Accurate and Efficient Distributed COVID-19 Spread Prediction based on a Large-Scale Time-Varying People Mobility Graph

Abstract

Compared to previous epidemics, COVID-19 spreads much faster in people gatherings. Thus, we need not only more accurate epidemic spread prediction considering the people gatherings but also more time-efficient prediction for taking actions (e.g., allocating medical equipments) in time. Motivated by this, we analyzed a time-varying people mobility graph of the United States (US) for one year and the effectiveness of previous methods in handling time-varying graphs. We identified several factors that influence COVID-19 spread and observed that some graph changes are transient, which degrades the effectiveness of the previous graph repartitioning and replication methods in distributed graph processing since they generate more time overhead than saved time. Based on the analysis, we propose an accurate and time-efficient Distributed Epidemic Spread Prediction system (DESP). First, DESP incorporates the factors into a previous prediction model to increase the prediction accuracy. Second, DESP conducts repartitioning and replication only when a graph change is stable for a certain time period (predicted using machine learning) to ensure the operation improves time-efficiency. We conducted extensive experiments on Amazon AWS based on real people movement datasets. Experimental results show DESP reduces communication time by up to 52%, while enhancing accuracy by up to 24% compared to existing methods.