Quantifying epidemiological drivers of gambiense human African Trypanosomiasis across the Democratic Republic of Congo.
PLoS Comput Biol. 2021 01; 17(1):e1008532.PC

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.

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Authors+Show Affiliations

Crump RE

Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom. Mathematics Institute, The University of Warwick, Coventry, United Kingdom. The School of Life Sciences, The University of Warwick, Coventry, United Kingdom.

Huang CI

Knock ES

Zeeman Institute for System Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom. The Department of Statistics, The University of Warwick, Coventry, United Kingdom.

Spencer SEF

Brown PE

Mwamba Miaka E

Programme National de Lutte contre la Trypanosomiase Humaine Africaine (PNLTHA), Kinshasa, D.R.C.

Shampa C

Programme National de Lutte contre la Trypanosomiase Humaine Africaine (PNLTHA), Kinshasa, D.R.C.

Keeling MJ

Rock KS

MeSH

Bayes TheoremComputational BiologyDemocratic Republic of the CongoHumansModels, BiologicalModels, StatisticalTrypanosoma brucei gambienseTrypanosomiasis, African

Pub Type(s)

Journal Article

Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

33513134

Citation

Crump, Ronald E., et al. "Quantifying Epidemiological Drivers of Gambiense Human African Trypanosomiasis Across the Democratic Republic of Congo." PLoS Computational Biology, vol. 17, no. 1, 2021, pp. e1008532.

Crump RE, Huang CI, Knock ES, et al. Quantifying epidemiological drivers of gambiense human African Trypanosomiasis across the Democratic Republic of Congo. PLoS Comput Biol. 2021;17(1):e1008532.

Crump, R. E., Huang, C. I., Knock, E. S., Spencer, S. E. F., Brown, P. E., Mwamba Miaka, E., Shampa, C., Keeling, M. J., & Rock, K. S. (2021). Quantifying epidemiological drivers of gambiense human African Trypanosomiasis across the Democratic Republic of Congo. PLoS Computational Biology, 17(1), e1008532. https://doi.org/10.1371/journal.pcbi.1008532

Crump RE, et al. Quantifying Epidemiological Drivers of Gambiense Human African Trypanosomiasis Across the Democratic Republic of Congo. PLoS Comput Biol. 2021;17(1):e1008532. PubMed PMID: 33513134.

* Article titles in AMA citation format should be in sentence-case

TY - JOUR T1 - Quantifying epidemiological drivers of gambiense human African Trypanosomiasis across the Democratic Republic of Congo. AU - Crump,Ronald E, AU - Huang,Ching-I, AU - Knock,Edward S, AU - Spencer,Simon E F, AU - Brown,Paul E, AU - Mwamba Miaka,Erick, AU - Shampa,Chansy, AU - Keeling,Matt J, AU - Rock,Kat S, Y1 - 2021/01/29/ PY - 2020/06/23/received PY - 2020/11/12/accepted PY - 2021/02/22/revised PY - 2021/1/30/pubmed PY - 2021/5/18/medline PY - 2021/1/29/entrez SP - e1008532 EP - e1008532 JF - PLoS computational biology JO - PLoS Comput Biol VL - 17 IS - 1 N2 - 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. SN - 1553-7358 UR - https://www.unboundmedicine.com/medline/citation/33513134/Quantifying_epidemiological_drivers_of_gambiense_human_African_Trypanosomiasis_across_the_Democratic_Republic_of_Congo_ DB - PRIME DP - Unbound Medicine ER -

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Quantifying epidemiological drivers of gambiense human African Trypanosomiasis across the Democratic Republic of Congo.PLoS Comput Biol. 2021 01; 17(1):e1008532.PC