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10.1038/s41598-021-94696-2 http://scihub22266oqcxt.onion/10.1038/s41598-021-94696-2 34312455!8313557!34312455     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sci+Rep 2021 ; 11 (1): 15173 Nephropedia Template TP {{tp|p=34312455|t=2021. Improving prediction of COVID-19 evolution by fusing epidemiological and mobility data |pdf=|usr=}}{{34312455}} gab.com Text Improving prediction of COVID-19 evolution by fusing epidemiological and mobility data JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=34312455 #FOAvip 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. Twit Text FOAVip Improving prediction of COVID-19 evolution by fusing epidemiological and mobility data ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=34312455 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34312455 #FOAvip English Wikipedia <ref>{{Cite pmid|34312455}}</ref> Improving prediction of COVID-19 evolution by fusing epidemiological and mobility data #MMPMID34312455 Garcia-Cremades S; Morales-Garcia J; Hernandez-Sanjaime R; Martinez-Espana R; Bueno-Crespo A; Hernandez-Orallo E; Lopez-Espin JJ; Cecilia JM Sci Rep 2021[Jul]; 11 (1): 15173 PMID34312455show ga 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. |Artificial Intelligence[MESH] |COVID-19/*epidemiology[MESH] |Forecasting[MESH] |Humans[MESH] |Incidence[MESH] |Models, Statistical[MESH] |Neural Networks, Computer[MESH]Improving prediction of COVID-19 evolution by fusing epidemiological a(epmc).pdf DeepDyve Pubget Overpricing