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PLoS Negl Trop Dis. 2020 Jan 21;14(1):e0007976. doi: 10.1371/journal.pntd.0007976. eCollection 2020 Jan.

Assessing the impact of aggregating disease stage data in model predictions of human African trypanosomiasis transmission and control activities in Bandundu province (DRC).

Author information

1
Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland.
2
University of Basel, Basel, Switzerland.
3
School of Public Health, Yale University, New Haven, Connecticut, United States of America.
4
College of Veterinary Medicine and Biosciences, Texas A&M University, College Station, Texas, United States of America.
5
Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom.
6
Mathematics Institute, University of Warwick, Coventry, United Kingdom.
7
Institute of Disease Modeling, Seattle, Washington, United States of America.
8
Department of Statistics, University of Warwick, Coventry, United Kingdom.
9
Programme National de Lutte contre la Trypanosomiase Humaine Africaine, Kinshasa, the Democratic Republic of the Congo.
10
Foundation for Innovative New Diagnostics, Geneva, Switzerland.
11
Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, United Kingdom.
12
Department of Public Health, Institute of Tropical Medicine, Antwerp, Belgium.

Abstract

Since the turn of the century, the global community has made great progress towards the elimination of gambiense human African trypanosomiasis (HAT). Elimination programs, primarily relying on screening and treatment campaigns, have also created a rich database of HAT epidemiology. Mathematical models calibrated with these data can help to fill remaining gaps in our understanding of HAT transmission dynamics, including key operational research questions such as whether integrating vector control with current intervention strategies is needed to achieve HAT elimination. Here we explore, via an ensemble of models and simulation studies, how including or not disease stage data, or using more updated data sets affect model predictions of future control strategies.

PMID:
31961872
DOI:
10.1371/journal.pntd.0007976
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Conflict of interest statement

The authors have declared that no competing interests exist.

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