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Modeling the Impact of Migration on Mosquito Population Suppression

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Abstract

The Wolbachia-induced incompatible insect technique is a promising strategy for controlling wild mosquito populations. However, recent experimental studies have shown that mosquito migration into target areas dilutes the strategy’s effectiveness. In this work, we formulate a delay differential equation model to assess the impact of migration on mosquito population suppression. We identify that mosquito migration into an idealized target area makes it impossible to eliminate the target population completely. Our analysis identifies a lower bound of the suppression rate \(\gamma ^*\) for a given migration number, which reveals the possible maximum reduction of wild population size in the peak season. For a given suppression rate target \(\gamma _0>\gamma ^*\), we identify the permitted maximum migration number \(D(\gamma _0)\), above which is impossible to reduce the field mosquito density up to \((1-\gamma _0)\times 100\%\) in peak season. To reduce more than \(95\%\) of Aedes albopictus population during its peak season in Guangzhou within six weeks, the required minimum release number of Wolbachia-infected males climbs steeply as the migration number increases to D(0.05).

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Acknowledgements

We would like to thank four anonymous reviewers for valuable and precious comments on the manuscript. This work was supported by National Natural Science Foundation of China (12226414, 11471085, 11631005, 12171112).

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Authors and Affiliations

  1. School of Statistics and Mathematics, Guangdong University of Finance and Economics, Guangzhou, 510320, China

    Mugen Huang

  2. Guangzhou Center for Applied Mathematics, Guangzhou University, Guangzhou, 510006, China

    Jianshe Yu

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  1. Mugen Huang
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  2. Jianshe Yu
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MH and JY wrote the main manuscript text and prepared figures 1–3. All authors reviewed the manuscript.

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Correspondence to Jianshe Yu.

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Huang, M., Yu, J. Modeling the Impact of Migration on Mosquito Population Suppression. Qual. Theory Dyn. Syst. 22, 134 (2023). https://doi.org/10.1007/s12346-023-00834-8

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