ArticleJournal of data science : JDS2020
Meta-analysis of several epidemic characteristics of COVID-19.
Article in Journal of data science : JDS, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.
What it found
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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
7 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Meta-analysis of the severe acute respiratory syndrome coronavirus 2 serial intervals and the impact of parameter uncertainty on the coronavirus disease 2019 reproduction number.Statistical methods in medical research · 2022Pooled it
- Serial interval and incubation period of COVID-19: a systematic review and meta-analysis.BMC infectious diseases · 2021Pooled it
- Dynamic transmission modeling of COVID-19 to support decision-making in Brazil: A scoping review in the pre-vaccine era.PLOS global public health · 2023Article
- Preventing COVID-19 outbreaks through surveillance testing in healthcare facilities: a modelling study.BMC infectious diseases · 2022Article
- Epidemiologic Parameters for COVID-19: A Systematic Review and Meta-Analysis.Medical journal of the Islamic Republic of Iran · 2022Review
- In pursuit of the right tail for the COVID-19 incubation period.Public health · 2021Review
- Transmission Dynamics of the COVID-19 Epidemic at the District Level in India: Prospective Observational Study.JMIR public health and surveillance · 2020Observational
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
As the COVID-19 pandemic has strongly disrupted people's daily work and life, a great amount of scientific research has been conducted to understand the key characteristics of this new epidemic. In this manuscript, we focus on four crucial epidemic metrics with regard to the COVID-19, namely the basic reproduction number, the incubation period, the serial interval and the epidemic doubling time. We collect relevant studies based on the COVID-19 data in China and conduct a meta-analysis to obtain pooled estimates on the four metrics. From the summary results, we conclude that the COVID-19 has stronger transmissibility than SARS, implying that stringent public health strategies are necessary.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.