Assessing Strategies Against Gambiense Sleeping Sickness Through Mathematical Modeling
Author(s)
Type
Journal Article
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.
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.
Date Issued
2018-06-15
Date Acceptance
2018-06-01
Citation
Clinical Infectious Diseases, 2018, 66 (suppl_4), pp.S286-S292
ISSN
1058-4838
Publisher
Oxford University Press (OUP)
Start Page
S286
End Page
S292
Journal / Book Title
Clinical Infectious Diseases
Volume
66
Issue
suppl_4
Copyright Statement
© 2018 The Author(s). Published by Oxford University Press for the Infectious Diseases Society of America.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000434116900008&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Life Sciences & Biomedicine
Immunology
Infectious Diseases
Microbiology
gambiense human African trypanosomiasis
HAT
mathematical modeling
intervention effectiveness
elimination
ELIMINATION
Publication Status
Published
Date Publish Online
2018-06-01