Linking mosquito surveillance to dengue fever through Bayesian mechanistic modeling.
Our ability to effectively prevent the transmission of the dengue virus through targeted control of its vector, Aedes aegypti, depends critically on our understanding of the link between mosquito abundance and human disease risk. Mosquito and clinical surveillance data are widely collected, but link...
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ftdoajarticles:oai:doaj.org/article:435f11d9690a4a969153d9b3415f6c39 2023-05-15T15:12:24+02:00 Linking mosquito surveillance to dengue fever through Bayesian mechanistic modeling. Clinton B Leach Jennifer A Hoeting Kim M Pepin Alvaro E Eiras Mevin B Hooten Colleen T Webb 2020-11-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0008868 https://doaj.org/article/435f11d9690a4a969153d9b3415f6c39 EN eng Public Library of Science (PLoS) https://doi.org/10.1371/journal.pntd.0008868 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0008868 https://doaj.org/article/435f11d9690a4a969153d9b3415f6c39 PLoS Neglected Tropical Diseases, Vol 14, Iss 11, p e0008868 (2020) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2020 ftdoajarticles https://doi.org/10.1371/journal.pntd.0008868 2022-12-31T07:50:09Z Our ability to effectively prevent the transmission of the dengue virus through targeted control of its vector, Aedes aegypti, depends critically on our understanding of the link between mosquito abundance and human disease risk. Mosquito and clinical surveillance data are widely collected, but linking them requires a modeling framework that accounts for the complex non-linear mechanisms involved in transmission. Most critical are the bottleneck in transmission imposed by mosquito lifespan relative to the virus' extrinsic incubation period, and the dynamics of human immunity. We developed a differential equation model of dengue transmission and embedded it in a Bayesian hierarchical framework that allowed us to estimate latent time series of mosquito demographic rates from mosquito trap counts and dengue case reports from the city of Vitória, Brazil. We used the fitted model to explore how the timing of a pulse of adult mosquito control influences its effect on the human disease burden in the following year. We found that control was generally more effective when implemented in periods of relatively low mosquito mortality (when mosquito abundance was also generally low). In particular, control implemented in early September (week 34 of the year) produced the largest reduction in predicted human case reports over the following year. This highlights the potential long-term utility of broad, off-peak-season mosquito control in addition to existing, locally targeted within-season efforts. Further, uncertainty in the effectiveness of control interventions was driven largely by posterior variation in the average mosquito mortality rate (closely tied to total mosquito abundance) with lower mosquito mortality generating systems more vulnerable to control. Broadly, these correlations suggest that mosquito control is most effective in situations in which transmission is already limited by mosquito abundance. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic PLOS Neglected Tropical Diseases 14 11 e0008868 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
spellingShingle |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 Clinton B Leach Jennifer A Hoeting Kim M Pepin Alvaro E Eiras Mevin B Hooten Colleen T Webb Linking mosquito surveillance to dengue fever through Bayesian mechanistic modeling. |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
Our ability to effectively prevent the transmission of the dengue virus through targeted control of its vector, Aedes aegypti, depends critically on our understanding of the link between mosquito abundance and human disease risk. Mosquito and clinical surveillance data are widely collected, but linking them requires a modeling framework that accounts for the complex non-linear mechanisms involved in transmission. Most critical are the bottleneck in transmission imposed by mosquito lifespan relative to the virus' extrinsic incubation period, and the dynamics of human immunity. We developed a differential equation model of dengue transmission and embedded it in a Bayesian hierarchical framework that allowed us to estimate latent time series of mosquito demographic rates from mosquito trap counts and dengue case reports from the city of Vitória, Brazil. We used the fitted model to explore how the timing of a pulse of adult mosquito control influences its effect on the human disease burden in the following year. We found that control was generally more effective when implemented in periods of relatively low mosquito mortality (when mosquito abundance was also generally low). In particular, control implemented in early September (week 34 of the year) produced the largest reduction in predicted human case reports over the following year. This highlights the potential long-term utility of broad, off-peak-season mosquito control in addition to existing, locally targeted within-season efforts. Further, uncertainty in the effectiveness of control interventions was driven largely by posterior variation in the average mosquito mortality rate (closely tied to total mosquito abundance) with lower mosquito mortality generating systems more vulnerable to control. Broadly, these correlations suggest that mosquito control is most effective in situations in which transmission is already limited by mosquito abundance. |
format |
Article in Journal/Newspaper |
author |
Clinton B Leach Jennifer A Hoeting Kim M Pepin Alvaro E Eiras Mevin B Hooten Colleen T Webb |
author_facet |
Clinton B Leach Jennifer A Hoeting Kim M Pepin Alvaro E Eiras Mevin B Hooten Colleen T Webb |
author_sort |
Clinton B Leach |
title |
Linking mosquito surveillance to dengue fever through Bayesian mechanistic modeling. |
title_short |
Linking mosquito surveillance to dengue fever through Bayesian mechanistic modeling. |
title_full |
Linking mosquito surveillance to dengue fever through Bayesian mechanistic modeling. |
title_fullStr |
Linking mosquito surveillance to dengue fever through Bayesian mechanistic modeling. |
title_full_unstemmed |
Linking mosquito surveillance to dengue fever through Bayesian mechanistic modeling. |
title_sort |
linking mosquito surveillance to dengue fever through bayesian mechanistic modeling. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2020 |
url |
https://doi.org/10.1371/journal.pntd.0008868 https://doaj.org/article/435f11d9690a4a969153d9b3415f6c39 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 14, Iss 11, p e0008868 (2020) |
op_relation |
https://doi.org/10.1371/journal.pntd.0008868 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0008868 https://doaj.org/article/435f11d9690a4a969153d9b3415f6c39 |
op_doi |
https://doi.org/10.1371/journal.pntd.0008868 |
container_title |
PLOS Neglected Tropical Diseases |
container_volume |
14 |
container_issue |
11 |
container_start_page |
e0008868 |
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1766343089950031872 |