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Modelling African horse sickness emergence and transmission in the South African control area using a deterministic metapopulation approach
African horse sickness is an equine orbivirus transmitted by Culicoides Latreille biting midges. In the last 80 years, it has caused several devastating outbreaks in the equine population in Europe, the Far and Middle East, North Africa, South-East Asia, and sub-Saharan Africa. The disease is endemi...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10506717/ https://www.ncbi.nlm.nih.gov/pubmed/37672554 http://dx.doi.org/10.1371/journal.pcbi.1011448 |
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author | de Klerk, Joanna N. Gorsich, Erin E. Grewar, John D. Atkins, Benjamin D. Tennant, Warren S. D. Labuschagne, Karien Tildesley, Michael J. |
author_facet | de Klerk, Joanna N. Gorsich, Erin E. Grewar, John D. Atkins, Benjamin D. Tennant, Warren S. D. Labuschagne, Karien Tildesley, Michael J. |
author_sort | de Klerk, Joanna N. |
collection | PubMed |
description | African horse sickness is an equine orbivirus transmitted by Culicoides Latreille biting midges. In the last 80 years, it has caused several devastating outbreaks in the equine population in Europe, the Far and Middle East, North Africa, South-East Asia, and sub-Saharan Africa. The disease is endemic in South Africa; however, a unique control area has been set up in the Western Cape where increased surveillance and control measures have been put in place. A deterministic metapopulation model was developed to explore if an outbreak might occur, and how it might develop, if a latently infected horse was to be imported into the control area, by varying the geographical location and months of import. To do this, a previously published ordinary differential equation model was developed with a metapopulation approach and included a vaccinated horse population. Outbreak length, time to peak infection, number of infected horses at the peak, number of horses overall affected (recovered or dead), re-emergence, and R(v) (the basic reproduction number in the presence of vaccination) were recorded and displayed using GIS mapping. The model predictions were compared to previous outbreak data to ensure validity. The warmer months (November to March) had longer outbreaks than the colder months (May to September), took more time to reach the peak, and had a greater total outbreak size with more horses infected at the peak. R(v) appeared to be a poor predictor of outbreak dynamics for this simulation. A sensitivity analysis indicated that control measures such as vaccination and vector control are potentially effective to manage the spread of an outbreak, and shortening the vaccination window to July to September may reduce the risk of vaccine-associated outbreaks. |
format | Online Article Text |
id | pubmed-10506717 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-105067172023-09-19 Modelling African horse sickness emergence and transmission in the South African control area using a deterministic metapopulation approach de Klerk, Joanna N. Gorsich, Erin E. Grewar, John D. Atkins, Benjamin D. Tennant, Warren S. D. Labuschagne, Karien Tildesley, Michael J. PLoS Comput Biol Research Article African horse sickness is an equine orbivirus transmitted by Culicoides Latreille biting midges. In the last 80 years, it has caused several devastating outbreaks in the equine population in Europe, the Far and Middle East, North Africa, South-East Asia, and sub-Saharan Africa. The disease is endemic in South Africa; however, a unique control area has been set up in the Western Cape where increased surveillance and control measures have been put in place. A deterministic metapopulation model was developed to explore if an outbreak might occur, and how it might develop, if a latently infected horse was to be imported into the control area, by varying the geographical location and months of import. To do this, a previously published ordinary differential equation model was developed with a metapopulation approach and included a vaccinated horse population. Outbreak length, time to peak infection, number of infected horses at the peak, number of horses overall affected (recovered or dead), re-emergence, and R(v) (the basic reproduction number in the presence of vaccination) were recorded and displayed using GIS mapping. The model predictions were compared to previous outbreak data to ensure validity. The warmer months (November to March) had longer outbreaks than the colder months (May to September), took more time to reach the peak, and had a greater total outbreak size with more horses infected at the peak. R(v) appeared to be a poor predictor of outbreak dynamics for this simulation. A sensitivity analysis indicated that control measures such as vaccination and vector control are potentially effective to manage the spread of an outbreak, and shortening the vaccination window to July to September may reduce the risk of vaccine-associated outbreaks. Public Library of Science 2023-09-06 /pmc/articles/PMC10506717/ /pubmed/37672554 http://dx.doi.org/10.1371/journal.pcbi.1011448 Text en © 2023 de Klerk et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article de Klerk, Joanna N. Gorsich, Erin E. Grewar, John D. Atkins, Benjamin D. Tennant, Warren S. D. Labuschagne, Karien Tildesley, Michael J. Modelling African horse sickness emergence and transmission in the South African control area using a deterministic metapopulation approach |
title | Modelling African horse sickness emergence and transmission in the South African control area using a deterministic metapopulation approach |
title_full | Modelling African horse sickness emergence and transmission in the South African control area using a deterministic metapopulation approach |
title_fullStr | Modelling African horse sickness emergence and transmission in the South African control area using a deterministic metapopulation approach |
title_full_unstemmed | Modelling African horse sickness emergence and transmission in the South African control area using a deterministic metapopulation approach |
title_short | Modelling African horse sickness emergence and transmission in the South African control area using a deterministic metapopulation approach |
title_sort | modelling african horse sickness emergence and transmission in the south african control area using a deterministic metapopulation approach |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10506717/ https://www.ncbi.nlm.nih.gov/pubmed/37672554 http://dx.doi.org/10.1371/journal.pcbi.1011448 |
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