Spatio-temporal dynamics of drug-resistant Plasmodium falciparum malaria in Africa: implications for public health

Malaria is a parasite which can cause severe illness and death in humans if they do not receive effective treatment. In the 1990s, parasites resistant to malaria drugs spread from Asia throughout the African continent, causing mortality from the disease to double or triple in many areas. New, effective artemisinin therapies are now used across the world. However, recently parasites in South-East Asia have developed resistance to these new treatments and are highly likely to spread to Africa once again.

Dr Okell, a researcher based in the Department of Infectious Disease Epidemiology at Imperial College London, will develop a computer simulation to describe how resistant strains spread across Africa over time, based on extensive analysis of historical data on resistance. Through understanding what factors contributed towards or prevented the spread of drug resistance in the past, the research will identify which control strategies could be the most effective against the new resistant parasites. For example, national control programs could improve the accuracy of diagnosis to reduce the overuse of treatment, or introduce better transmission control. As more than 80% of global malaria cases (200 million) occur in Africa, successful control of drug resistance will have a huge public health impact.

Background

In the 1990s, the spread of drug-resistant Plasmodium falciparum malaria strains in Africa after importation from South-East Asia led to a substantial increase in malaria mortality. New first-line antimalarial treatments, artemisinin combination therapies (ACTs), are currently efficacious in Africa but artemisinin-resistant strains have recently emerged in South-East Asia. Artemisinin resistance is likely to spread to Africa where more than 80% of global malaria cases currently occur. Given the lack of affordable alternative antimalarials, understanding how resistance spreads in Africa and how it can be contained is a key public health question. An important first step is to characterize the spread of resistance to sulphadoxine-pyrimethamine (SP), the previous first line treatment. Molecular analysis has made it possible to measure the spread of parasite lineages resistant to SP across Africa over time. Mathematical models are powerful tools for analysis of such data. The aim of this project is to use models to identify causal factors behind the pattern of SP resistance spread, indicate likely dispersal routes of artemisinin resistance for monitoring purposes and compare proposed control strategies.



Proposed work

The first objective of this project is to develop a spatially-structured transmission model to describe the spread of SP-resistant genotypes over time across Africa. Factors likely to determine the rate of spread of a resistant strain will be incorporated, including the usage of the antimalarial, the level of transmission intensity, the evolutionary fitness of the resistant strain relative to competing wild-types, and pharmacokinetics of the drug. Available administrative area-level data on antimalarial use and transmission intensity will be used in a metapopulation model framework. Furthermore the role of human travel in transferring resistant strains from Asia to Africa and between areas within Africa will be investigated using gravity models and migration data. The model will be calibrated against geo-referenced data on the prevalence of SP resistance mutations. The next main objective is to adapt the fully-developed model to characterize the potential spatio-temporal spread of artemisinin-resistant parasites under different scenarios and identify key countries at risk where artemisinin resistance should be monitored. Proposed policies to contain this spread will be modelled and contrasted, including vector control, preventing overuse of treatment, introducing multiple first-line therapies and preventing artemisinin monotherapy use. Such models could be key to informing policy given the public health urgency and lack of resources to empirically test the full range of control options across different endemic areas.