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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Main Authors: Clinton B Leach, Jennifer A Hoeting, Kim M Pepin, Alvaro E Eiras, Mevin B Hooten, Colleen T Webb
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2020-11-01
Series:PLoS Neglected Tropical Diseases
Online Access:https://doi.org/10.1371/journal.pntd.0008868
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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
collection DOAJ
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.
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spelling doaj.art-435f11d9690a4a969153d9b3415f6c392022-12-21T21:30:17ZengPublic Library of Science (PLoS)PLoS Neglected Tropical Diseases1935-27271935-27352020-11-011411e000886810.1371/journal.pntd.0008868Linking mosquito surveillance to dengue fever through Bayesian mechanistic modeling.Clinton B LeachJennifer A HoetingKim M PepinAlvaro E EirasMevin B HootenColleen T WebbOur 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.https://doi.org/10.1371/journal.pntd.0008868
spellingShingle 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.
PLoS Neglected Tropical Diseases
title 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_short Linking mosquito surveillance to dengue fever through Bayesian mechanistic modeling.
title_sort linking mosquito surveillance to dengue fever through bayesian mechanistic modeling
url https://doi.org/10.1371/journal.pntd.0008868
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