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Does ignoring transmission dynamics lead to underestimation of the impact of interventions against mosquito-borne disease?

New vector-control technologies to fight mosquito-borne diseases are urgently needed, the adoption of which depends on efficacy estimates from large-scale cluster-randomised trials (CRTs). The release of Wolbachia-infected mosquitoes is one promising strategy to curb dengue virus (DENV) transmission...

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Autores principales: Cavany, Sean, Huber, John H, Wieler, Annaliese, Tran, Quan Minh, Alkuzweny, Manar, Elliott, Margaret, España, Guido, Moore, Sean M, Perkins, T Alex
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10476117/
https://www.ncbi.nlm.nih.gov/pubmed/37652566
http://dx.doi.org/10.1136/bmjgh-2023-012169
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author Cavany, Sean
Huber, John H
Wieler, Annaliese
Tran, Quan Minh
Alkuzweny, Manar
Elliott, Margaret
España, Guido
Moore, Sean M
Perkins, T Alex
author_facet Cavany, Sean
Huber, John H
Wieler, Annaliese
Tran, Quan Minh
Alkuzweny, Manar
Elliott, Margaret
España, Guido
Moore, Sean M
Perkins, T Alex
author_sort Cavany, Sean
collection PubMed
description New vector-control technologies to fight mosquito-borne diseases are urgently needed, the adoption of which depends on efficacy estimates from large-scale cluster-randomised trials (CRTs). The release of Wolbachia-infected mosquitoes is one promising strategy to curb dengue virus (DENV) transmission, and a recent CRT reported impressive reductions in dengue incidence following the release of these mosquitoes. Such trials can be affected by multiple sources of bias, however. We used mathematical models of DENV transmission during a CRT of Wolbachia-infected mosquitoes to explore three such biases: human movement, mosquito movement and coupled transmission dynamics between trial arms. We show that failure to account for each of these biases would lead to underestimated efficacy, and that the majority of this underestimation is due to a heretofore unrecognised bias caused by transmission coupling. Taken together, our findings suggest that Wolbachia-infected mosquitoes could be even more promising than the recent CRT suggested. By emphasising the importance of accounting for transmission coupling between arms, which requires a mathematical model, we highlight the key role that models can play in interpreting and extrapolating the results from trials of vector control interventions.
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spelling pubmed-104761172023-09-05 Does ignoring transmission dynamics lead to underestimation of the impact of interventions against mosquito-borne disease? Cavany, Sean Huber, John H Wieler, Annaliese Tran, Quan Minh Alkuzweny, Manar Elliott, Margaret España, Guido Moore, Sean M Perkins, T Alex BMJ Glob Health Analysis New vector-control technologies to fight mosquito-borne diseases are urgently needed, the adoption of which depends on efficacy estimates from large-scale cluster-randomised trials (CRTs). The release of Wolbachia-infected mosquitoes is one promising strategy to curb dengue virus (DENV) transmission, and a recent CRT reported impressive reductions in dengue incidence following the release of these mosquitoes. Such trials can be affected by multiple sources of bias, however. We used mathematical models of DENV transmission during a CRT of Wolbachia-infected mosquitoes to explore three such biases: human movement, mosquito movement and coupled transmission dynamics between trial arms. We show that failure to account for each of these biases would lead to underestimated efficacy, and that the majority of this underestimation is due to a heretofore unrecognised bias caused by transmission coupling. Taken together, our findings suggest that Wolbachia-infected mosquitoes could be even more promising than the recent CRT suggested. By emphasising the importance of accounting for transmission coupling between arms, which requires a mathematical model, we highlight the key role that models can play in interpreting and extrapolating the results from trials of vector control interventions. BMJ Publishing Group 2023-08-31 /pmc/articles/PMC10476117/ /pubmed/37652566 http://dx.doi.org/10.1136/bmjgh-2023-012169 Text en © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Analysis
Cavany, Sean
Huber, John H
Wieler, Annaliese
Tran, Quan Minh
Alkuzweny, Manar
Elliott, Margaret
España, Guido
Moore, Sean M
Perkins, T Alex
Does ignoring transmission dynamics lead to underestimation of the impact of interventions against mosquito-borne disease?
title Does ignoring transmission dynamics lead to underestimation of the impact of interventions against mosquito-borne disease?
title_full Does ignoring transmission dynamics lead to underestimation of the impact of interventions against mosquito-borne disease?
title_fullStr Does ignoring transmission dynamics lead to underestimation of the impact of interventions against mosquito-borne disease?
title_full_unstemmed Does ignoring transmission dynamics lead to underestimation of the impact of interventions against mosquito-borne disease?
title_short Does ignoring transmission dynamics lead to underestimation of the impact of interventions against mosquito-borne disease?
title_sort does ignoring transmission dynamics lead to underestimation of the impact of interventions against mosquito-borne disease?
topic Analysis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10476117/
https://www.ncbi.nlm.nih.gov/pubmed/37652566
http://dx.doi.org/10.1136/bmjgh-2023-012169
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