Beyond mass drug administration: understanding Schistosomiasis dynamics to STOP transmission

Neglected tropical diseases (NTDs) are a diverse group of infections which are especially prevalent in low-income populations in tropical and subtropical areas in Africa, Asia and the Americas. Schistosomiasis is the deadliest NTD killing an estimated 280,000 people each year in the African region alone.

The World Health Organization (WHO) 2020 target for schistosomiasis is to reduce the prevalence of heavy-intensity infections to ≤5% among school-aged children through Mass Drug Administration (MDA).

However, in areas of moderate and high prevalence the goal seems unlikely to be met as even with intensive biannual MDA, prevalence of infections are still seen to rebound rapidly after each round. The reasons are still poorly understood.

Mathematical models and statistical methods have proven to be essential to gain insights into the complex processes underlying the transmission dynamics of infectious diseases.

In this research project, I will develop a stochastic individual-based model (IBM) to simulate schistosomiasis transmission integrating cutting-edge mathematical models and statistical methods with recent advances in parasite genetics, epidemiology, diagnostics and immunology.

This project aims to advance our current understanding of schistosomiasis transmission dynamics investigating the mechanisms that cause the rapid rebound of prevalence after MDA and proposing optimal implementation of current and novel strategies to achieve schistosomiasis control and move towards elimination.

In addition, this project will build a reference methodological framework to comprehensively study other MDA targeted infectious diseases.

The proposed project addresses one of the priorities of the EU, through global poverty reduction (SDG goal 1) by promoting ways of improving future health (SDG goal 3).This project will be carried out at Erasmus MC with Prof. Sake J. de Vlas and at University of Glasgow (secondment) with Dr. Poppy Lamberton.