Discovering Transmission Dynamics of COVID-19 in China

• URL: http://arxiv.org/abs/2512.22787v1
• Date: Sun, 28 Dec 2025 05:10:15 GMT
• Title: Discovering Transmission Dynamics of COVID-19 in China
• Authors: Zhou Yang, Edward Dougherty, Chen Zhang, Zhenhe Pan, Fang Jin,
• Abstract summary: We investigate China based SARS-CoV-2 transmission patterns using publicly released tracking data.<n>Results indicate substantial regional differences, with larger cities showing more infections, likely driven by social activities.
• Score: 7.360951660622174
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Mining Citywide Dengue Spread Patterns in Singapore Through Hotspot Dynamics from Open Web Data [13.39497094708001]
  Dengue continues to pose a persistent public health challenge in urban areas, particularly in tropical regions such as Singapore.<n>This study introduces a novel framework that uncovers latent transmission links between urban regions, mined directly from publicly available dengue case data.
  arXiv  Detail & Related papers  (2026-01-19T09:10:50Z)
• Combining digital data streams and epidemic networks for real time outbreak detection [14.31847187460321]
  We present LRTrend, a machine learning framework to identify outbreaks in real time.<n> LRTrend effectively aggregates diverse health and behavioral data streams within one region.<n>It learns disease-specific epidemic networks to aggregate information across regions.
  arXiv  Detail & Related papers  (2025-11-10T14:53:52Z)
• Understanding the Impact of the COVID-19 Pandemic on Transportation-related Behaviors with Human Mobility Data [36.99572681309649]
  The constrained outbreak of COVID-19 in Mainland China has recently been regarded as a successful example of fighting this virus. We use the huge amount of human mobility data collected from Baidu Maps to look into the detail reaction of the people there during the pandemic.
  arXiv  Detail & Related papers  (2022-02-16T10:32:39Z)
• C-Watcher: A Framework for Early Detection of High-Risk Neighborhoods Ahead of COVID-19 Outbreak [54.39837683016444]
  C-Watcher aims at screening every neighborhood in a target city and predicting infection risks, prior to the spread of COVID-19 from epicenters to the city. C-Watcher collects large-scale long-term human mobility data from Baidu Maps, then characterizes every residential neighborhood in the city using a set of features based on urban mobility patterns. We carried out extensive experiments on C-Watcher using the real-data records in the early stage of COVID-19 outbreaks.
  arXiv  Detail & Related papers  (2020-12-22T17:02:54Z)
• Predicting Infectiousness for Proactive Contact Tracing [75.62186539860787]
  Large-scale digital contact tracing is a potential solution to resume economic and social activity while minimizing spread of the virus. Various DCT methods have been proposed, each making trade-offs between privacy, mobility restrictions, and public health. This paper develops and test methods that can be deployed to a smartphone to proactively predict an individual's infectiousness.
  arXiv  Detail & Related papers  (2020-10-23T17:06:07Z)
• Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing [66.63200823918429]
  The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world. We used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research. Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues.
  arXiv  Detail & Related papers  (2020-07-22T18:02:39Z)
• Effectiveness and Compliance to Social Distancing During COVID-19 [72.94965109944707]
  We use a detailed set of mobility data to evaluate the impact that stay-at-home orders had on the spread of COVID-19 in the US. We show that there is a unidirectional Granger causality, from the median percentage of time spent daily at home to the daily number of COVID-19-related deaths with a lag of 2 weeks.
  arXiv  Detail & Related papers  (2020-06-23T03:36:19Z)
• Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment [90.12602012910465]
  We train on Italy's early COVID-19 outbreak through Twitter and transfer to several other countries. Our experiments show strong results with up to 0.85 Spearman correlation in cross-country predictions.
  arXiv  Detail & Related papers  (2020-06-05T02:04:25Z)
• COVI White Paper [67.04578448931741]
  Contact tracing is an essential tool to change the course of the Covid-19 pandemic. We present an overview of the rationale, design, ethical considerations and privacy strategy of COVI,' a Covid-19 public peer-to-peer contact tracing and risk awareness mobile application developed in Canada.
  arXiv  Detail & Related papers  (2020-05-18T07:40:49Z)
• Tracking COVID-19 by Tracking Infectious Trajectories [0.0]
  coronavirus pandemic has and is still causing large numbers of deaths and infected people. Studyciteref2 has reported that 79% of the confirmed infections in China were caused by undocumented patients who had no symptoms. We propose an IoT (Internet of Things) investigation system that was specifically designed to spot both undocumented patients and infectious places.
  arXiv  Detail & Related papers  (2020-05-12T02:20:09Z)
• Quantifying the Effects of Contact Tracing, Testing, and Containment Measures in the Presence of Infection Hotspots [18.227721607607183]
  Multiple lines of evidence strongly suggest that infection hotspots, where a single individual infects many others, play a key role in the transmission dynamics of COVID-19. We introduce a temporal point process modeling framework that specifically represents visits to the sites where individuals get in contact and infect each other. Under our model, the number of infections caused by an infectious individual naturally emerges to be overdispersed.
  arXiv  Detail & Related papers  (2020-04-15T17:18:32Z)
• A County-level Dataset for Informing the United States' Response to COVID-19 [5.682299443164938]
  We present a dataset that aggregates relevant data from governmental, journalistic, and academic sources on the U.S. county level. Our dataset contains more than 300 variables that summarize population estimates, demographics, ethnicity, housing, education, employment and income, climate, transit, scores, and healthcare system-related metrics.
  arXiv  Detail & Related papers  (2020-04-01T05:07:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.