Infection with the Plasmodium falciparum malaria parasite results in an immune response which includes the production of antibodies against the blood-stage of infection. In recent years there has been an increase in the use of serological data to monitor malaria transmission intensity. Traditionally, EIR and parasite prevalence were the preferred tools for measuring malaria transmission intensity. Serology has been shown to be particularly useful in areas of low endemicity where traditional measures (EIR and parasite prevalence) are problematic. Transmission intensity in this case is usually described by the seroconversion rate obtained from fitting a catalytic model to age-stratified serological responses.
The aim of my thesis was to better utilise the continuous measurements of antibody responses provided by serology studies to obtain improved estimates of transmission intensity. To do this, I developed a series of biologically motivated models to mimic the acquisition and decay in blood-stage antibody responses.
In the first part of the thesis, I developed a discrete model as a direct extension of the catalytic model and fitted this to cross-sectional data from several sites in Cambodia to obtain an estimate of the exposure rate. In the second part of the thesis a series of continuous density models were developed to mimic antibody acquisition and loss for P. falciparum infections. These models were fitted to both the Cambodian data and separately validated by fitting to data from Tanzanian villages at a wider range of transmission intensities. In the final section I applied and extended the model to encompass a wider range of endemic transmission in Somalia, Bioko Island, Gambia and Uganda in order to assess the robustness of the method.
My results show that estimates of the exposure rate obtained by fitting the density model are highly correlated with classic malariometric indices and that a key advantage of this approach is the increased precision in the estimates compared to estimates of the seroconversion rate, especially in areas of low transmission. This method could therefore be a useful alternative framework for quantifying transmission intensity which makes more complete use of serological data and shows potentials for detecting heterogeneity in malaria exposure.