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Improving prediction of COVID-19 evolution by fusing epidemiological and mobility data.
García-Cremades, Santi; Morales-García, Juan; Hernández-Sanjaime, Rocío; Martínez-España, Raquel; Bueno-Crespo, Andrés; Hernández-Orallo, Enrique; López-Espín, José J; Cecilia, José M.
  • García-Cremades S; Center of Operations Research, Miguel Hernandez University of Elche (UMH), 03202, Elche, Spain.
  • Morales-García J; Computer Science Department, Universidad Católica de Murcia (UCAM), 30107, Murcia, Spain.
  • Hernández-Sanjaime R; Center of Operations Research, Miguel Hernandez University of Elche (UMH), 03202, Elche, Spain.
  • Martínez-España R; Computer Science Department, Universidad Católica de Murcia (UCAM), 30107, Murcia, Spain.
  • Bueno-Crespo A; Computer Science Department, Universidad Católica de Murcia (UCAM), 30107, Murcia, Spain.
  • Hernández-Orallo E; Computer Engineering Department, Univesitat Politècnica de València (UPV), 46022, Valencia, Spain.
  • López-Espín JJ; Center of Operations Research, Miguel Hernandez University of Elche (UMH), 03202, Elche, Spain.
  • Cecilia JM; Computer Engineering Department, Univesitat Politècnica de València (UPV), 46022, Valencia, Spain. jmcecilia@disca.upv.es.
Sci Rep ; 11(1): 15173, 2021 07 26.
Article in English | MEDLINE | ID: covidwho-1327222
ABSTRACT
We are witnessing the dramatic consequences of the COVID-19 pandemic which, unfortunately, go beyond the impact on the health system. Until herd immunity is achieved with vaccines, the only available mechanisms for controlling the pandemic are quarantines, perimeter closures and social distancing with the aim of reducing mobility. Governments only apply these measures for a reduced period, since they involve the closure of economic activities such as tourism, cultural activities, or nightlife. The main criterion for establishing these measures and planning socioeconomic subsidies is the evolution of infections. However, the collapse of the health system and the unpredictability of human behavior, among others, make it difficult to predict this evolution in the short to medium term. This article evaluates different models for the early prediction of the evolution of the COVID-19 pandemic to create a decision support system for policy-makers. We consider a wide branch of models including artificial neural networks such as LSTM and GRU and statistically based models such as autoregressive (AR) or ARIMA. Moreover, several consensus strategies to ensemble all models into one system are proposed to obtain better results in this uncertain environment. Finally, a multivariate model that includes mobility data provided by Google is proposed to better forecast trend changes in the 14-day CI. A real case study in Spain is evaluated, providing very accurate results for the prediction of 14-day CI in scenarios with and without trend changes, reaching 0.93 [Formula see text], 4.16 RMSE and 1.08 MAE.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study Topics: Vaccines Limits: Humans Country/Region as subject: Europa Language: English Journal: Sci Rep Year: 2021 Document Type: Article Affiliation country: S41598-021-94696-2

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study Topics: Vaccines Limits: Humans Country/Region as subject: Europa Language: English Journal: Sci Rep Year: 2021 Document Type: Article Affiliation country: S41598-021-94696-2