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Modeling the effectiveness of targeting Rift Valley fever virus vaccination using imperfect network information
Livestock movements contribute to the spread of several infectious diseases. Data on livestock movements can therefore be harnessed to guide policy on targeted interventions for controlling infectious livestock diseases, including Rift Valley fever (RVF)—a vaccine-preventable arboviral fever. Detail...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10340087/ https://www.ncbi.nlm.nih.gov/pubmed/37456963 http://dx.doi.org/10.3389/fvets.2023.1049633 |
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author | Sulaimon, Tijani A. Chaters, Gemma L. Nyasebwa, Obed M. Swai, Emanuel S. Cleaveland, Sarah Enright, Jessica Kao, Rowland R. Johnson, Paul C. D. |
author_facet | Sulaimon, Tijani A. Chaters, Gemma L. Nyasebwa, Obed M. Swai, Emanuel S. Cleaveland, Sarah Enright, Jessica Kao, Rowland R. Johnson, Paul C. D. |
author_sort | Sulaimon, Tijani A. |
collection | PubMed |
description | Livestock movements contribute to the spread of several infectious diseases. Data on livestock movements can therefore be harnessed to guide policy on targeted interventions for controlling infectious livestock diseases, including Rift Valley fever (RVF)—a vaccine-preventable arboviral fever. Detailed livestock movement data are known to be useful for targeting control efforts including vaccination. These data are available in many countries, however, such data are generally lacking in others, including many in East Africa, where multiple RVF outbreaks have been reported in recent years. Available movement data are imperfect, and the impact of this uncertainty in the utility of movement data on informing targeting of vaccination is not fully understood. Here, we used a network simulation model to describe the spread of RVF within and between 398 wards in northern Tanzania connected by cattle movements, on which we evaluated the impact of targeting vaccination using imperfect movement data. We show that pre-emptive vaccination guided by only market movement permit data could prevent large outbreaks. Targeted control (either by the risk of RVF introduction or onward transmission) at any level of imperfect movement information is preferred over random vaccination, and any improvement in information reliability is advantageous to their effectiveness. Our modeling approach demonstrates how targeted interventions can be effectively used to inform animal and public health policies for disease control planning. This is particularly valuable in settings where detailed data on livestock movements are either unavailable or imperfect due to resource limitations in data collection, as well as challenges associated with poor compliance. |
format | Online Article Text |
id | pubmed-10340087 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103400872023-07-14 Modeling the effectiveness of targeting Rift Valley fever virus vaccination using imperfect network information Sulaimon, Tijani A. Chaters, Gemma L. Nyasebwa, Obed M. Swai, Emanuel S. Cleaveland, Sarah Enright, Jessica Kao, Rowland R. Johnson, Paul C. D. Front Vet Sci Veterinary Science Livestock movements contribute to the spread of several infectious diseases. Data on livestock movements can therefore be harnessed to guide policy on targeted interventions for controlling infectious livestock diseases, including Rift Valley fever (RVF)—a vaccine-preventable arboviral fever. Detailed livestock movement data are known to be useful for targeting control efforts including vaccination. These data are available in many countries, however, such data are generally lacking in others, including many in East Africa, where multiple RVF outbreaks have been reported in recent years. Available movement data are imperfect, and the impact of this uncertainty in the utility of movement data on informing targeting of vaccination is not fully understood. Here, we used a network simulation model to describe the spread of RVF within and between 398 wards in northern Tanzania connected by cattle movements, on which we evaluated the impact of targeting vaccination using imperfect movement data. We show that pre-emptive vaccination guided by only market movement permit data could prevent large outbreaks. Targeted control (either by the risk of RVF introduction or onward transmission) at any level of imperfect movement information is preferred over random vaccination, and any improvement in information reliability is advantageous to their effectiveness. Our modeling approach demonstrates how targeted interventions can be effectively used to inform animal and public health policies for disease control planning. This is particularly valuable in settings where detailed data on livestock movements are either unavailable or imperfect due to resource limitations in data collection, as well as challenges associated with poor compliance. Frontiers Media S.A. 2023-06-29 /pmc/articles/PMC10340087/ /pubmed/37456963 http://dx.doi.org/10.3389/fvets.2023.1049633 Text en Copyright © 2023 Sulaimon, Chaters, Nyasebwa, Swai, Cleaveland, Enright, Kao and Johnson. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Veterinary Science Sulaimon, Tijani A. Chaters, Gemma L. Nyasebwa, Obed M. Swai, Emanuel S. Cleaveland, Sarah Enright, Jessica Kao, Rowland R. Johnson, Paul C. D. Modeling the effectiveness of targeting Rift Valley fever virus vaccination using imperfect network information |
title | Modeling the effectiveness of targeting Rift Valley fever virus vaccination using imperfect network information |
title_full | Modeling the effectiveness of targeting Rift Valley fever virus vaccination using imperfect network information |
title_fullStr | Modeling the effectiveness of targeting Rift Valley fever virus vaccination using imperfect network information |
title_full_unstemmed | Modeling the effectiveness of targeting Rift Valley fever virus vaccination using imperfect network information |
title_short | Modeling the effectiveness of targeting Rift Valley fever virus vaccination using imperfect network information |
title_sort | modeling the effectiveness of targeting rift valley fever virus vaccination using imperfect network information |
topic | Veterinary Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10340087/ https://www.ncbi.nlm.nih.gov/pubmed/37456963 http://dx.doi.org/10.3389/fvets.2023.1049633 |
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