Quantifying epidemiological drivers of gambiense human African Trypanosomiasis across the Democratic Republic of Congo.
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...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1008532 |
| _version_ | 1818348789514108928 |
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| author | Ronald E Crump Ching-I Huang Edward S Knock Simon E F Spencer Paul E Brown Erick Mwamba Miaka Chansy Shampa Matt J Keeling Kat S Rock |
| author_facet | Ronald E Crump Ching-I Huang Edward S Knock Simon E F Spencer Paul E Brown Erick Mwamba Miaka Chansy Shampa Matt J Keeling Kat S Rock |
| author_sort | Ronald E Crump |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T17:55:38Z |
| format | Article |
| id | doaj.art-b71cc67d339e4bc68887a64c672fe2d6 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-13T17:55:38Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-b71cc67d339e4bc68887a64c672fe2d62022-12-21T23:36:23ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582021-01-01171e100853210.1371/journal.pcbi.1008532Quantifying epidemiological drivers of gambiense human African Trypanosomiasis across the Democratic Republic of Congo.Ronald E CrumpChing-I HuangEdward S KnockSimon E F SpencerPaul E BrownErick Mwamba MiakaChansy ShampaMatt J KeelingKat S RockGambiense 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.https://doi.org/10.1371/journal.pcbi.1008532 |
| spellingShingle | Ronald E Crump Ching-I Huang Edward S Knock Simon E F Spencer Paul E Brown Erick Mwamba Miaka Chansy Shampa Matt J Keeling Kat S Rock Quantifying epidemiological drivers of gambiense human African Trypanosomiasis across the Democratic Republic of Congo. PLoS Computational Biology |
| title | Quantifying epidemiological drivers of gambiense human African Trypanosomiasis across the Democratic Republic of Congo. |
| title_full | Quantifying epidemiological drivers of gambiense human African Trypanosomiasis across the Democratic Republic of Congo. |
| title_fullStr | Quantifying epidemiological drivers of gambiense human African Trypanosomiasis across the Democratic Republic of Congo. |
| title_full_unstemmed | Quantifying epidemiological drivers of gambiense human African Trypanosomiasis across the Democratic Republic of Congo. |
| title_short | Quantifying epidemiological drivers of gambiense human African Trypanosomiasis across the Democratic Republic of Congo. |
| title_sort | quantifying epidemiological drivers of gambiense human african trypanosomiasis across the democratic republic of congo |
| url | https://doi.org/10.1371/journal.pcbi.1008532 |
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