K Sandhya M Mudassar Chanda Manoj V Murhekar Subhash L Hoti Shivani K Kiran |
author_facet |
Bethan V Purse Narayanaswamy Darshan Gudadappa S Kasabi France Gerard Abhishek Samrat Charles George Abi T Vanak Meera Oommen Mujeeb Rahman Sarah J Burthe Juliette C Young Prashanth N Srinivas Stefanie M Schäfer Peter A Henrys Vijay K Sandhya M Mudassar Chanda Manoj V Murhekar Subhash L Hoti Shivani K Kiran |
author_sort |
Bethan V Purse |
title |
Predicting disease risk areas through co-production of spatial models: The example of Kyasanur Forest Disease in India's forest landscapes. |
title_short |
Predicting disease risk areas through co-production of spatial models: The example of Kyasanur Forest Disease in India's forest landscapes. |
title_full |
Predicting disease risk areas through co-production of spatial models: The example of Kyasanur Forest Disease in India's forest landscapes. |
title_fullStr |
Predicting disease risk areas through co-production of spatial models: The example of Kyasanur Forest Disease in India's forest landscapes. |
title_full_unstemmed |
Predicting disease risk areas through co-production of spatial models: The example of Kyasanur Forest Disease in India's forest landscapes. |
title_sort |
predicting disease risk areas through co-production of spatial models: the example of kyasanur forest disease in india's forest landscapes. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2020 |
url |
https://doi.org/10.1371/journal.pntd.0008179 https://doaj.org/article/7d5e183f2ab94efba0cdaeb468128498 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 14, Iss 4, p e0008179 (2020) |
op_relation |
https://doi.org/10.1371/journal.pntd.0008179 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0008179 https://doaj.org/article/7d5e183f2ab94efba0cdaeb468128498 |
op_doi |
https://doi.org/10.1371/journal.pntd.0008179 |
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PLOS Neglected Tropical Diseases |
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14 |
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4 |
container_start_page |
e0008179 |
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