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Spatial prediction of Crimean Congo hemorrhagic fever virus seroprevalence among livestock in Uganda
Crimean-Congo Hemorrhagic Fever (CCHF) is a viral disease that can infect humans via contact with tick vectors or livestock reservoirs and can cause moderate to severe disease. The first human case of CCHF in Uganda was identified in 2013. To determine the geographic distribution of the CCHF virus (...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10665170/ https://www.ncbi.nlm.nih.gov/pubmed/38024282 http://dx.doi.org/10.1016/j.onehlt.2023.100576 |
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author | Telford, Carson Nyakarahuka, Luke Waller, Lance Kitron, Uriel Shoemaker, Trevor |
author_facet | Telford, Carson Nyakarahuka, Luke Waller, Lance Kitron, Uriel Shoemaker, Trevor |
author_sort | Telford, Carson |
collection | PubMed |
description | Crimean-Congo Hemorrhagic Fever (CCHF) is a viral disease that can infect humans via contact with tick vectors or livestock reservoirs and can cause moderate to severe disease. The first human case of CCHF in Uganda was identified in 2013. To determine the geographic distribution of the CCHF virus (CCHFV), serosampling among herds of livestock was conducted in 28 Uganda districts in 2017. A geostatistical model of CCHF seroprevalence among livestock was developed to incorporate environmental and anthropogenic variables associated with elevated CCHF seroprevalence to predict CCHF seroprevalence on a map of Uganda and estimate the probability that CCHF seroprevalence exceeded 30% at each prediction location. Environmental and anthropogenic variables were also analyzed in separate models to determine the spatially varying drivers of prediction and determine which covariate class resulted in best prediction certainty. Covariates used in the full model included distance to the nearest croplands, average annual change in night-time light index, percent sand soil content, land surface temperature, and enhanced vegetation index. Elevated CCHF seroprevalence occurred in patches throughout the country, being highest in northern Uganda. Environmental covariates drove predicted seroprevalence in the full model more than anthropogenic covariates. Combination of environmental and anthropogenic variables resulted in the best prediction certainty. An understanding of the spatial distribution of CCHF across Uganda and the variables that drove predictions can be used to prioritize specific locations and activities to reduce the risk of future CCHF transmission. |
format | Online Article Text |
id | pubmed-10665170 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-106651702023-06-12 Spatial prediction of Crimean Congo hemorrhagic fever virus seroprevalence among livestock in Uganda Telford, Carson Nyakarahuka, Luke Waller, Lance Kitron, Uriel Shoemaker, Trevor One Health Research Paper Crimean-Congo Hemorrhagic Fever (CCHF) is a viral disease that can infect humans via contact with tick vectors or livestock reservoirs and can cause moderate to severe disease. The first human case of CCHF in Uganda was identified in 2013. To determine the geographic distribution of the CCHF virus (CCHFV), serosampling among herds of livestock was conducted in 28 Uganda districts in 2017. A geostatistical model of CCHF seroprevalence among livestock was developed to incorporate environmental and anthropogenic variables associated with elevated CCHF seroprevalence to predict CCHF seroprevalence on a map of Uganda and estimate the probability that CCHF seroprevalence exceeded 30% at each prediction location. Environmental and anthropogenic variables were also analyzed in separate models to determine the spatially varying drivers of prediction and determine which covariate class resulted in best prediction certainty. Covariates used in the full model included distance to the nearest croplands, average annual change in night-time light index, percent sand soil content, land surface temperature, and enhanced vegetation index. Elevated CCHF seroprevalence occurred in patches throughout the country, being highest in northern Uganda. Environmental covariates drove predicted seroprevalence in the full model more than anthropogenic covariates. Combination of environmental and anthropogenic variables resulted in the best prediction certainty. An understanding of the spatial distribution of CCHF across Uganda and the variables that drove predictions can be used to prioritize specific locations and activities to reduce the risk of future CCHF transmission. Elsevier 2023-06-12 /pmc/articles/PMC10665170/ /pubmed/38024282 http://dx.doi.org/10.1016/j.onehlt.2023.100576 Text en © 2023 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Paper Telford, Carson Nyakarahuka, Luke Waller, Lance Kitron, Uriel Shoemaker, Trevor Spatial prediction of Crimean Congo hemorrhagic fever virus seroprevalence among livestock in Uganda |
title | Spatial prediction of Crimean Congo hemorrhagic fever virus seroprevalence among livestock in Uganda |
title_full | Spatial prediction of Crimean Congo hemorrhagic fever virus seroprevalence among livestock in Uganda |
title_fullStr | Spatial prediction of Crimean Congo hemorrhagic fever virus seroprevalence among livestock in Uganda |
title_full_unstemmed | Spatial prediction of Crimean Congo hemorrhagic fever virus seroprevalence among livestock in Uganda |
title_short | Spatial prediction of Crimean Congo hemorrhagic fever virus seroprevalence among livestock in Uganda |
title_sort | spatial prediction of crimean congo hemorrhagic fever virus seroprevalence among livestock in uganda |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10665170/ https://www.ncbi.nlm.nih.gov/pubmed/38024282 http://dx.doi.org/10.1016/j.onehlt.2023.100576 |
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