A Holistic Approach to Dynamic Modelling of Malaria Transmission. An Investigation of Climate-Based Models used for Predicting Malaria Transmission

Abstract

The uninterrupted spread of malaria, besides its seasonal uncertainty, is due to the lack of suitable planning and intervention mechanisms and tools. Several studies have been carried out to understand the factors that affect the development and transmission of malaria, but these efforts have been largely limited to piecemeal specific methods, hence they do not offer comprehensive solutions to predict disease outbreaks. This thesis introduces a ’holistic’ approach to understand the relationship between climate parameters and the occurrence of malaria using both mathematical and computational methods. In this respect, we develop new climate-based models using mathematical, agent-based and data-driven modelling techniques. A malaria model is developed using mathematical modelling to investigate the impact of temperature-dependent delays. Although this method is widely applicable, but it is limited to the study of homogeneous populations. An agent-based technique is employed to address this limitation, where the spatial and temporal variability of agents involved in the transmission of malaria are taken into account. Moreover, whilst the mathematical and agent-based approaches allow for temperature and precipitation in the modelling process, they do not capture other dynamics that might potentially affect malaria. Hence, to accommodate the climatic predictors of malaria, an intelligent predictive model is developed using machine-learning algorithms, which supports predictions of endemics in certain geographical areas by monitoring the risk factors, e.g., temperature and humidity. The thesis not only synthesises mathematical and computational methods to better understand the disease dynamics and its transmission, but also provides healthcare providers and policy makers with better planning and intervention tools.