Modelling the dynamics and control of aedes aegypti populations at fine spatial scales
File(s)
Author(s)
McCormack, Clare
Type
Thesis
Abstract
Novel vector control measures such as the release of Wolbachia-infected Aedes aegypti offer a promising new pathway for dengue control. However, to realistically model the likely impact of these measures, improved mathematical models of Aedes aegypti population dynamics are needed, with the final goal being spatially explicit models of Aedes aegypti population dynamics, calibrated against high quality entomological data.
Here we begin to address the challenge of developing such models by (i) examining the role
of spatial structure in shaping the dynamics of Aedes aegypti populations at fine spatial scales
and (ii) using advanced inferential methods to fit a dynamical model of Aedes aegypti population
dynamics to entomological field data, while allowing for the highly variable nature of mosquito trapping data.
We explore the effects of larval breeding habitat fragmentation on fine-scale Aedes aegypti
population dynamics for a variety of different landscapes, examining how features of the
underlying landscape and the dispersal behaviour of the mosquito affect the dynamics observed. In addition, by modelling the same population at different levels of spatial granularity, we investigate the appropriate level of spatial granularity for models to adopt to represent the fine-scale dynamics of Aedes aegypti populations.
We examine the results of a small-scale field trial testing the use of Wolbachia as a tool for
Aedes aegypti population suppression in Singapore by calibrating a stochastic model of Aedes aegypti population dynamics against the detailed entomological data collected during the trial. We model both the underlying population dynamics and the trapping process, thereby accounting for variability in mosquito trapping data when estimating the impact of the trial on local Aedes aegypti populations.
Thus, the work presented in this thesis represents an important step forward in the challenge of developing models needed to realistically assess the likely impact of novel vector control measures.
Here we begin to address the challenge of developing such models by (i) examining the role
of spatial structure in shaping the dynamics of Aedes aegypti populations at fine spatial scales
and (ii) using advanced inferential methods to fit a dynamical model of Aedes aegypti population
dynamics to entomological field data, while allowing for the highly variable nature of mosquito trapping data.
We explore the effects of larval breeding habitat fragmentation on fine-scale Aedes aegypti
population dynamics for a variety of different landscapes, examining how features of the
underlying landscape and the dispersal behaviour of the mosquito affect the dynamics observed. In addition, by modelling the same population at different levels of spatial granularity, we investigate the appropriate level of spatial granularity for models to adopt to represent the fine-scale dynamics of Aedes aegypti populations.
We examine the results of a small-scale field trial testing the use of Wolbachia as a tool for
Aedes aegypti population suppression in Singapore by calibrating a stochastic model of Aedes aegypti population dynamics against the detailed entomological data collected during the trial. We model both the underlying population dynamics and the trapping process, thereby accounting for variability in mosquito trapping data when estimating the impact of the trial on local Aedes aegypti populations.
Thus, the work presented in this thesis represents an important step forward in the challenge of developing models needed to realistically assess the likely impact of novel vector control measures.
Version
Open Access
Date Issued
2018-10
Date Awarded
2019-06
Copyright Statement
Creative Commons Attribution NonCommercial Licence
Advisor
Ferguson, Neil M.
Ghani, Azra C.
Sponsor
Medical Research Council (U.K.)
Grant Number
MR/L501414/1
Publisher Department
Department of Infectious Disease Epidemiology
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)