A Multivariate Spatiotemporal Model of COVID-19 Epidemic Using Ensemble of ConvLSTM Networks

Abstract

The high R-naught factor of SARS-CoV-2 has created a race against time for mankind, and it necessitates rapid containment actions to control the spread. In such scenario short-term accurate spatiotemporal predictions can help understanding the dynamics of the spread in a geographic region and identify hotspots. However, due to the novelty of the disease there is very little disease-specific data generated yet. This poses a difficult problem for machine learning methods to learn a model of the epidemic spread from data. A proposed ensemble of convolutional LSTM-based spatiotemporal model can forecast the spread of the epidemic with high resolution and accuracy in a large geographic region. The feature construction method creates geospatial frames of features with or without temporal component based on latitudes and longitudes thus avoiding the need of location specific adjacency matrix. The model has been trained with available data for USA and Italy. It achieved 5.57% and 0.3% mean absolute percent error for total number of predicted infection cases in a 5-day prediction period for USA and Italy, respectively.