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Clin Infect Dis. 2018 Jun 1;66(suppl_4):S286-S292. doi: 10.1093/cid/ciy018.

Assessing Strategies Against Gambiense Sleeping Sickness Through Mathematical Modeling.

Author information

1
Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, Coventry, United Kingdom.
2
School of Life Sciences, University of Warwick, Coventry, United Kingdom.
3
Yale School of Public Health, Yale University, New Haven, Connecticut.
4
Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Switzerland.
5
University of Basel, Switzerland.
6
Institute of Disease Modeling, Bellevue, Washington.
7
Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, United Kingdom.
8
Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, United Kingdom.
9
Foundation for Innovative New Diagnostics, Geneva, Switzerland.
10
Mathematics Institute, University of Warwick, Coventry, United Kingdom.

Abstract

Background:

Control of gambiense sleeping sickness relies predominantly on passive and active screening of people, followed by treatment.

Methods:

Mathematical modeling explores the potential of 3 complementary interventions in high- and low-transmission settings.

Results:

Intervention strategies that included vector control are predicted to halt transmission most quickly. Targeted active screening, with better and more focused coverage, and enhanced passive surveillance, with improved access to diagnosis and treatment, are both estimated to avert many new infections but, when used alone, are unlikely to halt transmission before 2030 in high-risk settings.

Conclusions:

There was general model consensus in the ranking of the 3 complementary interventions studied, although with discrepancies between the quantitative predictions due to differing epidemiological assumptions within the models. While these predictions provide generic insights into improving control, the most effective strategy in any situation depends on the specific epidemiology in the region and the associated costs.

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