Surveilling Malaria through machine learning and clustering tools in pregnancy (PregMal)

Objectives

The PregMal project proposes to develop and apply novel statistical tools (adapted from the field of cosmology) to test an innovative and cost-efficient surveillance approach based on the strategic use of parasitological, serological, and genomic data from easy-access pregnant women at antenatal care (ANC) clinics.

The application of these new tools on data obtained from pregnant women can suppose an enormous breakthrough for sustainable and actionable surveillance systems that can accelerate efforts toward malaria elimination. With these newly developed tools, the project will:

1. Assess the potential of parasitological and serological data from pregnant women at first ANC visit as a source of reliable data to reflect temporal and spatial malaria trends in the community
2. Compare genetic metrics in the parasite population of pregnant women and the overall community that can inform about changes of malaria transmission, clustering of infections, and parasite importation