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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2023-09-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011448&type=printable |
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| author | Joanna N de Klerk Erin E Gorsich John D Grewar Benjamin D Atkins Warren S D Tennant Karien Labuschagne Michael J Tildesley |
| author_facet | Joanna N de Klerk Erin E Gorsich John D Grewar Benjamin D Atkins Warren S D Tennant Karien Labuschagne Michael J Tildesley |
| author_sort | Joanna N de Klerk |
| collection | DOAJ |
| 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 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. |
| first_indexed | 2024-03-11T21:54:20Z |
| format | Article |
| id | doaj.art-0669e491d76c4945bd7d42db6812c79e |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-03-11T21:54:20Z |
| publishDate | 2023-09-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-0669e491d76c4945bd7d42db6812c79e2023-09-26T05:30:46ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582023-09-01199e101144810.1371/journal.pcbi.1011448Modelling African horse sickness emergence and transmission in the South African control area using a deterministic metapopulation approach.Joanna N de KlerkErin E GorsichJohn D GrewarBenjamin D AtkinsWarren S D TennantKarien LabuschagneMichael J TildesleyAfrican 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.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011448&type=printable |
| spellingShingle | Joanna N de Klerk Erin E Gorsich John D Grewar Benjamin D Atkins Warren S D Tennant Karien Labuschagne Michael J Tildesley Modelling African horse sickness emergence and transmission in the South African control area using a deterministic metapopulation approach. PLoS Computational Biology |
| 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 |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011448&type=printable |
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