Observational studyThe Lancet. Digital health2020
Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study.
Observational study in The Lancet. Digital health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 221 papers, 11 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
221 citing papers in PubMed, 11 syntheses or guidelines pooled it.
- The COVID-19 pandemic in a paediatric population: A health care perspective.Journal of mother and child · 2022Pooled it
- Case fatality rate of COVID-19: a systematic review and meta-analysis.Journal of preventive medicine and hygiene · 2021Pooled it
- How do we share data in COVID-19 research? A systematic review of COVID-19 datasets in PubMed Central Articles.Briefings in bioinformatics · 2021Pooled it
- Clinical, epidemiological, laboratory, and radiological characteristics of novel Coronavirus (2019-nCoV) in retrospective studies: A systemic review and meta-analysis.Indian journal of medical microbiology · 2021Pooled it
- SARS-CoV-2 (COVID-19): What Do We Know About Children? A Systematic Review.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2020Pooled it
- Clinical characteristics, laboratory findings, radiographic signs and outcomes of 61,742 patients with confirmed COVID-19 infection: A systematic review and meta-analysis.Microbial pathogenesis · 2020Pooled it
- Prevalence of co-morbidities and their association with mortality in patients with COVID-19: A systematic review and meta-analysis.Diabetes, obesity & metabolism · 2020Pooled it
- Key questions for modelling COVID-19 exit strategies.Proceedings. Biological sciences · 2020Pooled it
- Comparison of confirmed COVID-19 with SARS and MERS cases - Clinical characteristics, laboratory findings, radiographic signs and outcomes: A systematic review and meta-analysis.Reviews in medical virology · 2020Pooled it
- Clinical characteristics of hospitalized patients with SARS-CoV-2 infection: A single arm meta-analysis.Journal of medical virology · 2020Pooled it
- Systematic review of COVID-19 in children shows milder cases and a better prognosis than adults.Acta paediatrica (Oslo, Norway : 1992) · 2020Pooled it
- Trial
- The Impact of the COVID-19 Pandemic on Diagnosis, Treatment, and Survival of Lung Cancer Patients in Thailand from 2019-2021.Journal of clinical medicine · 2026Article
- Impact of nasal and inhaled corticosteroids on SARS-CoV-2 infection susceptibility.The Journal of allergy and clinical immunology · 2025Article
- Comprehensive Analysis of SARS-CoV-2 Dynamics in Bangladesh: Infection Trends and Variants (2020-2023).Viruses · 2024Article
- Article
- How much does government's short-term response matter for explaining cross-country variation in COVID-19 infection outcomes? A regression-based relative importance analysis of 84 countries.BMJ public health · 2024Article
- How robust are estimates of key parameters in standard viral dynamic models?PLoS computational biology · 2024Article
- Investigating the spatiotemporal characteristics and medical response during the initial COVID-19 epidemic in six Chinese cities.Scientific reports · 2024Article
- Picture analysis of billboards and infographic graphics advertising COVID-19 on promoting preventive behaviors and taking vaccination against the Coronavirus disease pandemic.Scientific reports · 2024Article
161 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: As the outbreak of coronavirus disease 2019 (COVID-19) progresses, epidemiological data are needed to guide situational awareness and intervention strategies. Here we describe efforts to compile and disseminate epidemiological information on COVID-19 from news media and social networks. Methods: In this population-level observational study, we searched DXY.cn, a health-care-oriented social network that is currently streaming news reports on COVID-19 from local and national Chinese health agencies. We compiled a list of individual patients with COVID-19 and daily province-level case counts between Jan 13 and Jan 31, 2020, in China. We also compiled a list of internationally exported cases of COVID-19 from global news media sources (Kyodo News, The Straits Times, and CNN), national governments, and health authorities. We assessed trends in the epidemiology of COVID-19 and studied the outbreak progression across China, assessing delays between symptom onset, seeking care at a hospital or clinic, and reporting, before and after Jan 18, 2020, as awareness of the outbreak increased. All data were made publicly available in real time. Findings: We collected data for 507 patients with COVID-19 reported between Jan 13 and Jan 31, 2020, including 364 from mainland China and 143 from outside of China. 281 (55%) patients were male and the median age was 46 years (IQR 35-60). Few patients (13 [3%]) were younger than 15 years and the age profile of Chinese patients adjusted for baseline demographics confirmed a deficit of infections among children. Across the analysed period, delays between symptom onset and seeking care at a hospital or clinic were longer in Hubei province than in other provinces in mainland China and internationally. In mainland China, these delays decreased from 5 days before Jan 18, 2020, to 2 days thereafter until Jan 31, 2020 (p=0·0009). Although our sample captures only 507 (5·2%) of 9826 patients with COVID-19 reported by official sources during the analysed period, our data align with an official report published by Chinese authorities on Jan 28, 2020. Interpretation: News reports and social media can help reconstruct the progression of an outbreak and provide detailed patient-level data in the context of a health emergency. The availability of a central physician-oriented social network facilitated the compilation of publicly available COVID-19 data in China. As the outbreak progresses, social media and news reports will probably capture a diminishing fraction of COVID-19 cases globally due to reporting fatigue and overwhelmed health-care systems. In the early stages of an outbreak, availability of public datasets is important to encourage analytical efforts by independent teams and provide robust evidence to guide interventions. Funding: Fogarty International Center, US National Institutes of Health.
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