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Clin Infect Dis. 2018 Jun 1;66(suppl_4):S281-S285. doi: 10.1093/cid/ciy005.

Policy Lessons From Quantitative Modeling of Leprosy.

Author information

1
Department of Global Health and Development, London School of Hygiene and Tropical Medicine, United Kingdom.
2
Department of Public Health, Erasmus MC, University Medical Center Rotterdam, The Netherlands.
3
Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry.
4
Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, United Kingdom.
5
Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, Connecticut.
6
Francis I. Proctor Foundation for Research in Ophthalmology, University of California, San Francisco.

Abstract

Recent mathematical and statistical modeling of leprosy incidence data provides estimates of the current undiagnosed population and projections of diagnosed cases, as well as ongoing transmission. Furthermore, modeling studies have been used to evaluate the effectiveness of proposed intervention strategies, such as postleprosy exposure prophylaxis and novel diagnostics, relative to current approaches. Such modeling studies have revealed both a slow decline of new cases and a substantial pool of undiagnosed infections. These findings highlight the need for active case detection, particularly targeting leprosy foci, as well as for continued research into innovative accurate, rapid, and cost-effective diagnostics. As leprosy incidence continues to decline, targeted active case detection primarily in foci and connected areas will likely become increasingly important.

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