Deep Spatiotemporal Model for COVID-19 Forecasting Articles uri icon

publication date

  • May 2022

start page

  • 3519

end page

  • 3535


  • 9


  • 22

International Standard Serial Number (ISSN)

  • 1424-3210

Electronic International Standard Serial Number (EISSN)

  • 1424-8220


  • COVID-19 has caused millions of infections and deaths over the last 2 years. Machine
    learning models have been proposed as an alternative to conventional epidemiologic models in
    an effort to optimize short- and medium-term forecasts that will help health authorities to optimize
    the use of policies and resources to tackle the spread of the SARS-CoV-2 virus. Although previous
    machine learning models based on time pattern analysis for COVID-19 sensed data have shown
    promising results, the spread of the virus has both spatial and temporal components. This manuscript
    proposes a new deep learning model that combines a time pattern extraction based on the use of
    a Long-Short Term Memory (LSTM) Recurrent Neural Network (RNN) over a preceding spatial
    analysis based on a Convolutional Neural Network (CNN) applied to a sequence of COVID-19
    incidence images. The model has been validated with data from the 286 health primary care centers
    in the Comunidad de Madrid (Madrid region, Spain). The results show improved scores in terms
    of both root mean square error (RMSE) and explained variance (EV) when compared with previous
    models that have mainly focused on the temporal patterns and dependencies.


  • Telecommunications


  • machine learning; deep learning; covid-19 forecasting; spatiotemporal model; model optimization