Modelling to Quantify the Likelihood that Local Elimination of Transmission has Occurred Using Routine <i>Gambiense</i> Human African Trypanosomiasis Surveillance Data

Abstract Background The gambiense human African trypanosomiasis (gHAT) elimination programme in the Democratic Republic of Congo (DRC) routinely collects case data through passive surveillance and active screening, with several regions reporting no cases for years, despite being endemic early 2000s. Methods We use mathematical models fitted to longitudinal estimate probability that selected administrative have already achieved transmission (EOT) gHAT. examine impact screening coverage on certainty model estimates therefore role measurement EOT. Results In 3 example health zones Sud-Ubangi province, we find there is a moderate (&amp;gt;40%) EOT has been by 2018, based 2000–2016 data. Budjala Mbaya reported zero during 2017–18, this further increases our respective 99.9% 99.6% (model S) 87.3% 92.1% W). Bominenge had recent reporting, however, if were found 2021, it would substantially raise met (99.0% S 88.5% W); could be higher 50% year (99.1% 94.0% Conclusions demonstrate how routine coupled mechanistic modeling can likelihood achieved. Such quantitative assessment will become increasingly important measuring local achievement as 2030 approaches.