Table 2. Immediate priorities for modelling for gambiense human African trypanosomiasis (gHAT).

Priority issue / question identified by WHO during this meeting	How can modelling address this?
Probability of interrupted transmission: Can existing mathematical models be used to define the probability of interruption of gHAT transmission in regions where no cases have been detected?	Using historic data, and assumptions on current passive surveillance, models can be generated that capture the observed dynamics at regional foci and calculate the probability (positive predictive value, PPV) of interrupted transmission given that no cases have been reported for different periods of time.
Reactive screening: How does a reactive screening strategy compare to active screening and passive detection, or passive detection alone in terms of: - reduction of transmission and associated timescales? - case reporting?	Modelers can develop/refine modelling of current active and passive strategies to simulate a reactive screening strategy. - The spatial scale considered will impact results. - Reactive strategies can and should be included in cost predictions and cost- effectiveness analyses
Animal reservoir: - What do we know about their role in transmitting disease? - How could an animal reservoir affect the 2030 target?	Some modelling has already explored possible animal reservoirs. Modelers can continue to explore: - Whether there are signals of animal reservoirs by assessing human case data alone - If there is any support for these models, to assess the relative contribution of animals to transmission, and what impact this could have on timescales to achieve EOT - To include animals in a village-scale model framework (to assess PPV of zero case detections in active screening on EOT) - To make estimates more robust by fitting to human and animal data (if available) - To assess implications of animal reservoirs in decision analyses between interventions
Asymptomatics: - Can we estimate the potential number of asymptomatic infections? E.g. for one detected case, how many remain undetected? - How likely are asymptomatics to infect others? - What do we know about their role in (maintaining) transmission?	- Existing modelling frameworks can be adapted to include potential asymptomatics (including self-cure or skin infections) - Sensitivity analysis and/or matching to data (if available) could estimate possible numbers of asymptomatics, their relative contribution to transmission, infection timescales, and relative infectivity. Lack of data may lead to large confidence intervals - Modellers can evaluate the effectiveness of different strategy types in models with and without asymptomatic people - e.g. would we select the same intervention strategy if asymptomatics play a substantial role in transmission?
Spatial prediction: Support defining areas that should be screened, where there is potential of transmission. Similarly, can we rule out certain areas?	- A tsetse absence model could be used to assess regions which are unlikely to have gHAT due to unsuitable habitat. - This can be used to explore the joint distribution of the active and passive surveillance data and to look for factors/variables which could predict the underlying variation and probability of reporting. - It may be possible to include a range of factors into these predictions including changing population distribution and land-use.