Serial Interval of COVID-19 among Publicly Reported Confirmed Cases.
Ontology highlight
ABSTRACT:
SUBMITTER: Du Z
PROVIDER: S-EPMC7258488 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:We estimate the distribution of serial intervals for 468 confirmed cases of COVID-19 reported in 93 Chinese cities by February 8, 2020. The mean and standard deviation are 3.96 (95% CI 3.53-4.39) and 4.75 (95% CI 4.46-5.07) days, respectively, with 12.6% of reports indicating pre-symptomatic transmission.
| S-EPMC7239082 | biostudies-literature
Project description:As a novel coronavirus (COVID-19) continues to spread widely and claim lives worldwide, its transmission characteristics remain uncertain. Here, we present and analyze the serial intervals-the time period between the onset of symptoms in an index (infector) case and the onset of symptoms in a secondary (infectee) case-of 339 confirmed cases of COVID-19 identified from 264 cities in mainland China prior to February 19, 2020. Here, we provide the complete dataset in both English and Chinese to support further COVID-19 research and modeling efforts.
| S-EPMC7276043 | biostudies-literature
Project description:Background:A novel human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified in China in December 2019. There is limited support for many of its key epidemiologic features, including the incubation period for clinical disease (coronavirus disease 2019 [COVID-19]), which has important implications for surveillance and control activities. Objective:To estimate the length of the incubation period of COVID-19 and describe its public health implications. Design:Pooled analysis of confirmed COVID-19 cases reported between 4 January 2020 and 24 February 2020. Setting:News reports and press releases from 50 provinces, regions, and countries outside Wuhan, Hubei province, China. Participants:Persons with confirmed SARS-CoV-2 infection outside Hubei province, China. Measurements:Patient demographic characteristics and dates and times of possible exposure, symptom onset, fever onset, and hospitalization. Results:There were 181 confirmed cases with identifiable exposure and symptom onset windows to estimate the incubation period of COVID-19. The median incubation period was estimated to be 5.1 days (95% CI, 4.5 to 5.8 days), and 97.5% of those who develop symptoms will do so within 11.5 days (CI, 8.2 to 15.6 days) of infection. These estimates imply that, under conservative assumptions, 101 out of every 10 000 cases (99th percentile, 482) will develop symptoms after 14 days of active monitoring or quarantine. Limitation:Publicly reported cases may overrepresent severe cases, the incubation period for which may differ from that of mild cases. Conclusion:This work provides additional evidence for a median incubation period for COVID-19 of approximately 5 days, similar to SARS. Our results support current proposals for the length of quarantine or active monitoring of persons potentially exposed to SARS-CoV-2, although longer monitoring periods might be justified in extreme cases. Primary Funding Source:U.S. Centers for Disease Control and Prevention, National Institute of Allergy and Infectious Diseases, National Institute of General Medical Sciences, and Alexander von Humboldt Foundation.
| S-EPMC7081172 | biostudies-literature
Project description:OBJECTIVE:To estimate the serial interval of novel coronavirus (COVID-19) from information on 28 infector-infectee pairs. METHODS:We collected dates of illness onset for primary cases (infectors) and secondary cases (infectees) from published research articles and case investigation reports. We subjectively ranked the credibility of the data and performed analyses on both the full dataset (n = 28) and a subset of pairs with highest certainty in reporting (n = 18). In addition, we adjust for right truncation of the data as the epidemic is still in its growth phase. RESULTS:Accounting for right truncation and analyzing all pairs, we estimated the median serial interval at 4.0 days (95% credible interval [CrI]: 3.1, 4.9). Limiting our data to only the most certain pairs, the median serial interval was estimated at 4.6 days (95% CrI: 3.5, 5.9). CONCLUSIONS:The serial interval of COVID-19 is close to or shorter than its median incubation period. This suggests that a substantial proportion of secondary transmission may occur prior to illness onset. The COVID-19 serial interval is also shorter than the serial interval of severe acute respiratory syndrome (SARS), indicating that calculations made using the SARS serial interval may introduce bias.
