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Modelling to infer the role of animals in gambiense human African trypanosomiasis transmission and elimination in the DRC.
PLoS Negl Trop Dis. 2022 07; 16(7):e0010599.PN

Abstract

Gambiense human African trypanosomiasis (gHAT) has been targeted for elimination of transmission (EoT) to humans by 2030. Whilst this ambitious goal is rapidly approaching, there remain fundamental questions about the presence of non-human animal transmission cycles and their potential role in slowing progress towards, or even preventing, EoT. In this study we focus on the country with the most gHAT disease burden, the Democratic Republic of Congo (DRC), and use mathematical modelling to assess whether animals may contribute to transmission in specific regions, and if so, how their presence could impact the likelihood and timing of EoT. By fitting two model variants-one with, and one without animal transmission-to the human case data from 2000-2016 we estimate model parameters for 158 endemic health zones of the DRC. We evaluate the statistical support for each model variant in each health zone and infer the contribution of animals to overall transmission and how this could impact predicted time to EoT. We conclude that there are 24/158 health zones where there is substantial to decisive statistical support for some animal transmission. However-even in these regions-we estimate that animals would be extremely unlikely to maintain transmission on their own. Animal transmission could hamper progress towards EoT in some settings, with projections under continuing interventions indicating that the number of health zones expected to achieve EoT by 2030 reduces from 68/158 to 61/158 if animal transmission is included in the model. With supplementary vector control (at a modest 60% tsetse reduction) added to medical screening and treatment interventions, the predicted number of health zones meeting the goal increases to 147/158 for the model including animal transmission. This is due to the impact of vector reduction on transmission to and from all hosts.

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Authors+Show Affiliations

Crump RE
Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom. Mathematics Institute, The University of Warwick, Coventry, United Kingdom.
Huang CI
Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom. Mathematics Institute, The University of Warwick, Coventry, United Kingdom.
Spencer SEF
Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom. The Department of Statistics, The University of Warwick, Coventry, United Kingdom.
Brown PE
Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom. Mathematics Institute, The University of Warwick, Coventry, United Kingdom.
Shampa C
Programme National de Lutte contre la Trypanosomiase Humaine Africaine (PNLTHA), Kinshasa, Democratic Republic of the Congo.
Mwamba Miaka E
Programme National de Lutte contre la Trypanosomiase Humaine Africaine (PNLTHA), Kinshasa, Democratic Republic of the Congo.
Rock KS
Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, The University of Warwick, Coventry, United Kingdom. Mathematics Institute, The University of Warwick, Coventry, United Kingdom.

MeSH

AnimalsDemocratic Republic of the CongoForecastingHumansModels, TheoreticalTrypanosoma brucei gambienseTrypanosomiasis, African

Pub Type(s)

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

Language

eng

PubMed ID

35816487

Citation

Crump, Ronald E., et al. "Modelling to Infer the Role of Animals in Gambiense Human African Trypanosomiasis Transmission and Elimination in the DRC." PLoS Neglected Tropical Diseases, vol. 16, no. 7, 2022, pp. e0010599.
Crump RE, Huang CI, Spencer SEF, et al. Modelling to infer the role of animals in gambiense human African trypanosomiasis transmission and elimination in the DRC. PLoS Negl Trop Dis. 2022;16(7):e0010599.
Crump, R. E., Huang, C. I., Spencer, S. E. F., Brown, P. E., Shampa, C., Mwamba Miaka, E., & Rock, K. S. (2022). Modelling to infer the role of animals in gambiense human African trypanosomiasis transmission and elimination in the DRC. PLoS Neglected Tropical Diseases, 16(7), e0010599. https://doi.org/10.1371/journal.pntd.0010599
Crump RE, et al. Modelling to Infer the Role of Animals in Gambiense Human African Trypanosomiasis Transmission and Elimination in the DRC. PLoS Negl Trop Dis. 2022;16(7):e0010599. PubMed PMID: 35816487.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Modelling to infer the role of animals in gambiense human African trypanosomiasis transmission and elimination in the DRC. AU - Crump,Ronald E, AU - Huang,Ching-I, AU - Spencer,Simon E F, AU - Brown,Paul E, AU - Shampa,Chansy, AU - Mwamba Miaka,Erick, AU - Rock,Kat S, Y1 - 2022/07/11/ PY - 2021/12/23/received PY - 2022/06/22/accepted PY - 2022/07/21/revised PY - 2022/7/12/pubmed PY - 2022/7/26/medline PY - 2022/7/11/entrez SP - e0010599 EP - e0010599 JF - PLoS neglected tropical diseases JO - PLoS Negl Trop Dis VL - 16 IS - 7 N2 - Gambiense human African trypanosomiasis (gHAT) has been targeted for elimination of transmission (EoT) to humans by 2030. Whilst this ambitious goal is rapidly approaching, there remain fundamental questions about the presence of non-human animal transmission cycles and their potential role in slowing progress towards, or even preventing, EoT. In this study we focus on the country with the most gHAT disease burden, the Democratic Republic of Congo (DRC), and use mathematical modelling to assess whether animals may contribute to transmission in specific regions, and if so, how their presence could impact the likelihood and timing of EoT. By fitting two model variants-one with, and one without animal transmission-to the human case data from 2000-2016 we estimate model parameters for 158 endemic health zones of the DRC. We evaluate the statistical support for each model variant in each health zone and infer the contribution of animals to overall transmission and how this could impact predicted time to EoT. We conclude that there are 24/158 health zones where there is substantial to decisive statistical support for some animal transmission. However-even in these regions-we estimate that animals would be extremely unlikely to maintain transmission on their own. Animal transmission could hamper progress towards EoT in some settings, with projections under continuing interventions indicating that the number of health zones expected to achieve EoT by 2030 reduces from 68/158 to 61/158 if animal transmission is included in the model. With supplementary vector control (at a modest 60% tsetse reduction) added to medical screening and treatment interventions, the predicted number of health zones meeting the goal increases to 147/158 for the model including animal transmission. This is due to the impact of vector reduction on transmission to and from all hosts. SN - 1935-2735 UR - https://www.unboundmedicine.com/medline/citation/35816487/Modelling_to_infer_the_role_of_animals_in_gambiense_human_African_trypanosomiasis_transmission_and_elimination_in_the_DRC_ DB - PRIME DP - Unbound Medicine ER -