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A novel statistical framework for exploring the population dynamics and seasonality of mosquito populations

Understanding the temporal dynamics of mosquito populations underlying vector-borne disease transmission is key to optimizing control strategies. Many questions remain surrounding the drivers of these dynamics and how they vary between species—questions rarely answerable from individual entomologica...

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Autores principales: Whittaker, Charles, Winskill, Peter, Sinka, Marianne, Pironon, Samuel, Massey, Claire, Weiss, Daniel J., Nguyen, Michele, Gething, Peter W., Kumar, Ashwani, Ghani, Azra, Bhatt, Samir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9006040/
https://www.ncbi.nlm.nih.gov/pubmed/35414241
http://dx.doi.org/10.1098/rspb.2022.0089
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author Whittaker, Charles
Winskill, Peter
Sinka, Marianne
Pironon, Samuel
Massey, Claire
Weiss, Daniel J.
Nguyen, Michele
Gething, Peter W.
Kumar, Ashwani
Ghani, Azra
Bhatt, Samir
author_facet Whittaker, Charles
Winskill, Peter
Sinka, Marianne
Pironon, Samuel
Massey, Claire
Weiss, Daniel J.
Nguyen, Michele
Gething, Peter W.
Kumar, Ashwani
Ghani, Azra
Bhatt, Samir
author_sort Whittaker, Charles
collection PubMed
description Understanding the temporal dynamics of mosquito populations underlying vector-borne disease transmission is key to optimizing control strategies. Many questions remain surrounding the drivers of these dynamics and how they vary between species—questions rarely answerable from individual entomological studies (that typically focus on a single location or species). We develop a novel statistical framework enabling identification and classification of time series with similar temporal properties, and use this framework to systematically explore variation in population dynamics and seasonality in anopheline mosquito time series catch data spanning seven species, 40 years and 117 locations across mainland India. Our analyses reveal pronounced variation in dynamics across locations and between species in the extent of seasonality and timing of seasonal peaks. However, we show that these diverse dynamics can be clustered into four ‘dynamical archetypes’, each characterized by distinct temporal properties and associated with a largely unique set of environmental factors. Our results highlight that a range of environmental factors including rainfall, temperature, proximity to static water bodies and patterns of land use (particularly urbanicity) shape the dynamics and seasonality of mosquito populations, and provide a generically applicable framework to better identify and understand patterns of seasonal variation in vectors relevant to public health.
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spelling pubmed-90060402022-04-20 A novel statistical framework for exploring the population dynamics and seasonality of mosquito populations Whittaker, Charles Winskill, Peter Sinka, Marianne Pironon, Samuel Massey, Claire Weiss, Daniel J. Nguyen, Michele Gething, Peter W. Kumar, Ashwani Ghani, Azra Bhatt, Samir Proc Biol Sci Ecology Understanding the temporal dynamics of mosquito populations underlying vector-borne disease transmission is key to optimizing control strategies. Many questions remain surrounding the drivers of these dynamics and how they vary between species—questions rarely answerable from individual entomological studies (that typically focus on a single location or species). We develop a novel statistical framework enabling identification and classification of time series with similar temporal properties, and use this framework to systematically explore variation in population dynamics and seasonality in anopheline mosquito time series catch data spanning seven species, 40 years and 117 locations across mainland India. Our analyses reveal pronounced variation in dynamics across locations and between species in the extent of seasonality and timing of seasonal peaks. However, we show that these diverse dynamics can be clustered into four ‘dynamical archetypes’, each characterized by distinct temporal properties and associated with a largely unique set of environmental factors. Our results highlight that a range of environmental factors including rainfall, temperature, proximity to static water bodies and patterns of land use (particularly urbanicity) shape the dynamics and seasonality of mosquito populations, and provide a generically applicable framework to better identify and understand patterns of seasonal variation in vectors relevant to public health. The Royal Society 2022-04-13 2022-04-13 /pmc/articles/PMC9006040/ /pubmed/35414241 http://dx.doi.org/10.1098/rspb.2022.0089 Text en © 2022 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited.
spellingShingle Ecology
Whittaker, Charles
Winskill, Peter
Sinka, Marianne
Pironon, Samuel
Massey, Claire
Weiss, Daniel J.
Nguyen, Michele
Gething, Peter W.
Kumar, Ashwani
Ghani, Azra
Bhatt, Samir
A novel statistical framework for exploring the population dynamics and seasonality of mosquito populations
title A novel statistical framework for exploring the population dynamics and seasonality of mosquito populations
title_full A novel statistical framework for exploring the population dynamics and seasonality of mosquito populations
title_fullStr A novel statistical framework for exploring the population dynamics and seasonality of mosquito populations
title_full_unstemmed A novel statistical framework for exploring the population dynamics and seasonality of mosquito populations
title_short A novel statistical framework for exploring the population dynamics and seasonality of mosquito populations
title_sort novel statistical framework for exploring the population dynamics and seasonality of mosquito populations
topic Ecology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9006040/
https://www.ncbi.nlm.nih.gov/pubmed/35414241
http://dx.doi.org/10.1098/rspb.2022.0089
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