| S-EPMC7128842 | biostudies-literature
Project description:The new Coronavirus (COVID-19) is an emerging disease responsible for infecting millions of people since the first notification until nowadays. Developing efficient short-term forecasting models allow forecasting the number of future cases. In this context, it is possible to develop strategic planning in the public health system to avoid deaths. In this paper, autoregressive integrated moving average (ARIMA), cubist regression (CUBIST), random forest (RF), ridge regression (RIDGE), support vector regression (SVR), and stacking-ensemble learning are evaluated in the task of time series forecasting with one, three, and six-days ahead the COVID-19 cumulative confirmed cases in ten Brazilian states with a high daily incidence. In the stacking-ensemble learning approach, the CUBIST regression, RF, RIDGE, and SVR models are adopted as base-learners and Gaussian process (GP) as meta-learner. The models' effectiveness is evaluated based on the improvement index, mean absolute error, and symmetric mean absolute percentage error criteria. In most of the cases, the SVR and stacking-ensemble learning reach a better performance regarding adopted criteria than compared models. In general, the developed models can generate accurate forecasting, achieving errors in a range of 0.87%-3.51%, 1.02%-5.63%, and 0.95%-6.90% in one, three, and six-days-ahead, respectively. The ranking of models, from the best to the worst regarding accuracy, in all scenarios is SVR, stacking-ensemble learning, ARIMA, CUBIST, RIDGE, and RF models. The use of evaluated models is recommended to forecasting and monitor the ongoing growth of COVID-19 cases, once these models can assist the managers in the decision-making support systems.
| S-EPMC7252162 | biostudies-literature
Project description:COVID-19 is an emerging respiratory infectious disease caused by the coronavirus SARS-CoV-2. It was first reported on in early December 2019 in Wuhan, China and within three months spread as a pandemic around the whole globe. Here, we study macro-epidemiological patterns along the time course of the COVID-19 pandemic. We compute the distribution of confirmed COVID-19 cases and deaths for countries worldwide and for counties in the US and show that both distributions follow a truncated power-law over five orders of magnitude. We are able to explain the origin of this scaling behavior as a dual-scale process: the large-scale spread of the virus between countries and the small-scale accumulation of case numbers within each country. Assuming exponential growth on both scales, the critical exponent of the power-law is determined by the ratio of large-scale to small-scale growth rates. We confirm this theory in numerical simulations in a simple meta-population model, describing the epidemic spread in a network of interconnected countries. Our theory gives a mechanistic explanation why most COVID-19 cases occurred within a few epicenters, at least in the initial phase of the outbreak. By combining real world data, modeling, and numerical simulations, we make the case that the distribution of epidemic prevalence might follow universal rules.
| S-EPMC7519452 | biostudies-literature
Project description:A novel coronavirus disease, designated as COVID-19, has become a pandemic worldwide. This study aims to estimate the incubation period and serial interval of COVID-19. We collected contact tracing data in a municipality in Hubei province during a full outbreak period. The date of infection and infector-infectee pairs were inferred from the history of travel in Wuhan or exposed to confirmed cases. The incubation periods and serial intervals were estimated using parametric accelerated failure time models, accounting for interval censoring of the exposures. Our estimated median incubation period of COVID-19 is 5.4 days (bootstrapped 95% confidence interval (CI) 4.8-6.0), and the 2.5th and 97.5th percentiles are 1 and 15 days, respectively; while the estimated serial interval of COVID-19 falls within the range of -4 to 13 days with 95% confidence and has a median of 4.6 days (95% CI 3.7-5.5). Ninety-five per cent of symptomatic cases showed symptoms by 13.7 days (95% CI 12.5-14.9). The incubation periods and serial intervals were not significantly different between male and female, and among age groups. Our results suggest a considerable proportion of secondary transmission occurred prior to symptom onset. And the current practice of 14-day quarantine period in many regions is reasonable.
