Discovering Transmission Dynamics of COVID-19 in China
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 15 references that are in the map. First posted on arXiv 28 Dec 2025.
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References · 6
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2018Spatial-temporal Multi-Task Learning for Within-field Cotton Yield Predictioncited by 7
- 2018Coordinating Disaster Emergency Response with Heuristic Reinforcement Learningcited by 5
- 2018Forecasting People's Needs in Hurricane Events from Social Networkcited by 4
- 2019Data Centers Job Scheduling with Deep Reinforcement Learningcited by 2
- 2020COVID19 Tracking: An Interactive Tracking, Visualizing and Analyzing Platformcited by 1
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