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Roll Back Malaria Progress & Impact Series

Mathematical Modelling to Support Malaria Control and Elimination

Mathematical Modelling to Support Malaria Control and Elimination
Photo © Bonnie Gillespie/
Johns Hopkins University

Mathematical Modelling to Support Malaria Control and Elimination, the fifth report of the Roll Back Malaria Progress & Impact Series, provides an overview of mathematical modelling, explains its history in relation to epidemiology and malaria, and details its implications and uses for global and national malaria control and elimination planning. The report aims to expand the dialogue within the global malaria community - and among public health decisionmakers in particular - on when and how mathematical modelling can help inform malaria control programmes and policies.

A summary of key messages

Using modelling to inform decision-making
Mathematical modelling uses computer-based models to describe, explain, or predict behaviour or phenomena in the real world. It is particularly useful in investigating questions or testing ideas within complex systems. For this reason, modelling can be especially helpful in informing decision-making in global malaria control and eradication efforts because they involve extensive changes to a complex web of interconnected biological systems. Establishing optimal policies and programmes to support these efforts is complicated by the potential for parasites and vectors to evolve, the waxing and waning of human immunity, behavioural changes in human and vector populations, and interactions among large numbers of heterogeneous sub-populations of the organisms involved.

As countries scale up malaria control efforts and reach high intervention coverage targets, they are faced with the question of what to do next. The strategy for maintaining and enhancing the achieved reductions in transmission is not obvious. It is often not clear whether maintaining current coverage levels would continue to reduce transmission, stabilize transmission at a new level, or slowly give way to an increase in transmission. Mathematical modelling can build on available data, test multiple scenarios and combinations of intervention strategies, and make verifiable predictions on what can be expected from these strategies.
Box 1 offers a practical example of how mathematical modelling can be applied in a country context.

To produce relevant, robust findings, mathematical modelling should at the outset involve partnership and good communication between technical experts in mathematical modelling, experts in malaria field and laboratory science, and health policy decision-makers. Models produce the most useful data when they are formulated with important biological, economic, and practical realities in mind and when their results are interpreted with care, making them another useful tool in the fight against malaria.

Related materials

Mathematical Modelling to Support Malaria Control and Elimination

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