Bridging the Gap from Molecular Surveillance to Programmatic Decisions for Malaria Control and Elimination

Am J Trop Med Hyg. 2023 Dec 26:tpmd220749. doi: 10.4269/ajtmh.22-0749. Online ahead of print.

Abstract

An increasing number of molecular and genomic assays are available to study malaria parasite populations. However, so far they have played a marginal role in informing policy and programmatic decision-making. Currently, molecular data are mainly used for monitoring drug efficacy against Plasmodium falciparum; assessing molecular markers of drug and insecticide resistance; and assessing P. falciparum histidine-rich protein 2 and 3 genes (Pfhrp2/3) deletion. We argue that additional use cases for molecular routine surveillance could be implemented in the near future, especially in transmission settings approaching elimination. These would include using quantitative polymerase chain reaction to monitor the prevalence of sub-patent infections in asymptomatic carriers, monitoring parasite genetic diversity as transmission intensity is changing, using genomic data to determine the origin of imported infections and characterize transmission chains in settings with very low malaria transmission, and using serology to monitor recent and past exposures in low-transmission settings. Molecular surveillance could inform control programs on adapting novel strategies, such as reactive case detection or focal mass drug administration, and help evaluate the impact of interventions currently in place. To better integrate molecular and genomic data into control program decision-making, engagement of national malaria control experts is crucial. Local laboratory capacity needs to be strengthened, shortening the time from sample collection to data availability. Here, we discuss opportunities and challenges of the use of molecular and genomic data for supporting malaria control and elimination efforts, as well as the avenues to link molecular and genomic data with gold standard epidemiological measurements through mathematical modeling.