Members of SBIDER are actively involved in multiple projects seeking to predict and help control of Neglected Tropical Diseases (NTDs). These diseases affect the world's poorest billion, and therefore mathematical models and new methods of efficient control are required. Our work feeds into a variety of policy recommendations, including the WHO's 2030 goals for elimination of many of these infections as a public health problem.
Our work on NTDs focuses on six main areas:
- Human African Schistosomiasis (HAT) which causes sleeping sickness. Work is funded by the Bill & Melinda Gates Foundation through the NTD modelling consortium and the HATMEPP project.
- Visceral Leishmaniasis where research funded by the Wellcome Trust is examining the role of dogs as a reservoir of infection in Brazil, and how insecticide treatment can lower the incidence in humans.
- Rabies in South East Asia, where interdisciplinary research using mobile phone apps to guide integrated bite management is proving highly effective.
- Leprosy, using annual data on detected cases together with detailed epidemiological knowledge to predict the number of undetected cases of leprosy in the population.
- Schistosomiasis, caused by a parasitic flatworm. Research has used predictive models to assess the potential cost-effectiveness of vaccination, and hence whether vaccine candidates should be taken to stage 2 and 3 trials.
- Effective vaccination against Plague and Rift Valley Fever. This work, funded by NIHR, will develop model and estimate how best to deploy vaccines against these two infections.
Mathematical modelling for infectious diseases and public health works best through collaborations with epidemiologists, policy makers and field experts. Multiple modelling groups working on the same question allows us to investigate model structure uncertainty – the importance of particular underlying assumptions of each model type. The need for structures that connect modellers and users. Designing and parameterising models raises questions about underlying biology, data availability and implementation policy structures. Conversations between these different sectors will help identify the need for studies to address key uncertainties. NTD modelling lags behind modelling for other infectious diseases and it needs to be able to access the huge advances in the broader modelling field. With the right funding framework and the right relationship between modellers and users, new insight into NTDs will be possible.