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

Lead Research Organisation: University of Warwick
Department Name: Mathematics


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)

Planned Impact

In the 2018 Government Office for Science report, 'Computational Modelling: Technological Futures', Greg Clarke, the Secretary of State for Business Energy and Industrial Strategy, wrote "Computational modelling is essential to our future productivity and competitiveness, for businesses of all sizes and across all sectors of the economy". With its focus on computational models, the mathematics that underpin them, and their integration with complex data, the MathSys II CDT will generate diverse impacts beyond academia. This includes impacts on skills, on the economy, on policy and on society.

Impacts on skills.
MathSys II will produce a minimum of 50 PhD graduates to support the growing national demand for advanced mathematical modelling and data analysis skills. The CDT will provide each of them with broad core skills in the MSc, a deep knowledge of their chosen research specialisation in the PhD and a complementary qualification in transferable skills integrated throughout. Graduates will thus acquire the profiles needed to form the next generation of leaders in business, government and academia. They will be supported by an integrated pastoral support framework, including a diverse group of accessible leadership role models. The cohort based environment of the CDT provides a multiplier effect by encouraging cohorts to forge long-lasting professional networks whose value and influence will long outlast the CDT itself. MathSys II will seek to maximise the influence of these networks by providing topical training in Responsible Research and Innovation, by maintaining a robust Equality, Diversity & Inclusion policy, and by integration with Warwick's global network of international partnerships.

Economic impacts.
The research outputs from many MathSys II PhD projects will be of direct economic value to commercial, public sector and charitable external partners. Engagement with CDT partners will facilitate these impacts. This includes co-supervision of PhD and MSc projects, co-creation of Research Study Groups, and a strong commitment to provide placements/internships for CDT students. When commercial innovations or IP are generated, we will work with Warwick Ventures, the commercial arm of the University of Warwick, to commercialise/license IP where appropriate. Economic impact may also come from the creation of new companies by CDT graduates. MathSys II will present entrepreneurship as a viable career option to students. One external partner, Spectra Analytics, was founded by graduates of the preceding Complexity Science CDT, thus providing accessible role models. We will also provide in-house entrepreneurship training via Warwick Ventures and host events by external start-up accelerator Entrepreneur First.

Impacts on policy.
The CDT will influence policy at the national and international level by working with external partners operating in policy. UK examples include Department of Health, Public Health England and DEFRA. International examples include World Health Organisation (WHO) and the European Commission for the Control of Foot-and-mouth Disease (EuFMD). MathSys students will also utilise the recently announced UKRI policy internships scheme.

Impacts on society.
Public engagement will allow CDT students to promote the value of their research to society at large. Aside from social media, suitable local events include DataBeers, Cafe Scientifique, and the Big Bang Fair. MathSys will also promote a socially-oriented ethos of technology for the common good. Concretely, this includes the creation of open-source software, integration of software and data carpentry into our computational and data driven research training and championing open-access to research. We will also contribute to the 'innovation culture and science' strand of Coventry's 2021 City of Culture programme.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S022244/1 01/10/2019 31/03/2028
2271213 Studentship EP/S022244/1 01/10/2019 30/09/2023 Melissa Iacovidou