ArticleInternational health2021
Wuhan's experience in curbing the spread of coronavirus disease (COVID-19).
Article in International health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
2 citing papers in PubMed, 4 citations in OpenAlex.
- Review
- Spatio-temporal characteristics and control strategies in the early period of COVID-19 spread: a case study of the mainland China.Environmental science and pollution research international · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSince December 2019, coronavirus disease (COVID-19) has affected over 50 000 people in Wuhan, China. However, the number of daily infection cases, hospitalization rate, lag time from onset to diagnosis date and their associations with measures introduced to slow down the spread of COVID-19 have not been fully explored.
methodsThis study recruited 6872 COVID-19 patients in the Wuchang district, Wuhan. All of the patients had an onset date from 21 December 2019 to 23 February 2020. The overall and weekly hospitalization rate and lag time from onset to diagnosis date were calculated. The number of daily infections was estimated by the back-projection method based on the number of daily onset cases. Their association with major government reactions and measures was analyzed narratively.
resultsThe overall hospitalization rate was 45.9% (95% CI 44.7 to 47.1%) and the mean lag time from onset to diagnosis was 11.1±7.4 d. The estimated infection curve was constructed for the period from 14 December 2019 to 23 February 2020. Raising public awareness regarding self-protecting and social distancing, as well as the provision of timely testing and inpatient services, were coincident with the decline in the daily number of infections.
conclusionEarly public awareness, early identification and early quarantine, supported by appropriate infrastructure, are important elements for containing the spread of COVID-19 in the community.
Indexed as
Identifiers
What OpenQuestion holds
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.