Mathematical modelling for tropical diseases

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.

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Lisa is a mathematical modeller with a focus on global health and policymaking. Her work on combines within and between host infection models with multi-strain/species modelling to consider the characterisation, emergence and spread of antimicrobial drug resistance and its containment. Her modelling work is participatory, with models for malaria and other diseases being developed in close collaboration with national control programs, international decision-makers, funders and donors.

Using mathematical and economic modelling to support integrated strategies for multiple diseases is a new developing theme in Lisa’s new group, the Oxford Modelling for Global Health group based at the Big Data Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford.

Lisa is an enthusiastic advocate of increasing modelling capacity in countries where the discipline is absent or in its early stages with the vision of equitable global collaborations with more established groups. To this end Lisa is developing a new MSc programme at the Nuffield Department of Medicine entitled “Modelling for Global Health” to train future cohorts of global health modellers.

A consortium of international modellers from over ten countries spanning four continents was initiated by Lisa in March 2020 to address urgent in-country requests for modelling support during the global covid-19 pandemic. The consortium focusses on supporting low- and middle-income countries by providing in-country modellers with support, shared resources and a forum for sharing innovation. This was made possible as a result of the extensive training and mentoring Lisa has provided over the preceding decade.