PregMal · Surveilling Malaria through machine learning and clustering tools in pregnancy
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2021-04-01 → 2023-12-21
- EU contribution
- €172,932
- Participants
- 1
- Scheme
- MSCA-IF
Lines connect the coordinator with its partners.
Results in brief
Surveilling Malaria through machine learning and clustering tools in pregnancy
Malaria still represents a humanitarian thread that has become a high priority in the World Health Organization (WHO) and European Union (EU) directives. Robust malaria surveillance is key for an optimal and equitable allocation of interventions, but estimating malaria trends from current surveillance systems remains unreliable and expensive. Pregnant women represent a promising convenient group for sentinel surveillance. They are of easy access at antenatal care (ANC) visits, and their high attendance provide a good representation of the population. They can inform about asympomatic infections, providing a timely monitoring and a representative geographical distribution of the population. However, few studies analysed the consistency of malaria burden levels between pregnant women and the community. The overarching goal of the project is to assess the potential of pregnant women at maternal health care clinics for providing precise and effective actionable information for malaria control and elimination. The specific objectives are three: 1. To develop new statistical tools to better characterise spatial clustering, temporal trends of Pf transmission and genetic differentiation of the parasites. 2. To 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 Pf trends in the community. 3. To compare genetic metrics in the parasite population collected from pregnant women and from the overall community that can inform about changes of Pf transmission, clustering of infections and parasite importation.
Data: CORDIS, © European Union
Project objective
The vision of the World Health Organisation (WHO) for 2030 is a world free of malaria. For this, agile and robust malaria surveillance systems are required to efficiently guide actions towards interruption of transmission. Estimating malaria trends from passive detection of clinical malaria cases at health facilities or from cross-sectional surveys remains difficult and expensive. Pregnant women represent a promising convenience group for malaria surveillance, providing a representative section of the overall population in a cost-efficient and sustainable manner. Serological and molecular surveillance has also become a potential key approach to guide elimination efforts, providing information about the history of exposure, the geographic origin (malaria importation) and the intensity of malaria transmission. Here we propose 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 towards malaria elimination. With these new developed tools I will a) 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 and b) 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.
Original text from CORDIS.
Participants
- FUNDACION PRIVADA INSTITUTO DE SALUD GLOBAL BARCELONA · BarcelonaCoordinatorSpain
Links
Data: CORDIS, © European Union