Vector bionomics and vectorial capacity as emergent properties of mosquito behaviors and ecology
File(s)S. L. Wu - Vector bionomics and vectori...pdf (10.45 MB)
Published version
Author(s)
Type
Journal Article
Abstract
Mosquitoes are important vectors for pathogens that infect humans and other vertebrate animals. Some aspects of adult mosquito behavior and mosquito ecology play an important role in determining the capacity of vector populations to transmit pathogens. Here, we re-examine factors affecting the transmission of pathogens by mosquitoes using a new approach. Unlike most previous models, this framework considers the behavioral states and state transitions of adult mosquitoes through a sequence of activity bouts. We developed a new framework for individual-based simulation models called MBITES (Mosquito Bout-based and Individual-based Transmission Ecology Simulator). In MBITES, it is possible to build models that simulate the behavior and ecology of adult mosquitoes in exquisite detail on complex resource landscapes generated by spatial point processes. We also developed an ordinary differential equation model which is the Kolmogorov forward equations for models developed in MBITES under a specific set of simplifying assumptions. While mosquito infection and pathogen development are one possible part of a mosquito’s state, that is not our main focus. Using extensive simulation using some models developed in MBITES, we show that vectorial capacity can be understood as an emergent property of simple behavioral algorithms interacting with complex resource landscapes, and that relative density or sparsity of resources and the need to search can have profound consequences for mosquito populations’ capacity to transmit pathogens.
Editor(s)
Lloyd-Smith, James
Date Issued
2020-04-22
Date Acceptance
2020-03-21
Citation
PLoS Computational Biology, 2020, 16 (4), pp.1-32
ISSN
1553-734X
Publisher
Public Library of Science (PLoS)
Start Page
1
End Page
32
Journal / Book Title
PLoS Computational Biology
Volume
16
Issue
4
Copyright Statement
Copyright: © 2020 Wu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
License URL
Identifier
http://dx.doi.org/10.1371/journal.pcbi.1007446
Publication Status
Published
Date Publish Online
2020-04-22