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Skeeter Buster: A Stochastic, Spatially Explicit Modeling Tool for Studying Aedes aegypti Population Replacement and Population Suppression Strategies

Author

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  • Krisztian Magori
  • Mathieu Legros
  • Molly E Puente
  • Dana A Focks
  • Thomas W Scott
  • Alun L Lloyd
  • Fred Gould

Abstract

Background: Dengue is the most important mosquito-borne viral disease affecting humans. The only prevention measure currently available is the control of its vectors, primarily Aedes aegypti. Recent advances in genetic engineering have opened the possibility for a new range of control strategies based on genetically modified mosquitoes. Assessing the potential efficacy of genetic (and conventional) strategies requires the availability of modeling tools that accurately describe the dynamics and genetics of Ae. aegypti populations. Methodology/Principal findings: We describe in this paper a new modeling tool of Ae. aegypti population dynamics and genetics named Skeeter Buster. This model operates at the scale of individual water-filled containers for immature stages and individual properties (houses) for adults. The biology of cohorts of mosquitoes is modeled based on the algorithms used in the non-spatial Container Inhabiting Mosquitoes Simulation Model (CIMSiM). Additional features incorporated into Skeeter Buster include stochasticity, spatial structure and detailed population genetics. We observe that the stochastic modeling of individual containers in Skeeter Buster is associated with a strongly reduced temporal variation in stage-specific population densities. We show that heterogeneity in container composition of individual properties has a major impact on spatial heterogeneity in population density between properties. We detail how adult dispersal reduces this spatial heterogeneity. Finally, we present the predicted genetic structure of the population by calculating FST values and isolation by distance patterns, and examine the effects of adult dispersal and container movement between properties. Conclusions/Significance: We demonstrate that the incorporated stochasticity and level of spatial detail have major impacts on the simulated population dynamics, which could potentially impact predictions in terms of control measures. The capacity to describe population genetics confers the ability to model the outcome of genetic control methods. Skeeter Buster is therefore an important tool to model Ae. aegypti populations and the outcome of vector control measures. Author Summary: Dengue is a viral disease that affects approximately 50 million people annually, and is estimated to result in 12,500 fatalities. Dengue viruses are vectored by mosquitoes, predominantly by the species Aedes aegypti. Because there is currently no vaccine or specific treatment, the only available strategy to reduce dengue transmission is to control the populations of these mosquitoes. This can be achieved by traditional approaches such as insecticides, or by recently developed genetic methods that propose the release of mosquitoes genetically engineered to be unable to transmit dengue viruses. The expected outcome of different control strategies can be compared by simulating the population dynamics and genetics of mosquitoes at a given location. Development of optimal control strategies can then be guided by the modeling approach. To that end, we introduce a new modeling tool called Skeeter Buster. This model describes the dynamics and the genetics of Ae. aegypti populations at a very fine scale, simulating the contents of individual houses, and even the individual water-holding containers in which mosquito larvae reside. Skeeter Buster can be used to compare the predicted outcomes of multiple control strategies, traditional or genetic, making it an important tool in the fight against dengue.

Suggested Citation

  • Krisztian Magori & Mathieu Legros & Molly E Puente & Dana A Focks & Thomas W Scott & Alun L Lloyd & Fred Gould, 2009. "Skeeter Buster: A Stochastic, Spatially Explicit Modeling Tool for Studying Aedes aegypti Population Replacement and Population Suppression Strategies," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 3(9), pages 1-18, September.
  • Handle: RePEc:plo:pntd00:0000508
    DOI: 10.1371/journal.pntd.0000508
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    Citations

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    Cited by:

    1. Dennis L Chao & Ira M Longini Jr & M Elizabeth Halloran, 2013. "The Effects of Vector Movement and Distribution in a Mathematical Model of Dengue Transmission," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-6, October.
    2. Dan Pagendam & Nigel Snoad & Wen-Hsi Yang & Michal Segoli & Scott Ritchie & Brendan Trewin & Nigel Beebe, 2018. "Improving Estimates of Fried’s Index from Mating Competitiveness Experiments," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(4), pages 446-462, December.
    3. Maneerat, Somsakun & Daudé, Eric, 2016. "A spatial agent-based simulation model of the dengue vector Aedes aegypti to explore its population dynamics in urban areas," Ecological Modelling, Elsevier, vol. 333(C), pages 66-78.
    4. Valdez, L.D. & Sibona, G.J. & Condat, C.A., 2018. "Impact of rainfall on Aedes aegypti populations," Ecological Modelling, Elsevier, vol. 385(C), pages 96-105.
    5. Walker, Melody & Robert, Michael A. & Childs, Lauren M., 2021. "The importance of density dependence in juvenile mosquito development and survival: A model-based investigation," Ecological Modelling, Elsevier, vol. 440(C).
    6. Romeo Aznar, Victoria & Otero, Marcelo & De Majo, María Sol & Fischer, Sylvia & Solari, Hernán G., 2013. "Modeling the complex hatching and development of Aedes aegypti in temperate climates," Ecological Modelling, Elsevier, vol. 253(C), pages 44-55.
    7. Amanda C. Walsh, 2019. "Impacts of Dengue Epidemics on Household Labor Market Outcomes," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 41(4), pages 684-702, December.
    8. Natiello, Mario A. & Solari, Hernán G., 2020. "Modelling population dynamics based on experimental trials with genetically modified (RIDL) mosquitoes," Ecological Modelling, Elsevier, vol. 424(C).
    9. T Alex Perkins & Thomas W Scott & Arnaud Le Menach & David L Smith, 2013. "Heterogeneity, Mixing, and the Spatial Scales of Mosquito-Borne Pathogen Transmission," PLOS Computational Biology, Public Library of Science, vol. 9(12), pages 1-16, December.
    10. Macias Torres, M. & Naranjo Mayorga, F., 2022. "Characterization of resilience in Aedes aegypti mosquito networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    11. Frieß, Johannes L. & Lalyer, Carina R. & Giese, Bernd & Simon, Samson & Otto, Mathias, 2023. "Review of gene drive modelling and implications for risk assessment of gene drive organisms," Ecological Modelling, Elsevier, vol. 478(C).

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