Abstract
The aim of this research project is the development of new advanced, state-of-the-art methodology for epidemiologists, mathematical modellers and biostatisticians interested in modelling vector-borne infectious disease transmission. More specifically, we focus on the estimation of age- and time-dependent epidemiological malaria parameters, correcting for other attribute data, governing the spread and transmission of malaria. In addition, interest is in the identification of key individuals responsible for sustained malaria transmission in low transmission settings. Based on social network analysis techniques, we aim at gaining insights relevant for the development of malaria elimination strategies. In conclusion, the main objectives in this proposal are (1) the development of novel methodology to integrate mathematical and statistical models to estimate time- and age-varying malaria epidemiological parameters in the presence of unobserved heterogeneity; (2) the development of approaches to deal with doubly interval censored observations in combination with outcome-dependent sampling and heterogeneity; and (3) the study of heterogeneity in household conditions and individual attribute data using social network data. Although special attention is directed to malaria, the methodology developed in this project is more widely applicable in the context of vector-borne infections in both human and animal populations.
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