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Title: Quantifying spatio-temporal variation in malaria transmission in near elimination settings using individual level surveillance data
Author: Routledge, Isobel
ISNI:       0000 0004 9350 0599
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2019
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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.
Supervisor: Bhatt, Samir ; Ghani, Azra ; Walker, Patrick Sponsor: Wellcome Trust
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral