Fusing an agent-based model of mosquito population dynamics with a statistical reconstruction of spatio-temporal abundance patterns

• Type of Material: Article
• Authors: Seann C Cavany, University of Notre Dame; Guido C España, University of Notre Dame; Alun Lloyd, North Carolina State University; Gonzalo Vazquez-Prokopec, Emory University; Helvio S Astete, U.S. Naval Medical Research Unit Six, Lima, Peru; Lance Waller, Emory University; Uriel Kitron, Emory University; Thomas W Scott, University of California Davis; Amy S Morrison, University of California Davis; Robert C Reiner Jr, University of Washington; Alex T Perkins, University of Notre Dame
• Language: English
• Date: 2023-04-01
• Publisher: PUBLIC LIBRARY SCIENCE
• Publication Version: Final Published Version
• Copyright Statement: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
• License: Creative Commons Universal : Public Domain Dedication
• Final Published Version (URL): http://dx.doi.org/10.1371/journal.pcbi.1010424
• Title of Journal or Parent Work: PLOS COMPUTATIONAL BIOLOGY
• Volume: 19
• Issue: 4
• Start Page: e1010424
• End Page: e1010424
• Grant/Funding Information: SMC, GFCE, GMVP, ACM, TWS, RCR, and TAP were supported by grant P01AI098670 (TWS, PI) from the National Institutes of Health, National Institute for Allergy and Infectious Disease (https://www.niaid.nih.gov). In addition, this work was supported by the NIH National Institute of General Medical Sciences R35 MIRA program to TAP (R35GM143029). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
• Keywords: EFFICACY; EMERGENCY CONTROL; LIFE TABLE MODEL; Mathematical & Computational Biology; Biochemical Research Methods; Science & Technology; Biochemistry & Molecular Biology; CULICIDAE; AEDES-AEGYPTI DIPTERA; Life Sciences & Biomedicine

Abstract

The mosquito Aedes aegypti is the vector of a number of medically-important viruses, including dengue virus, yellow fever virus, chikungunya virus, and Zika virus, and as such vector control is a key approach to managing the diseases they cause. Understanding the impact of vector control on these diseases is aided by first understanding its impact on Ae. aegypti population dynamics. A number of detail-rich models have been developed to couple the dynamics of the immature and adult stages of Ae. aegypti. The numerous assumptions of these models enable them to realistically characterize impacts of mosquito control, but they also constrain the ability of such models to reproduce empirical patterns that do not conform to the models’ behavior. In contrast, statistical models afford sufficient flexibility to extract nuanced signals from noisy data, yet they have limited ability to make predictions about impacts of mosquito control on disease caused by pathogens that the mosquitoes transmit without extensive data on mosquitoes and disease. Here, we demonstrate how the differing strengths of mechanistic realism and statistical flexibility can be fused into a single model. Our analysis utilizes data from 176,352 household-level Ae. aegypti aspirator collections conducted during 1999–2011 in Iquitos, Peru. The key step in our approach is to calibrate a single parameter of the model to spatio-temporal abundance patterns predicted by a generalized additive model (GAM). In effect, this calibrated parameter absorbs residual variation in the abundance time-series not captured by other features of the mechanistic model. We then used this calibrated parameter and the literature-derived parameters in the agent-based model to explore Ae. aegypti population dynamics and the impact of insecticide spraying to kill adult mosquitoes. The baseline abundance predicted by the agent-based model closely matched that predicted by the GAM. Following spraying, the agent-based model predicted that mosquito abundance rebounds within about two months, commensurate with recent experimental data from Iquitos. Our approach was able to accurately reproduce abundance patterns in Iquitos and produce a realistic response to adulticide spraying, while retaining sufficient flexibility to be applied across a range of settings.