Comparing Models for Early Warning Systems of Neglected Tropical Diseases
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http://data.d4science.org/ctlg/RISIS2OpenData/dedup_wf_001--a2b0d702876987f71399705bf45eac66 |
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Identity
Access Modality
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Access Right | Open Access |
Attribution
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Author | Luis Fernando Chaves, 0000-0002-5301-2764 |
Contributor | Utzinger, Juerg |
Publishing
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Collected From | PubMed Central; ORCID; Datacite; UnpayWall; DOAJ-Articles; Crossref; Microsoft Academic Graph |
Hosted By | Europe PubMed Central; PLoS Neglected Tropical Diseases |
Publication Date | 2007-10-01 |
Publisher | Public Library of Science (PLoS) |
Additional Info
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Description | Author Summary Early Warning Systems (EWS) are management tools to predict the occurrence of epidemics. They are based on the dependence of a given infectious disease on environmental variables. Although several neglected tropical diseases are sensitive to the effect of climate, our ability to predict their dynamics has been barely studied. In this paper, we use several models to determine if the relationship between cases and climatic variability is robust—that is, not simply an artifact of model choice. We propose that EWS should be based on results from several models that are to be compared in terms of their ability to predict future number of cases. We use a specific metric for this comparison known as the predictive R 2, which measures the accuracy of the predictions. For example, an R 2 of 1 indicates perfect accuracy for predictions that perfectly match observed cases. For cutaneous leishmaniasis, R 2 values range from 72% to77%, well above predictions using mean seasonal values (64%). We emphasize that predictability should be evaluated with observations that have not been used to fit the model. Finally, we argue that EWS should incorporate climatic variables that are known to have a consistent relationship with the number of observed cases. |
Language | UNKNOWN |
Resource Type | Other literature type; Article |
keyword | keywords.Public Health, Environmental and Occupational Health |
system:type | publication |
Management Info
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Source | https://science-innovation-policy.openaire.eu/search/publication?articleId=dedup_wf_001::a2b0d702876987f71399705bf45eac66 |
Author | jsonws_user |
Last Updated | 27 December 2020, 02:01 (CET) |
Created | 27 December 2020, 02:01 (CET) |