About: Counting Down the 2020 Goals for 9 Neglected Tropical Diseases: What Have We Learned From Quantitative Analysis and Transmission Modeling?

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Counting Down the 2020 Goals for 9 Neglected Tropical Diseases: What Have We Learned From Quantitative Analysis and Transmission Modeling?
Creator	Hollingsworth, T
Source	Medline; PMC
abstract	The control of neglected tropical diseases (NTDs) has received huge investment in recent years, leading to large reductions in morbidity. In 2012, the World Health Organization set ambitious targets for eliminating many of these diseases as a public health problem by 2020, an aspiration that was supported by donations of treatments, intervention materials, and funding committed by a broad partnership of stakeholders in the London Declaration on NTDs. Alongside these efforts, there has been an increasing role for quantitative analysis and modeling to support the achievement of these goals through evaluation of the likely impact of interventions, the factors that could undermine these achievements, and the role of new diagnostics and treatments in reducing transmission. In this special issue, we aim to summarize those insights in an accessible way. This article acts as an introduction to the special issue, outlining key concepts in NTDs and insights from modeling as we approach 2020.
has issue date	2018-06-15(xsd:dateTime)
bibo:doi	10.1093/cid/ciy284
bibo:pmid	29860293
has license	cc-by
sha1sum (hex)	3f9b24ea368416ce33708bb28327fb0b686b0ef0
schema:url	https://doi.org/10.1093/cid/ciy284
resource representing a document's title	Counting Down the 2020 Goals for 9 Neglected Tropical Diseases: What Have We Learned From Quantitative Analysis and Transmission Modeling?
has PubMed Central identifier	PMC5982793
has PubMed identifier	29860293
schema:publication	Clin Infect Dis
resource representing a document's body	covid:3f9b24ea368416ce33708bb28327fb0b686b0ef0#body_text
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