Genomic data are increasingly being used to provide malaria control programmes with actionable information that can help guide control efforts. By using genetic data to uniquely identify parasites it is possible to determine which infections were imported vs. locally acquired, what level of spatial structure exists in the parasite population, if there are key sources and sinks of transmission, and even whether transmission is declining or rebounding. Effective genomic surveillance requires a long chain of analysis going all the way from blood spots in the field to DNA extraction and sequencing in the lab and finally to bioinformatics and downstream analysis on computer. Working with researchers in the IDEEL network and beyond, we are streamlining this analysis pipeline to provide novel tools and approaches to help malaria control programmes get the most value out of genomic data.
One area where genomic data can add value is in questions related to parasite movement over space. Here traditional surveillance methods such as travel history surveys can be supplemented with genomic data to better visualise the links that exist between populations. Blocks of genetic similarity tend to be broken up by recombination over short timescales, meaning parasites that share long blocks of common ancestry are likely to be closely linked infections – for example one infection being the parent of the other. Using genetic data from the Democratic Republic of the Congo we were able to use this signal to identify links within the country that represent important routes of migration, sometimes over many hundreds of kilometres. We are now developing more sophisticated methods to dig deeper into the data, for example establishing whether road distance or air travel can most easily explain this signal.
Verity, Robert, et al. "The impact of antimalarial resistance on the genetic structure of Plasmodium falciparum in the DRC." Nature communications 11.1 (2020): 1-10.
Wesolowski, Amy, et al. "Mapping malaria by combining parasite genomic and epidemiologic data." BMC medicine 16.1 (2018): 1-8.
Watson, Oliver J., et al. "Evaluating the Performance of Malaria Genetics for Inferring Changes in Transmission Intensity Using Transmission Modeling." Molecular biology and evolution 38.1 (2021): 274-289.
Moser, Kara A., et al. "Describing the current status of Plasmodium falciparum population structure and drug resistance within mainland Tanzania using molecular inversion probes." Molecular Ecology 30.1 (2021): 100-113.