Discovering Transmission Dynamics of COVID-19 in China

TL;DR

This study analyzes COVID-19 transmission in China using public data, NLP, and mobility analysis to understand regional differences, infection sources, and temporal-spatial spread patterns.

Contribution

It introduces a comprehensive method combining NLP and mobility data to map COVID-19 transmission dynamics in China.

Findings

Larger cities had more infections, driven by social activities.

Most symptomatic individuals sought hospitalization within 5 days.

Transmission sources shifted from travel to social activities over time.

Abstract

A comprehensive retrospective analysis of public health interventions, such as large scale testing, quarantining, and contact tracing, can help identify mechanisms most effective in mitigating COVID-19. We investigate China based SARS-CoV-2 transmission patterns (e.g., infection type and likely transmission source) using publicly released tracking data. We collect case reports from local health commissions, the Chinese CDC, and official local government social media, then apply NLP and manual curation to construct transmission/tracking chains. We further analyze tracking data together with Wuhan population mobility data to quantify and visualize temporal and spatial spread dynamics. Results indicate substantial regional differences, with larger cities showing more infections, likely driven by social activities. Most symptomatic individuals (79\%) were hospitalized within 5 days of symptom onset, and those with confirmed-case contact sought admission in under 5 days. Infection sources also shifted over time: early cases were largely linked to travel to (or contact with travelers from) Hubei Province, while later transmission was increasingly associated with social activities.