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Mathematical modelling, particularly when combined with economical modelling, allows researchers and policy makers to determine the most effective interventions to fight infectious diseases such as malaria. We can use those models to explore ‘what ifs’ scenarios, at country or province level, save more lives and limit costs.

My name is Lisa White and I am a Professor of Modelling and Epidemiology at the Nuffield Department of Medicine, Oxford University.

Mathematical Modelling is a framework for understanding a particular system, for example infectious diseases. If we understand the way the disease transmits and we understand the biology and the behaviour of the people and how they pass the disease on to each other, then we can translate that understanding into mathematical language, and then it becomes a mathematical model. Once it is in that language we can use the model to explore ‘what ifs’ scenarios for the future and determine whether, if we intervened in some way –  treating everyone or providing a vaccine –  how that would have an impact in the future on people’s health and also how much it would cost.

I believe that mathematical modelling can help us fight tropical illnesses, mainly from a strategic point of view. For example, if there is a particular budget available to control or fight a tropical disease at the population level, then what mathematical modelling can do is determine the most effective way to intervene with that disease to control it, therefore save more lives and buy more health for your money.

An example of some research that my team and I have been doing recently is to produce a mathematical and economic model for multiple species of malaria for the Asia-Pacific region. The purpose of this model framework was to explore strategies for elimination of malaria for the entire region. This is a piece of work still underway and about to be published.

With our Asia-Pacific model, our challenge was to make it spatially explicit, which means that although we were trying to make a model for the entire region, we had multiple patches within that region for each country, one patch was a country. Actually, we are able to have a much higher resolution than this – data allowing.

The next step on from producing this model structure and applying it to the Asia-Pacific region is to zoom in. We are working with the malaria control programme in Cambodia to have a much more detailed dataset at the national level. We are able to use this dataset to produce a model that is a multi-patch model, again a spatially explicit model, but for Cambodia itself with the patches being provinces within Cambodia.

The purpose of doing this would be purely for the national control programme decision makers to take the finished item, the completed model, and run through a series of scenarios that they would like to explore in a simulation environment before they do it for real for their malaria elimination strategy.

I think, for my particular field of Epidemiological Modelling the most important development is the link between mathematical and economic modelling and policymaking. This link has been growing as the field is becoming more and more mature. I believe there is a lot of impact, the potential to save lives and to maximise the impact of public health interventions by using mathematical models in this way, but only if policy makers are partners with the researchers.

I think my line of research matters. Although there are many ways to approach the control of infectious diseases and also to approach the scientific enquiry of tropical disease, what mathematical modelling can offer is an evidence-based logical framework for thinking about these questions and for approaching the problem in a methodical and optimal way. I also believe that a good mathematical and/or economic modeller within a team or within an institution will probably save more money than is invested in them because of the cost savings that they can achieve through this kind of work.

Mathematical modelling and especially if economic modelling is included as well provides a bridge between basic science research and policy. The way we can work with basic scientists and clinical researchers is that when we have results from the lab or from the field we can place these results into the context of the potential rollout at population level using a mathematical model and predict the impact of research. I would say it is a very useful tool to have for translational research or translating research into policy.

This interview was recorded in February 2019

Lisa White

Professor Lisa White heads the Mathematical and Economic Modelling (MAEMOD) group based in Bangkok at MORU, with a research focus on tropical infections, primarily malaria. MAEMOD develops mathematical and economic models used for malaria elimination strategy in the Greater Mekong Sub-Region. MAEMOD also coordinates an international network of infectious disease modellers working in the tropics.

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