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Modelling to quantify the likelihood that local elimination of transmission has occurred using routine gambiense human African trypanosomiasis surveillance data
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Davis, Christopher N., Castano, Maria Soledad, Aliee, Maryam, Patel, Swati, Miaka, Erick Mwamba, Keeling, Matthew James, Spencer, Simon E. F., Chitnis, Nakul and Rock, Kat S. (2021) Modelling to quantify the likelihood that local elimination of transmission has occurred using routine gambiense human African trypanosomiasis surveillance data. Clinical Infectious Diseases . ciab190. doi:10.1093/cid/ciab190
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WRAP-Modelling-quantify-elimination-transmission-routine-gambiense-human-African-trypanosomiasis-2021.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (710Kb) | Preview |
Official URL: https://doi.org/10.1093/cid/ciab190
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 three 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 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), and 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 modelling 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.
| Item Type: | Journal Article | |||||||||
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| Subjects: | Q Science > QA Mathematics R Medicine > RC Internal medicine |
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| Divisions: | Faculty of Science > Life Sciences (2010- ) Faculty of Science > Mathematics |
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| Library of Congress Subject Headings (LCSH): | African trypanosomiasis -- Mathematical models, African trypanosomiasis -- Epidemiology, African trypanosomiasis -- Congo (Democratic Republic), African trypanosomiasis -- Prevention, Population Surveillance -- Methods | |||||||||
| Journal or Publication Title: | Clinical Infectious Diseases | |||||||||
| Publisher: | University of Chicago Press | |||||||||
| ISSN: | 1058-4838 | |||||||||
| Official Date: | 27 April 2021 | |||||||||
| Dates: |
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| Article Number: | ciab190 | |||||||||
| DOI: | 10.1093/cid/ciab190 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Publisher Statement: | This is a pre-copyedited, author-produced version of an article accepted for publication in Clinical Infectious Diseases following peer review. The version of record Christopher N Davis, Maria Soledad Castaño, Maryam Aliee, Swati Patel, Erick Mwamba Miaka, Matt J Keeling, Simon E F Spencer, Nakul Chitnis, Kat S Rock, Modelling to quantify the likelihood that local elimination of transmission has occurred using routine gambiense human African trypanosomiasis surveillance data, Clinical Infectious Diseases, 2021;, ciab190 is available online at: https://doi.org/10.1093/cid/ciab190 | |||||||||
| Access rights to Published version: | Open Access | |||||||||
| Funder: | Bill and Melinda Gates Foundation | |||||||||
| RIOXX Funder/Project Grant: |
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