Space-Distributed Traffic-Enhanced LSTM-Based Machine Learning Model for COVID-19 Incidence Forecasting

Abstract

The COVID-19 virus continues to generate waves of infections around the world. With major areas in developing countries still lagging behind in vaccination campaigns, the risk of new variants that can cause re-infections worldwide makes the monitoring and forecasting of the evolution of the virus a high priority. Having accurate models able to forecast the incidence of the spread of the virus provides help to policymakers and health professionals in managing the scarce resources in an optimal way. In this paper, a new machine learning model is proposed to forecast the spread of the virus one-week ahead in a geographic area which combines mobility and COVID-19 incidence data. The area is divided into zones or districts according to the location of the COVID-19 measuring points. A traffic-driven mobility estimate among adjacent districts is proposed to capture the spatial spread of the virus. Traffic-driven mobility in adjacent districts will be used together with COVID-19 incidence data to feed a new deep learning LSTM-based model which will extract patterns from mobility-modulated COVID-19 incidence spatiotemporal data in order to optimize one-week ahead estimations. The model is trained and validated with open data available for the city of Madrid (Spain) for 3 different validation scenarios. A baseline model based on previous literature able to extract temporal patterns in COVID-19 incidence time series is also trained with the same dataset. The results show that the proposed model, based on the combination of traffic and COVID-19 incidence data, is able to outperform the baseline model in all the validation scenarios.

Using Traffic Sensors in Smart Cities to Enhance a Spatio-Temporal Deep Learning Model for COVID-19 Forecasting

“…Regression Trees [12] and Gaussian Process Regressors (GPR) [13] have shown the best performance as compared to other regression models for traffic volume forecasting [6]. Machine learning models based on time-series analysis are able to achieve better COVID-19 short-term predictions when using traffic data measured by smart city sensors [14]. Several machine learning models have been proposed to provide answers to different COVID-19-related questions, such as improving the diagnosis of positive cases based on reported symptoms [15], X-ray images [16], or laboratory data [17] or estimating the probability that positive cases will develop complications that will require hospitalization [18].…”