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2020, PLOS ONE
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9 pages
1 file
Objective To estimate the incubation period of Vietnamese confirmed COVID-19 cases. Methods Only confirmed COVID-19 cases who are Vietnamese and locally infected with available data on date of symptom onset and clearly defined window of possible SARS-CoV-2 exposure were included. We used three parametric forms with Hamiltonian Monte Carlo method for Bayesian Inference to estimate incubation period for Vietnamese COVID-19 cases. Leave-one-out Information Criterion was used to assess the performance of three models. Results A total of 19 cases identified from 23 Jan 2020 to 13 April 2020 was included in our analysis. Average incubation periods estimated using different distribution model ranged from 6.0 days to 6.4 days with the Weibull distribution demonstrated the best fit to the data. The estimated mean of incubation period using Weibull distribution model was 6.4 days (95% credible interval (CrI): 4.89–8.5), standard deviation (SD) was 3.05 (95%CrI 3.05–5.30), median was 5.6, rang...
2020
Objective: To estimate the incubation period of Vietnamese confirmed COVID−19 cases. Methods: Only confirmed COVID−19 cases who are Vietnamese and locally infected with available data on date of symptom onset and clearly defined window of possible SARS−CoV−2 exposure were included. We used three parametric forms with Hamiltonian Monte Carlo method for Bayesian Inference to estimate incubation period for Vietnamese COVID−19 cases. Leave-one-out Information Criterion was used to assess the performance of three models. Results: A total of 19 cases identified from 23 Jan 2020 to 13 April 2020 was included in our analysis. Average incubation periods estimated using different distribution model ranged from 6.0 days to 6.4 days with the Weibull distribution demonstrated the best fit to the data. The estimated mean of incubation period using Weibull distribution model was 6.4 days (95% credible interval (CI): 4.89 − 8.5), standard deviation (SD) was 3.05 (95%CI 3.05 − 5.30), median was 5.6,...
International Journal of Engineering Research and Technology (IJERT), 2021
https://www.ijert.org/estimating-the-incubation-period-of-covid-19-sars-cov-2-virus https://www.ijert.org/research/estimating-the-incubation-period-of-covid-19-sars-cov-2-virus-IJERTCONV9IS02006.pdf Our intention is to estimate the incubation period for the novel coronavirus disease (COVID-19 / SARS-CoV-2) that emerged in Wuhan, Hubei province, China, in 2019. We also wish to undertake a comparison of our results with few other published studies. Understanding the incubation period is very important for health authorities as it allows them to introduce more effective quarantine systems for people suspected of carrying the virus, as a way of controlling and hopefully preventing the spread of the virus. As part of this study, 195 cases have been chosen for which data was available from various sources. This includes individuals of 22 countries. Their ages range from 2 to 85 years of age with a median age of 45 years. 57% of the people were male while there were 43% were female. The data has only those cases where information about the time interval of exposure and symptom onset is available. Here we have tried to identify the incubation period using the following three methods: Weibull Distribution, Log-normal distribution, Kaplan Meier Distribution. These results have also been compared with the incubation periods resulting out of few other prominent studies.
2020
The incubation period, or the time from infection to symptom onset of COVID-19 has been usually estimated using data collected through interviews with cases and their contacts. However, this estimation is influenced by uncertainty in recalling effort of exposure time. We propose a novel method that uses viral load data collected over time since hospitalization, hindcasting the timing of infection with a mathematical model for viral dynamics. As an example, we used the reported viral load data from multiple countries (Singapore, China, Germany, France, and Korea) and estimated the incubation period. The median, 2.5, and 97.5 percentiles of the incubation period were 5.23 days (95% CI: 5.17, 5.25), 3.29 days (3.25, 3.37), and 8.22 days (8.02, 8.46), respectively, which are comparable to the values estimated in previous studies. Using viral load to estimate the incubation period might be a useful approach especially when impractical to directly observe the infection event.
Objectives: Amid the continuing spread of the novel coronavirus (COVID-19), the incubation period of COVID-19 should be regularly re-assessed as more information is available upon the increase in reported cases. The present work estimated the distribution of incubation periods of patients infected in and outside Hubei province of China. Methods: Clinical data were collected from the individual cases reported by the media as they were not fully available on the official pages of the Chinese health authorities. MLE was used to estimate the distributions of the incubation period. Results: It was found that the incubation period of patients with no travel history to Hubei was longer and more volatile. Conclusion: It is recommended that the duration of quarantine should be extended to at least 3 weeks.
