Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2)
Open accessPublished version of a preprint
Abstract
Undetected cases: The virus causing coronavirus disease 2019 (COVID-19) has now become pandemic. How has it managed to spread from China to all around the world within 3 to 4 months? Li et al. used multiple sources to infer the proportion of early infections that went undetected and their contribution to virus spread. The researchers combined data from Tencent, one of the world's largest social media and technology companies, with a networked dynamic metapopulation model and Bayesian inference to analyze early spread within China. They estimate that ∼86% of cases were undocumented before travel restrictions were put in place. Before travel restriction and personal isolation were implemented, the transmission rate of undocumented infections was a little more than half that of the known cases. However, because of their greater numbers, undocumented infections were the source for ∼80% of the documented cases. Immediately after travel restrictions were imposed, ∼65% of cases were documented. These findings help to explain the lightning-fast spread of this virus around the world. Science , this issue p. 489
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Early in the pandemic, many deaths due to COVID-19 among older individuals in high-income countries, particularly in long-term care facilities, are unlikely to have been attributed to COVID-19.
“early in the pandemic, before tests were widely available, many deaths due to COVID-19 among older individuals in high-income countries, particularly in long-term care facilities, are unlikely to have been attributed to COVID-19”
From Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020–21 (2022) · cites this paper as [3]
The Outlook reports undocumented infections in China before 23 January 2020, not deaths that went unattributed to COVID-19. Searches for deaths, older people, and long-term care found no passage addressing the claim about mortality attribution in high-income countries. (LLM, conf=0.78)
Experimental — accuracy not yet measuredHealth-care reporting systems generally do not list COVID-19 as the cause of death without a positive SARS-CoV-2 test, so deaths due to COVID-19 will be missed in official counts in locations with low testing capacity.
“health-care reporting systems generally do not list COVID-19 as the cause of death without a positive SARS-CoV-2 test, and thus deaths due to COVID-19 will be missed in official counts in locations with low testing capacity”
From Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020–21 (2022) · cites this paper as [3]
The Outlook discusses undocumented infections and how testing and reporting practices affect infection documentation. Searches for death certification, positive-test requirements, and mortality reporting found no passage addressing whether COVID-19 deaths are omitted from official counts in low-testing locations. (LLM, conf=0.76)
Experimental — accuracy not yet measuredInsufficient testing capacity for SARS-CoV-2, misclassification of causes of death, and definitional inconsistencies in counting COVID-19 deaths make the true toll of COVID-19 infections hard to estimate.
“Insufficient testing capacity for SARS-CoV-2, the causative pathogen of coronavirus disease, especially during the early phases of the pandemic, misclassification of causes of death and definitional inconsistencies in counting COVID-19 deaths across different sources make the true toll of COVID-19 infections hard to estimate.2 3”
From Estimating the burden of the COVID-19 pandemic on mortality, life expectancy and lifespan inequality in England and Wales: a population-level analysis (2021) · cites this paper as [2]
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- Authors
- Li, R., Pei, S., Chen, B., Song, Y., Zhang, T., Yang, W., & Shaman, J.
- Affiliations
- Imperial College London (GB)
- Columbia University (US)
- Department of Land, Air and Water Resources, University of California, Davis, Davis, CA 95616, USA.
- University of Hong Kong (HK)
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 10084, P. R. China.
- Corresponding author
- Pei, Sen
- Shaman, Jeffrey
- Venue
- Science
- Published
- 2020-05
- Volume
- 368
- Issue
- 6490
- Pages
- 489-493
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC7164387
- OpenAlex
- W3012284084
- Cited by
- 3,633
- References
- 31
- Citations by year
- 2026: 59 · 2025: 89 · 2024: 135 · 2023: 272 · 2022: 508 · 2021: 1009 · 2020: 1556 · 2018: 1 · 2013: 1
- FWCI
- 132.90
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Science (publishedVersion, cc-by)
- medRxiv (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- The HKU Scholars Hub (University of Hong Kong) (publishedVersion)
- PubMed Central (submittedVersion, cc-by)
- Spiral (Imperial College London) (submittedVersion, cc-by)
- Spiral (Imperial College London) (submittedVersion)
- Funders
- National Institute of General Medical Sciences (GM110748)
- National Institute of Allergy and Infectious Diseases (AI145883)
- Topics
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Mathematical and Theoretical Epidemiology and Ecology Models
- Keywords
- SARS-CoV-2, COVID-19 pandemic, infectious disease transmission, transmission rate, metapopulation model, mobility data, epidemic spread, travel restrictions, epidemic containment, China
- MeSH terms
- Betacoronavirus, Missed Diagnosis, COVID-19, SARS-CoV-2, Bayes Theorem, China, Humans, Models, Theoretical, Pneumonia, Viral, Travel, Prevalence, Coronavirus Infections, Pandemics, Epidemiological Monitoring
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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