Challenges of Distributed Computing for Pandemic Spread Prediction based on Large-Scale Human Interaction Data

Pandemic like COVID-19 poses severe challenges to public health and causes great damages to human lives and economy. Computational epidemiology allows the authorities (e.g., federal, state, city governments) to predict the future states of pandemic and take preventive measures based on that. However, due to widespread human mobility, epidemic prediction on a small-scale (e.g., city) may not produce effective results. Therefore, to obtain a clearer picture of pandemics, authorities often rely on large-scale human mobility data. Processing such a large graph dataset is highly computation intensive, and thus generates long latency. Distributed computing, in which the graph is partitioned and processed by multiple servers in parallel, is a solution to this problem. However, as human mobility changes over time, the graph varies over time, which may make the previous graph partition not effective anymore in limiting the communication overhead. In this paper, we study the existing works in the literature for processing large-scale human mobility graph data to predict pandemic spread. We conducted comprehensive experiments based on real-world large-scale human mobility data that covers the entire state of Virginia, USA. Based on the experimental evaluations, we present our findings and discuss the possible future research directions for the aforementioned challenge.