Statistical methods for estimating age and time-dependent epidemiological malaria parameters and the analysis of social network data as a new approach to the development of malaria elimination strategies
PhD summary
Malaria is a life-threatening mosquito-borne disease which, despite increased efforts towards its eradication, remains the leading cause of deaths in African children. Malaria elimination is increasingly feasible in certain Sub-Saharan countries. However, some fundamental requirements for effective elimination strategies, such as ways of targeting key individuals and local clusters of infection sustaining or re-initiating transmission, remain unmet. The aim of this research project is to develop new methodologies for the estimation of age- and time[1]varying epidemiological parameters related to vector-borne diseases. In this research project, the focus is on malaria, which remains an important public health concern worldwide, but the general aim is to develop a translational framework applicable for the analysis of data on relevant vector-borne infectious diseases, since a lot of recent outbreaks have been vector-borne (e.g., Zika virus).
This research proposal features four objectives: (1) Developing methods to integrate both mathematical and statistical models in order to estimate age- and time-varying epidemiological parameters such as the force of infection and point prevalence based on longitudinal malaria parasitaemia cohort data in the presence of unobserved heterogeneity and outcome-dependent sampling; (2) Accommodate for the doubly interval censored nature of the data using appropriate statistical models thereby accounting for heterogeneity, outcome-dependent sampling as well as the dependence in recurrent infection times within the same individual; (3) Relate heterogeneity in household conditions and individual attributes to social network data and study differences in social network structures between malaria-infected and uninfected individuals using Social Network Analysis techniques, an applications of mathematical graph theory; (4) Combine the age- and time-dependent epidemiological malaria parameters, estimated in the presence of unobserved heterogeneity, outcome-dependent sampling and accounting for the doubly interval censored nature of the parasitaemia data, with the derived social network structures in a simulation model allowing for the study of elimination strategies.