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. Overall, the project has three main objectives: (i) selection of informative antibody in multiplex data using machine learning techniques, (ii) extending existing threshold-free methodology to a multivariate setting, and (iii) study of the performance of multivariate serological outcomes in the context of disease pre-elimination elimination. 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.

Where does the project lie on the Translational Pathway?

T1 – Basic Research

Expected Outputs

The project is expected to generate at least 3 papers in an international refereed epidemiology and public health journal. Statistical software of the newly developed methods will be made publicly available in a dedicated GitHub repository.

Training Opportunities

The student will be expected to gain specialized statistical training as part of the MSc in Health Data Science delivered at Lancaster University. In addition, the student is entitled to participate in the post-graduate course e in Computational Biomedicine using R, which runs every year by the second supervisor in Warsaw University of Technology.  

Skills Required


Key Publications associated with this project

Sepúlveda, N., Stresman, G., White, M. T., & Drakeley, C. J. (2015). Current Mathematical Models for Analyzing Anti-Malarial Antibody Data with an Eye to Malaria Elimination and Eradication. Journal of immunology research, 2015, 738030. doi: 10.1155/2015/738030

van den Hoogen, L. L., Bareng, P., Alves, J., Reyes, R., Macalinao, M., Rodrigues, J. M., Fernandes, J. M., Goméz, L. F., Hall, T., Singh, S. K., Fornace, K., Luchavez, J., Kitchen, A., Chiodini, P., Espino, F., Tetteh, K., Stresman, G., Sepúlveda, N., & Drakeley, C. (2020). Comparison of Commercial ELISA Kits to Confirm the Absence of Transmission in Malaria Elimination Settings. Frontiers in public health, 8, 480.

Cunha, M. G., Silva, E. S., Sepúlveda, N., Costa, S. P., Saboia, T. C., Guerreiro, J. F., Póvoa, M. M., Corran, P. H., Riley, E., & Drakeley, C. J. (2014). Serologically defined variations in malaria endemicity in Pará state, Brazil. PloS one, 9(11), e113357.

Migchelsen, S. J., Sepúlveda, N., Martin, D. L., Cooley, G., Gwyn, S., Pickering, H., Joof, H., Makalo, P., Bailey, R., Burr, S. E., Mabey, D., Solomon, A. W., & Roberts, C. H. (2017). Serology reflects a decline in the prevalence of trachoma in two regions of The Gambia. Scientific reports, 7(1), 15040. Doi: 10.1038/s41598-017-15056-7

Kyomuhangi, I., Giorgi, E. (2021) A unified and flexible modelling framework for the analysis of malaria serology data. Epidemiology & Infection.  doi:10.1017/S0950268821000753


Now Accepting Applications 

CLOSING DATE FOR APPLICATIONS: Application Portal closes: Wednesday 9th February 2022 (12:00 noon UK time)

Shortlisting complete by: End Feb/early March 2022

Interviews by: Late March/early April 2022

For more information on Eligibility, funding and how to apply please visit the MRC DTP/CASE pages