Informing public health decisions with geospatial models of vector-borne disease
Loading...
Citations
Altmetric:
Altmetric::
Download
Associated Records
Authors
File Licence
Editors
Date
2024
Source Title
Journal Title
Journal ISSN
Volume Title
Publisher
Melbourne Authors
Affiliation
School of Mathematics and Statistics
Document Type
PhD thesis
Subject Code
Citation
Access Status
This item is embargoed and will be available on 2027-04-09.
DOI
Access URL
Open Access URL
Open Access at PMC
Linked Resource URL
NHMRC Grant Code
ARC Grant Code
Description
© 2024 Lucinda Elizabeth Harrison
Abstract
Disease surveillance aims to collect data at different times or locations, to improve our understanding of the spatial and/or temporal distribution of a disease. Surveillance data assist public health authorities to distribute resources where and when they are needed; data collection is typically limited by budget, time and resources. Surveillance activities should therefore be optimised subject to specific stakeholder priorities.
The geographic distributions of zoonotic, vector-borne pathogens are inherently linked to the environment through the habitat preferences of their host and vector species. In this thesis, I consider the case studies of Plasmodium knowlesi malaria in Indonesia and Japanese encephalitis virus in Australia. The geospatial distribution of P. knowlesi malaria is likely environmentally stable, although it is poorly understood and there is limited existing surveillance data in much of the parasite's nidus of transmission, including in western Indonesia. The southern extent of Japanese encephalitis virus transmission was historically the Torres Strait and Far North Queensland, but the virus spread widely across mainland Australia during 2021-23. There is considerable uncertainty in the ecology and transmission cycle of both pathogens, diagnostic challenges and operational constraints on prospective surveillance activities, given the wide possible extents of pathogen transmission.
Species distribution models predict the occurrence or abundance of species through space given limited sampling data. These models make predictions based on the locations of sampling alone or given some information about the environment where sampling was carried out. The outputs of species distribution models provide more information to decision-makers than sampling data alone, predicting to unsampled areas and accounting for sampling bias. However, the precise way that models can be used to inform decision-making is highly dependent on the decision scenario, objectives and constraints.
In this thesis, I explore geospatial models of zoonotic, vector-borne disease risk and their use in multi-criteria and multi-objective decision support. I consider the optimisation of model-informed and logistically-focused objectives and constraints, which may be applied to different disease transmission settings. I explore two frameworks for the surveillance of P. knowlesi malaria in Indonesia, under two different surveillance scenarios: active surveillance in humans and targeted surveillance of attendees to existing primary healthcare centres. Under active surveillance, I explore model-informed objectives and logistic constraints and develop an interactive web application for use by decision-makers. I extend this framework to the problem of selecting primary healthcare centres by considering several models of travel to healthcare and optimising model-informed and logistically-focused objectives simultaneously. Following the outbreak of Japanese encephalitis virus in Australia in 2021-23, I describe a geospatial assessment of transmission risk, drawing on estimated distributions of non-human host and vector habitat and disease surveillance data. I estimate the potential and realised distributions of the pathogen in Australia, then use these results to optimise pathogen surveillance in mosquitoes. I frame this optimisation task as a multi-objective maximal covering location problem and solve the problem via a heuristic algorithm to find candidate optimal designs for surveillance of Japanese encephalitis virus in mosquitoes in two Australian jurisdictions. The methods explored in this thesis aim to use surveillance data of zoonotic, vector-borne diseases to quantitatively inform public health decision-making, through geospatial modelling and the careful selection of decision objectives and constraints. These methods may be applied to other surveillance problems and diseases, to support decision-makers to make the most effective use of finitely available disease surveillance resources.
Notes
Terms of Use
Keywords
geospatial modelling, structured decision-making, multi-criteria decision-making, multi-objective decision-making, ecological niche modelling, heuristic optimisation, zoonoses, arboviruses, Plasmodium knowlesi malaria, Japanese encephalitis virus