Modelling to Quantify the Likelihood that Local Elimination of Transmission has Occurred Using Routine Gambiense Human African Trypanosomiasis Surveillance Data. (14th June 2021)

• Record Type: Journal Article
• Authors: Davis, Christopher N; Castaño, María Soledad; Aliee, Maryam; Patel, Swati; Miaka, Erick Mwamba; Keeling, Matt J; Spencer, Simon E F; Chitnis, Nakul; Rock, Kat S
• Is Part Of: Clinical infectious diseases. Volume 72(2021)Supplement 3
• Page Start: S146
• Page End: S151
• Publication Date: 2021-06-14
• Subjects: gambiense human African trypanosomiasis (gHAT) -- modeling -- elimination of transmission -- surveillance; Communicable diseases -- Periodicals; 616.905
• DOI: 10.1093/cid/ciab190
• Languages: English
• ISSNs: 1058-4838

Abstract

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 several years, despite being endemic in the early 2000s. Methods: We use mathematical models fitted to longitudinal data to estimate the probability that selected administrative regions have already achieved elimination of transmission (EOT) of gHAT. We examine the impact of active screening coverage on the certainty of model estimates for transmission and therefore the role of screening in the measurement of EOT. Results: In 3 example health zones of Sud-Ubangi province, we find there is a moderate (>40%) probability that EOT has been achieved by 2018, based on 2000–2016 data. Budjala and Mbaya reported zero cases during 2017–18, and this further increases our respective estimates to 99.9% and 99.6% (model S) and to 87.3% and 92.1% (model W). Bominenge had recent case reporting, however, that if zero cases were found in 2021, it would substantially raise our certainty that EOT has been met there (99.0% for model S and 88.5% for model W); this could be higher with 50% coverage screening that year (99.1% for model S and 94.0% for model W). Conclusions: We demonstrate how routine surveillance data coupled with mechanistic modeling can estimate the likelihood that EOT has already been achieved. Such quantitative assessment will become increasingly important for measuring local achievement of EOT as 2030 approaches.