Statistical methods for the analysis of multi-antibody data to inform malaria control and elimination strategies

The potential of antibody data to inform disease control and surveillance is being increasingly recognised. Influenza, trachoma, lymphatic filariasis and malaria are examples of infectious diseases where sero-surveillance is actively performed. As a consequence, the creation of a World Serum Bank has been advocated to facilitate the next generation of sero-surveillance tools. In the last decade, the relevance of sero-surveillance for malaria has increased due to reductions in malaria transmission evidenced by decreasing numbers of malaria deaths and cases. In low transmission settings, the uncertainty in the estimates of conventional malaria metrics aimed at detecting the presence of infection in humans or mosquitoes, can increase significantly. In addition, this issue is exacerbated by the fact these metrics are strongly affected by the sampling frame and the seasonality of malaria transmission. Serological studies overcome such limitations, because they aim to quantify exposure rather than infection. As a result, serological assessment is now being considered by the World Organization (WHO) in their guidelines for malaria elimination. The prevailing practice in sero-epidemiological analyses is to estimate malaria transmission intensity treating the data from multiple antibody responses independently. This project will focus on the development of multivariate statistical methods that overcome the limitations of this approach to fully borrow the strength of information across multi-antibody data. The developed statistical methods in this project will also be deployed to other infectious diseases (e.g., COVID-19), where serological assessment is also a priority in their control and elimination strategies.

Surveillance

Oct 2022 — Oct 2026

United Kingdom