STSIR: Spatial Temporal Pandemic Model with Mobility Data

TL;DR

The paper introduces STSIR, a novel spatial-temporal pandemic model integrating mobility data to predict COVID-19 spread and evaluate policies more effectively than traditional models.

Contribution

It presents the STSIR framework that combines mobility indices with SIR dynamics and introduces MSSA for parameter optimization, enabling observable index-based policy evaluation.

Findings

Accurately predicts pandemic scale using mobility data.

Provides clear policy analysis with observable indices.

Demonstrates effectiveness on Baidu and China datasets.

Abstract

With the outbreak of COVID-19, how to mitigate and suppress its spread is a big issue to the government. Department of public health need powerful models to model and predict the trend and scale of such pandemic. And models that could evaluate the effect of the public policy are also essential to the fight with the COVID-19. A main limitation of existing models is that they can only evaluate the policy by calculating $R_0$ after infection happens instead of giving observable index. To tackle this, based on the transmission character of the COVID-19, we preposed a novel framework Spatial-Temporal-Susceptible-Infected-Removed (STSIR) model. In particular, we merged both intra-city and inter-city mobility index with the traditional SIR dynamics and make it a dynamic system. And we proved that the STSIR system is a closed system which makes the system self-consistent. And finally we proposed a Multi-Stage Simulated Annealing (MSSA) algorithm to find optimal parameter of the system. In our experiments, based on Baidu Mobility dataset, and China pandemic dataset provided by Dingxiangyuan, our model can effectively predict the total scale of the pandemic and also gives clear policy analysis with observable index.