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Quantifying spatio-temporal variation in malaria transmission in near elimination settings using individual level surveillance data

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Title: Quantifying spatio-temporal variation in malaria transmission in near elimination settings using individual level surveillance data
Authors: Routledge, Isobel
Item Type: Thesis or dissertation
Abstract: As countries move towards malaria elimination, tracking progress through quantifying changes in transmission over space and time is key. This information is necessary to effectively target resources to remaining ‘hotspots’ (high-risk locations) and ‘hotpops’ (high-risk populations) where transmission remains, decide if and when it is appropriate to scale back interventions, and to evaluate the success of existing interventions. However, as countries approach zero cases, it becomes difficult to measure transmission. Traditional metrics, such as the prevalence of parasites in the population, are no longer appropriate due to small numbers and increasingly focal distributions of cases over space and time. In order to address this, this thesis developed Bayesian network inference approaches to utilise information about the time and location of cases showing symptoms of malaria to jointly infer the likelihood that a) each observed case was linked to another by transmission and b) that a case was infected by an external, unobserved source. This information was used to calculate individual reproduction numbers for each reported case, or how many new cases of malaria are expected to have resulted from each case. In elimination settings, quantifying the distribution of individual reproduction numbers provides useful information about how quickly a disease may die out, and how the introduction of new cases through importation may affect ongoing transmission. These estimates were incorporated into additive regression models as well as geostatistical models to map how malaria transmission varied over space and time as well as considering timelines to elimination and the likelihood of resurgence of transmission once zero cases is achieved. This approach was applied to previously unanalysed individual-level datasets of malaria cases from China and El Salvador.
Content Version: Open Access
Issue Date: Sep-2019
Date Awarded: Mar-2020
URI: http://hdl.handle.net/10044/1/80191
DOI: https://doi.org/10.25560/80191
Copyright Statement: Creative Commons Attribution NonCommercial Licence
Supervisor: Bhatt, Samir
Ghani, Azra
Walker, Patrick
Sponsor/Funder: Wellcome Trust (London, England)
Funder's Grant Number: 109310/Z/15/Z
Department: School of Public Health
Publisher: Imperial College London
Qualification Level: Doctoral
Qualification Name: Doctor of Philosophy (PhD)
Appears in Collections:School of Public Health PhD Theses