| S-EPMC7324649 | biostudies-literature
Project description:Background: The purpose of the study was to compare trends in the progression of COVID-19 among South Asian countries with more developed Western countries. Methods: COVID-19 data from South Asian countries were used for this observational study. Data were taken up to April 21, 2020 from the outbreak of the COVID-19. Four of the seven countries met the inclusion criteria and were included in the analysis. Results: An exponential increase in the average number of weekly cases was reported after the fifth week following the first case. The correlation between reported cases and tests was found to be strong and significant (r=0.90, p=0.037). However, on average, 315.25 tests per million population were performed, which was at least 12 times lower than the number of tests performed in countries with a large number of COVID-19 cases. Conclusions: At present, the number of confirmed cases from South Asia was found to be significantly lower than in Western countries. Hence, an increase in the strength of performing diagnostic tests is highly recommended. Strict measures are required to make the people of these countries follow the instructions of social distancing and comply with preventive measures.
| S-EPMC7905493 | biostudies-literature
Project description:Some occupational sectors, such as human health and care, food service, cultural and sport activities, have been associated with a higher risk of SARS-CoV-2 infection than other sectors. To curb the spread of SARS-CoV-2, it is preferable to apply targeted non-pharmaceutical interventions on selected economic sectors, rather than a full lockdown. However, the effect of these general and sector-specific interventions on the virus circulation has only been sparsely studied. We assess the COVID-19 incidence under different levels of non-pharmaceutical interventions per economic activity during the autumn 2020 wave in Belgium. The 14-day incidence of confirmed COVID-19 cases per the Statistical Classification of Economic Activities in the European Community (NACE-BEL) sector is modelled by a longitudinal Gaussian-Gaussian two-stage approach. This is based on exhaustive data on all employees in all sectors. In the presence of sanitary protocols and minimal non-pharmaceutical interventions, many sectors with close contact with others show considerably higher COVID-19 14-day incidences than other sectors. The effect of stricter non-pharmaceutical interventions in the general population and non-essential sectors is seen in the timing of the peak incidence and the width and height of the post-peak incidence. In most sectors incidences returned to higher levels after the peak than before and this decrease took longer for the health and care sector. Sanitary protocols for close proximity occupations may be sufficient during periods of low-level virus circulation, but progressively less with increasing circulation. Stricter general and sector-specific non-pharmaceutical interventions adequately decrease COVID-19 incidences, even in close proximity in essential sectors under solely sanitary protocols.
| S-EPMC8656663 | biostudies-literature
Project description:To date, there is poor evidence on the transmission of infection in individuals handling the bodies of deceased persons infected with SARS-CoV-2 and in particular, during autopsies. The aim of this study was to demonstrate that when appropriate strategies are adopted autopsy is a safe procedure with a minimal infection risk for all subjects involved (pathologists, technical personnel, and others) when proper strategies are adopted. We performed 16 autopsies on cadavers of persons who had died with confirmed COVID-19 with different post-mortem intervals (PMI). To confirm the presence of SARS-CoV-2 RNA, for each autopsy, 2 swabs were sampled from lungs, while to evaluate environmental contamination, 11 swabs were taken at three different times: T0 (before autopsy), T1 (at the end of the autopsy, without removing the corpse), and T2 (after cleaning and disinfecting the autopsy room). Specifically, 2 swabs were sampled on face shields used by each pathologist, and 4 swabs were collected on the autopsy table; 4 swabs were also collected from walls and 1 from floor. Lung swabs confirmed the presence of SARS-CoV-2 RNA in all cases. Environmental swabs, collected at T0 and T2 were all negative, while swabs sampled at T1 were shown to be positive. Interestingly, no association was shown between PMI length and environmental contamination. Infection control strategies for safe management of clinical forensic autopsies of bodies with suspected or confirmed COVID-19 are also described.
| S-EPMC7999752 | biostudies-literature