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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...

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Detalles Bibliográficos
Autores principales: Yamana, Teresa K., Shaman, Jeffrey
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2019
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
Descripción
Sumario: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.