Modelling the Wolbachia incompatible insect technique: strategies for effective mosquito population elimination
Pagendam, D.E., Trewin, B.J., Snoad, N, Ritchie, S.A., Hoffman, A.A., Staunton, K.M., Paton, C, and Beebe, N (2020) Modelling the Wolbachia incompatible insect technique: strategies for effective mosquito population elimination. BMC Biology, 18. 161.
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Abstract
Background: The Wolbachia incompatible insect technique (IIT) shows promise as a method for eliminating populations of invasive mosquitoes such as Aedes aegypti (Linnaeus) (Diptera: Culicidae) and reducing the incidence of vector-borne diseases such as dengue, chikungunya and Zika. Successful implementation of this biological control strategy relies on high-fidelity separation of male from female insects in mass production systems for inundative release into landscapes. Processes for sex-separating mosquitoes are typically error-prone and laborious, and IIT programmes run the risk of releasing Wolbachia-infected females and replacing wild mosquito populations.
Results: We introduce a simple Markov population process model for studying mosquito populations subjected to a Wolbachia-IIT programme which exhibit an unstable equilibrium threshold. The model is used to study, in silico, scenarios that are likely to yield a successful elimination result. Our results suggest that elimination is best achieved by releasing males at rates that adapt to the ever-decreasing wild population, thus reducing the risk of releasing Wolbachia-infected females while reducing costs.
Conclusions: While very high-fidelity sex separation is required to avoid establishment, release programmes tend to be robust to the release of a small number of Wolbachia-infected females. These findings will inform and enhance the next generation of Wolbachia-IIT population control strategies that are already showing great promise in field trials.
| Item ID: | 65111 |
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| Item Type: | Article (Research - C1) |
| ISSN: | 1741-7007 |
| Keywords: | incompatible insect technique, Wolbachia, establishment risk, elimination, Stochastic model, simulation |
| Copyright Information: | © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. |
| Funders: | Australian National Health and Medical Research Council |
| Projects and Grants: | NHMRC 1082127 |
| Date Deposited: | 22 Nov 2020 22:01 |
| FoR Codes: | 42 HEALTH SCIENCES > 4203 Health services and systems > 420315 One health @ 100% |
| SEO Codes: | 92 HEALTH > 9204 Public Health (excl. Specific Population Health) > 920499 Public Health (excl. Specific Population Health) not elsewhere classified @ 100% |
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