Assessing the impact of data aggregation in model predictions of HAT transmission and control activities

Since the turn of the century, the global community has made great progress towards the elimination of gambiense human African trypanosomiasis (HAT). Elimination programs, primarily relying on screening and treatment campaigns, have also created a rich database of HAT epidemiology. Mathematical models calibrated with these data can help to fill remaining gaps in our understanding of HAT transmission dynamics, including key operational research questions such as whether integrating vector control with current intervention strategies is needed to achieve HAT elimination. Here we explore, via an ensemble of models and simulation studies, which aspects of the available data and level of data aggregation, such as separation by disease stage, would be most useful for better understanding transmission dynamics and improving model reliability in making future predictions of control and elimination strategies. Author summary Human African tryposonomiasis (HAT), also known as sleeping sickness, is a parasitic disease with over 65 million people estimated to be living at risk of infection. Sleeping sickness consists of two stages: the first one is relatively mild but the second stage is usually fatal if untreated. The World Health Organization has targeted HAT for elimination as a public health problem by 2020 and for elimination of transmission by 2030. Regular monitoring updates indicate that 2020 elimination goals are likely to be achieved. This monitoring relies mainly on case report data that is collected through medical-based control activities — the main strategy employed so far in HAT control. This epidemiological data are also used to calibrate mathematical models that can be used to analyse current interventions and provide projections of potential intensified strategies. We investigated the role of the type and level of aggregation of this HAT case data on model calibrations and projections. We highlight that the lack of detailed epidemiological information, such as missing stage of disease or truncated time series data, impacts model recommendations for strategy choice: it can misrepresent the underlying HAT epidemiology (for example, the ratio of stage 1 to stage 2 cases) and increase uncertainty in predictions. Consistently including new data from control activities as well as enriching it through cross-sectional (e.g. demographic or behavioural data) and geo-located data is likely to improve modelling accuracy to support planning, monitoring and adapting HAT interventions. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was supported by the Bill and Melinda Gates Foundation (https://www.gatesfoundation.org/) in partnership with the Task Force for Global Health (https://www.taskforce.org/) through the NTD Modelling Consortium (OPP1053230, OPP1156227) to MSC, MLNM, KSR, EK, SEFS, AG, MJK and NC. KSR and MJK also were supported by BMGF though the HAT Modelling and Economic Predictions for Policy project (OPP1177824). ST was supported by the Biotechnology and Biological Sciences Research Council (BB/L019035/1) and BMGF (OPP1154033). The views, opinions, assumptions or any other information set out in this article are solely those of the authors and should not be attributed to the funders or any person connected with the funders. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ### Author Declarations All relevant ethical guidelines have been followed and any necessary IRB and/or ethics committee approvals have been obtained. Not Applicable All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Not Applicable Any clinical trials involved have been registered with an ICMJE-approved registry such as ClinicalTrials.gov and the trial ID is included in the manuscript. Not Applicable I have followed all appropriate research reporting guidelines and uploaded the relevant Equator, ICMJE or other checklist(s) as supplementary files, if applicable. Yes All data is available in the supplementary information.