Dataset Information

Patient delay in a coronavirus disease 2019 (COVID-19) outbreak in Tianjin, China from January to February 2020.

ABSTRACT:

Background

Patient delay of COVID-19 patients occurs frequently, which poses a challenge to the overall epidemic situation. In this study, we aimed to evaluate the extent of patient delay, explore its factors, and investigate the effects of patient interval on epidemic situation.

Methods

A retrospective cohort study was conducted with 136 COVID-19 patients in Tianjin, China. Factors associated with patient delay were explored using logistic regression models. The relationship was investigated by spearman correlation analysis and mean absolute error between patient interval of lagging days and epidemic situation.

Results

The factors associated with patient delay of COVID-19 patients were mainly the imported cases, the first presentation to a tertiary hospital, close contacts and spatial accessibility to fever clinic. The longer the patient intervals of lagging days, the greater the number of new-onset and confirmed cases in 3-4 and 5-7 days after the first day symptoms, respectively.

Conclusions

Identification and quarantine of close contacts, promoting the spatial accessibility to fever clinics and creating public awareness are crucial to shortening patient delays to flat the curve for COVID-19.

SUBMITTER: Cao C

PROVIDER: S-EPMC8580809 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundSince the first cluster of cases was identified in Wuhan City, China, in December 2019, coronavirus disease 2019 (COVID-19) rapidly spreads globally. Scientists have made strides in estimating key transmission and epidemiological parameters. In particular, accumulating evidence points to a substantial fraction of asymptomatic or subclinical infections, which influences our understanding of the transmission potential and severity of this emerging disease. In this study, we derive estimates of the transmissibility and virulence of COVID-19 in Wuhan City, China, by reconstructing the underlying transmission dynamics using multiple data sources.MethodsWe employ statistical methods and publicly available epidemiological datasets to jointly derive estimates of transmissibility and severity associated with the novel coronavirus. For this purpose, the daily series of laboratory-confirmed COVID-19 cases and deaths in Wuhan City together with epidemiological data of Japanese repatriated from Wuhan City on board government-chartered flights were integrated into our analysis.ResultsOur posterior estimates of basic reproduction number (R) in Wuhan City, China, in 2019-2020 reached values at 3.49 (95% CrI 3.39-3.62) with a mean serial interval of 6.0 days, and the enhanced public health intervention after January 23 in 2020 was associated with a significantly reduced R at 0.84 (95% CrI 0.81-0.88), with the total number of infections (i.e., cumulative infections) estimated at 1,906,634 (95% CrI 1,373,500-2,651,124) in Wuhan City, elevating the overall proportion of infected individuals to 19.1% (95% CrI 13.5-26.6%). We also estimated the most recent crude infection fatality ratio (IFR) and time-delay adjusted IFR at 0.04% (95% CrI 0.03-0.06%) and 0.12% (95% CrI 0.08-0.17%), respectively, estimates that are substantially smaller than the crude CFR estimated at 4.06%.ConclusionsWe have estimated key epidemiological parameters of the transmissibility and virulence of COVID-19 in Wuhan, China, during January-February 2020 using an ecological modeling approach that is suitable to infer epidemiological parameters with quantified uncertainty from partial observations collected by surveillance systems. Our estimate of time-delay adjusted IFR falls in the range of the median IFR estimates based on multiple serological studies conducted in several areas of the world.

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