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

ABSTRACT: 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.

SUBMITTER: Whittaker C

PROVIDER: S-EPMC9006040 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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

Whittaker Charles C Winskill Peter P Sinka Marianne M Pironon Samuel S Massey Claire C Weiss Daniel J DJ Nguyen Michele M Gething Peter W PW Kumar Ashwani A Ghani Azra A Bhatt Samir S

Proceedings. Biological sciences 20220413 1972

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, a ...[more]

PMID: 35414241

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

Publications

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

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