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Quantifying epidemiological drivers of gambiense human African Trypanosomiasis across the Democratic Republic of Congo
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Crump, Ronald E., Huang, Ching-I, Knock, Edward, Spencer, Simon E. F., Brown, Paul E., Mwamba Miaka, Erick, Shampa, Chansy, Keeling, Matthew James and Rock, Kat S. (2021) Quantifying epidemiological drivers of gambiense human African Trypanosomiasis across the Democratic Republic of Congo. PLoS Computational Biology, 17 (1). e1008532. doi:10.1371/journal.pcbi.1008532
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Official URL: https://doi.org/10.1371/journal.pcbi.1008532
Abstract
Gambiense human African trypanosomiasis (gHAT) is a virulent disease declining in burden but still endemic in West and Central Africa. Although it is targeted for elimination of transmission by 2030, there remain numerous questions about the drivers of infection and how these vary geographically. In this study we focus on the Democratic Republic of Congo (DRC), which accounted for 84% of the global case burden in 2016, to explore changes in transmission across the country and elucidate factors which may have contributed to the persistence of disease or success of interventions in different regions. We present a Bayesian fitting methodology, applied to 168 endemic health zones ( 100,000 population size), which allows for calibration of a mechanistic gHAT model to case data (from the World Health Organization HAT Atlas) in an adaptive and automated framework. It was found that the model needed to capture improvements in passive detection to match observed trends in the data within former Bandundu and Bas Congo provinces indicating these regions have substantially reduced time to detection. Health zones in these provinces generally had longer burn-in periods during fitting due to additional model parameters. Posterior probability distributions were found for a range of fitted parameters in each health zone; these included the basic reproduction number estimates for pre-1998 (R0) which was inferred to be between 1 and 1.14, in line with previous gHAT estimates, with higher median values typically in health zones with more case reporting in the 2000s. Previously, it was not clear whether a fall in active case finding in the period contributed to the declining case numbers. The modelling here accounts for variable screening and suggests that underlying transmission has also reduced greatly { on average 96% in former Equateur, 93% in former Bas Congo and 89% in former Bandundu { Equateur and Bandundu having had the highest case burdens in 2000. This analysis also sets out a framework to enable future predictions for the country.
| Item Type: | Journal Article | ||||||
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| Subjects: | R Medicine > RC Internal medicine | ||||||
| Divisions: | Faculty of Science > Life Sciences (2010- ) Faculty of Science > Mathematics Faculty of Science > Statistics Faculty of Science > Centre for Systems Biology |
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| Library of Congress Subject Headings (LCSH): | African trypanosomiasis , African trypanosomiasis -- Congo (Democratic Republic), African trypanosomiasis -- Transmission -- Mathematical models -- Congo (Democratic Republic) | ||||||
| Journal or Publication Title: | PLoS Computational Biology | ||||||
| Publisher: | Public Library of Science | ||||||
| ISSN: | 1553-7358 | ||||||
| Official Date: | 29 January 2021 | ||||||
| Dates: |
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| Volume: | 17 | ||||||
| Number: | 1 | ||||||
| Article Number: | e1008532 | ||||||
| DOI: | 10.1371/journal.pcbi.1008532 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access | ||||||
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