Joint modelling of recurrent clinical malaria episodes and longitudinal parasitemia data
Date
2021
Authors
Stanley, Christopher Chikhosi
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Abstract
Background Plasmodium falciparum infection is one of the most common parasitic infections
in humans, with a disproportionately higher share of the problem in Sub-Saharan Africa SSA.
In Malawi, malaria is endemic and the prevalence of Plasmodium falciparum parasite is also
high. Individuals in high-transmission settings are exposed to bites of infected Anopheles
mosquitoes and develop frequent infections. With each infection, acquired immunity develops
making subsequent disease episodes less likely. Therefore, longitudinal cohorts are ideal to study
risk of malaria over time especially in high transmission areas because repeated infection is
common. When modelling the risk of malaria in a cohort study, methods ought to account for
this naturally acquired immunity which develops in individuals over time. Methods that focus on
first incident alone single event joint model excluding subsequent episodes may be inefficient
since much information is lost and may underestimate the disease burden. A gap exists in
literature on the use of joint modeling to analyze recurrent clinical malaria and parasitemia data
collected during cohort studies.
Description
A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy to the Faculty of Health Sciences, School of Public Health, University of the Witwatersrand, Johannesburg, 2021