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Modelling African horse sickness emergence and transmission in the South African control area using a deterministic metapopulation approach
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de Klerk, Joanna N., Gorsich, Erin E., Grewar, John D., Atkins, Benjamin D., Tennant, Warren, Labuschagne, Karien and Tildesley, Michael J. (2023) Modelling African horse sickness emergence and transmission in the South African control area using a deterministic metapopulation approach. PLOS Computational Biology, 19 (9). e1011448. doi:10.1371/journal.pcbi.1011448 ISSN 1553-7358.
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Official URL: https://doi.org/10.1371/journal.pcbi.1011448
Abstract
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 Rv (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. Rv 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.
Item Type: | Journal Article | |||||||||
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Subjects: | S Agriculture > SF Animal culture | |||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) | |||||||||
SWORD Depositor: | Library Publications Router | |||||||||
Library of Congress Subject Headings (LCSH): | African horse sickness, African horse sickness -- Transmission -- South Africa, Animals -- Diseases -- South Africa | |||||||||
Journal or Publication Title: | PLOS Computational Biology | |||||||||
Publisher: | Public Library of Science | |||||||||
ISSN: | 1553-7358 | |||||||||
Official Date: | 6 September 2023 | |||||||||
Dates: |
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Volume: | 19 | |||||||||
Number: | 9 | |||||||||
Article Number: | e1011448 | |||||||||
DOI: | 10.1371/journal.pcbi.1011448 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||
Date of first compliant deposit: | 20 September 2023 | |||||||||
Date of first compliant Open Access: | 20 September 2023 | |||||||||
RIOXX Funder/Project Grant: |
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