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A framework for evaluating the effects of observational type and quality on vector-borne disease forecast
Recent research has advanced infectious disease forecasting from an aspiration to an operational reality. The accuracy of such operational forecasting depends on the quantity and quality of observations available for system optimization. In particular, for forecasting systems that use combined mecha...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7315892/ https://www.ncbi.nlm.nih.gov/pubmed/31439454 http://dx.doi.org/10.1016/j.epidem.2019.100359 |
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author | Yamana, Teresa K. Shaman, Jeffrey |
author_facet | Yamana, Teresa K. Shaman, Jeffrey |
author_sort | Yamana, Teresa K. |
collection | PubMed |
description | Recent research has advanced infectious disease forecasting from an aspiration to an operational reality. The accuracy of such operational forecasting depends on the quantity and quality of observations available for system optimization. In particular, for forecasting systems that use combined mechanistic model-inference approaches, a broad suite of epidemiological observations could be utilized, if these data were available in near real time. In cases where such data are limited, an in silica, synthetic framework for evaluating the potential benefits of observations on forecasting accuracy can allow researchers and public health officials to more optimally allocate resources for disease surveillance and monitoring. Here, we demonstrate the application of such a framework, using a model-inference system designed to predict dengue, and identify the type and quality of observations that would improve forecasting accuracy. |
format | Online Article Text |
id | pubmed-7315892 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
record_format | MEDLINE/PubMed |
spelling | pubmed-73158922020-06-25 A framework for evaluating the effects of observational type and quality on vector-borne disease forecast Yamana, Teresa K. Shaman, Jeffrey Epidemics Article Recent research has advanced infectious disease forecasting from an aspiration to an operational reality. The accuracy of such operational forecasting depends on the quantity and quality of observations available for system optimization. In particular, for forecasting systems that use combined mechanistic model-inference approaches, a broad suite of epidemiological observations could be utilized, if these data were available in near real time. In cases where such data are limited, an in silica, synthetic framework for evaluating the potential benefits of observations on forecasting accuracy can allow researchers and public health officials to more optimally allocate resources for disease surveillance and monitoring. Here, we demonstrate the application of such a framework, using a model-inference system designed to predict dengue, and identify the type and quality of observations that would improve forecasting accuracy. 2019-08-05 2020-03 /pmc/articles/PMC7315892/ /pubmed/31439454 http://dx.doi.org/10.1016/j.epidem.2019.100359 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) ). |
spellingShingle | Article Yamana, Teresa K. Shaman, Jeffrey A framework for evaluating the effects of observational type and quality on vector-borne disease forecast |
title | A framework for evaluating the effects of observational type and quality on vector-borne disease forecast |
title_full | A framework for evaluating the effects of observational type and quality on vector-borne disease forecast |
title_fullStr | A framework for evaluating the effects of observational type and quality on vector-borne disease forecast |
title_full_unstemmed | A framework for evaluating the effects of observational type and quality on vector-borne disease forecast |
title_short | A framework for evaluating the effects of observational type and quality on vector-borne disease forecast |
title_sort | framework for evaluating the effects of observational type and quality on vector-borne disease forecast |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7315892/ https://www.ncbi.nlm.nih.gov/pubmed/31439454 http://dx.doi.org/10.1016/j.epidem.2019.100359 |
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