RT Journal Article
T1 A new RNN based machine learning model to forecast COVID-19 incidence, enhanced by the use of mobility data from the bike-sharing service in Madrid
A1 Muñoz Organero, Mario
A1 Callejo Pinardo, Patricia
A1 Hombrados Herrera, Miguel Ángel
AB As a respiratory virus, COVID-19 propagates based on human-to-human interactions with positive COVID-19 cases. The temporal evolution of new COVID-19 infections depends on the existing number of COVID-19 infections and the people's mobility. This article proposes a new model to predict upcoming COVID-19 incidence values that combines both current and near-past incidence values together with mobility data. The model is applied to the city of Madrid (Spain). The city is divided into districts. The weekly COVID-19 incidence data per district is used jointly with a mobility estimation based on the number of rides reported by the bike-sharing service in the city of Madrid (BiciMAD). The model employs a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) to detect temporal patterns for COVID-19 infections and mobility data, and combines the output of the LSTM layers into a dense layer that can learn the spatial patterns (the spread of the virus between districts). A baseline model that employs a similar RNN but only based on the COVID-19 confirmed cases with no mobility data is presented and used to estimate the model gain when adding mobility data. The results show that using the bike-sharing mobility estimation the proposed model increases the accuracy by 11.7% compared with the baseline model.
PB Elsevier
YR 2023
FD 2023-06-01
LK https://hdl.handle.net/10016/39041
UL https://hdl.handle.net/10016/39041
LA eng
NO This work is part of the agreement between the Community of Madrid and the Universidad Carlos III de Madrid for the funding of research projects on SARS-CoV-2 and COVID-19 disease, project name "Multi-source and multi-method prediction to support COVID-19 policy decision making”, which was supported with REACT-EU funds from the European regional development fund "a way of making Europe". This work was supported in part by the projects "Real time social sensor AnaLysis and deep learning based resource EStimations for multimodal transport" MaGIST-RALES, funded by the Spanish Agencia Estatal de Investigación (AEI, doi: 10.13039/501100011033) under grant PID2019-105221RB-C44/AEI/10.13039/501100011033 and "FLATCITY-APP: Mobile application for FlatCity" funded by the Spanish Ministerio de Ciencia e Innovación and the Agencia Estatal de Investigación MCIN/AEI/10.13039/501100011033 and the European Union "NextGenerationEU/PRTR" under grant PDC2021-121239-C33.
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