New approaches to modelling malaria transmission and insecticide resistance: using realistic mosquito biology and behaviour, and network methods.

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The proposed project aims to adapt standard mosquito-borne modelling frameworks by including additional details about mosquito biology and behaviour in the presence of insecticides. There will be a strong focus on time-series dynamics and spatial dynamics, both of which could have implications for real-world disease control programmes. Experimental data on mosquito behavior has been provided by the external partner to inform model development; it was collected during field experiments on the malaria vector, Anopheles gambiae s.l. The aim is to calibrate the adapted model to mosquito surveillance data, and ideally match to human malaria data as well, with the intention to focus on specific locations, depending on data availability, and the external partner's interests, for example Côte d'Ivoire and other African countries.The literature on malaria does not routinely focus on network models of mosquito-borne transmission with detailed vector biology included and so it would be interesting to develop theoretical approaches on networks having mosquito and human biology and behaviour in them. Such approaches, particularly those including the use of bednets and insecticide, would help understand malaria transmission dynamics, and could also be generalised for other mosquito- or vector-borne diseases such as dengue, or Rift Valley fever.The basic research questions are:(a) How does malaria spread and persist over time?(b) What is the impact of insecticide resistance on the spread?Mosquito and host movement during mosquito feeding could potentially give an insight to these questions, along with exploring the impact of insecticide exposure on mosquitoes and their ability to transmit malaria.The context of the research - This research relates to Mathematical Epidemiology, i.e. mathematical modelling of infectious diseases. Specifically, modelling of malaria, a serious mosquito-borne disease, which is the cause of death of thousands of people every year, especially in African countries. Emerging insecticide resistance among mosquitoes is a key concern and worthy of further exploration. The lack of research on networks regarding vector-borne diseases and detailed vector biology gives an opportunity to produce novel methodologies.The aims and objectives of the research - This project will adapt standard mosquito-borne modelling frameworks by taking into consideration details about mosquito biology and behaviour, and further understand the impact of insecticide resistance on malaria transmission. The aim is to calibrate the adapted model to real-life mosquito data collected in Côte d'Ivoire, and explore different scenarios regarding insecticides. An additional aim is to develop theoretical approaches on network models in order to better understand malaria transmission dynamics.The novelty of the research methodology - This project will apply adapted models on data collected from specific regions in Africa to compare the different model frameworks. Additionally, construct network models aiming to develop the limited existing research in network models regarding vector-borne disease and insecticide.The potential impact, applications, and benefits - Malaria affects millions of people each year. Insecticides (bednets or spraying) are one main way to control this disease due to the current lack of vaccine. This project could support understanding of appropriate disease control strategies in African countries by looking at malaria from using a novel network approach.How the research relates to the remit - This project falls into the category of Mathematical Biology and also Complexity Science. It aims to develop and apply mathematical techniques in order to investigate biological systems at a population level.Research area; Mathematical Sciences External Partner - Liverpool School of Tropical Medicine (LSTM)