Journal of Infection and Public Health, 2021
Background: A novel coronavirus (COVID-19) has taken the world by storm. The disease has spread very swiftly worldwide. A timely clue which includes the estimation of the incubation period among COVID-19 patients can allow governments and healthcare authorities to act accordingly. Objectives: to undertake a review and critical appraisal of all published/preprint reports that offer an estimation of incubation periods for COVID-19. Eligibility criteria: This research looked for all relevant published articles between the dates of December 1, 2019, and April 25, 2020, i.e. those that were related to the COVID-19 incubation period. Papers were included if they were written in English, and involved human participants. Papers were excluded if they were not original (e.g. reviews, editorials, letters, commentaries, or duplications). Sources of evidence: COVID-19 Open Research Dataset supplied by Georgetown's Centre for Security and Emerging Technology as well as PubMed and Embase via Arxiv, medRxiv, and bioRxiv. Charting methods: A data-charting form was jointly developed by the two reviewers (NZ and EA), to determine which variables to extract. The two reviewers independently charted the data, discussed the results, and updated the data-charting form. Results and conclusions: screening was undertaken 44,000 articles with a final selection of 25 studies referring to 18 different experimental projects related to the estimation of the incubation period of COVID-19. The majority of extant published estimates offer empirical evidence showing that the incubation period for the virus is a mean of 7.8 days, with a median of 5.01 days, which falls into the ranges proposed by the WHO (0 to 14 days) and the ECDC (2 to 12 days). Nevertheless, a number of authors proposed that quarantine time should be a minimum of 14 days and that for estimates of mortality risks a median time delay of 13 days between illness and mortality should be under consideration. It is unclear as to whether any correlation exists between the age of patients and the length of time they incubate the virus.
2020
Objective: to undertake a review and critical appraisal of all published/preprint reports that offer an estimation of incubation periods for novel coronavirus (COVID-19). Design: a rapid and systematic review/critical appraisal Data sources: COVID-19 Open Research Dataset supplied by Georgetown's Centre for Security and Emerging Technology as well as PubMed and Embase via Arxiv, medRxiv, and bioRxiv. Results: screening was undertaken 44,000 articles with a final selection of 25 studies referring to 18 different experimental projects related to the estimation of the incubation period of COVID-19. Findings: The majority of extant published estimates offer empirical evidence showing that the incubation period for the virus is a mean of 7.8 days, with a median of 5.01 days, which falls into the ranges proposed by the WHO (0 to 14 days) and the ECDC (2 to 12 days). Nevertheless, a number of authors proposed that quarantine time should be a minimum of 14 days and that for estimates of...
Frontiers in Public Health
BackgroundThe incubation period of the coronavirus disease 2019 (COVID-19) is estimated to vary by demographic factors and the COVID-19 epidemic periods.ObjectiveThis study examined the incubation period of the wild type of SARS-CoV-2 infections by the different age groups, gender, and epidemic periods in South Korea.MethodsWe collected COVID-19 patient data from the Korean public health authorities and estimated the incubation period by fitting three different distributions, including log-normal, gamma, and Weibull distributions, after stratification by gender and age groups. To identify any temporal impact on the incubation period, we divided the study period into two different epidemic periods (Period-1: 19 January−19 April 2020 and Period-2: 20 April−16 October 2020), and assessed for any differences.ResultsWe identified the log-normal as the best-fit model. The estimated median incubation period was 4.6 (95% CI: 3.9–4.9) days, and the 95th percentile was 11.7 (95% CI: 10.2–12.2...
Background Novel coronavirus (COVID-19) is a new strain of viruses that originated in China. In December 2019, a strange case of pneumonia was reported in Wuhan, which then was diagnosed to be COVID-19. In Singapore, the first positive case was reported on January 23, 2020. Aim This study aims to study the recovery time from COVID-19 in Singapore between January 23 and March 13, 2020. Methods It's a retrospective study from January 23 until March 13, 2020 for 187 cases with COVID-19 infection. Data of the instances were collected to identify the factors affecting the recovery time from COVID-19. Several parametric models were fitted and the best predictor model was selected using the Bayesian information criterion (BIC). Results Out of 187 patients, 96 (51.34%) were cured. The mean (±standard deviation) survival time was 9.40±7.17 days. Based on BIC, the exponential regression model was the weakest and the Weibull model was the best for fitting to data. According to the Weibull ...
2020
BackgroundsThe emerging virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a large outbreak of novel coronavirus disease (COVID-19) in Wuhan, China since December 2019. Based on the publicly available surveillance data, we identified 21 transmission chains in Hong Kong and estimated the serial interval (SI) of COVID-19.MethodsIndex cases were identified and reported after symptoms onset, and contact tracing was conducted to collect the data of the associated secondary cases. An interval censored likelihood framework is adopted to fit a Gamma distribution function to govern the SI of COVID-19.FindingsAssuming a Gamma distributed model, we estimated the mean of SI at 4.4 days (95%CI: 2.9−6.7) and SD of SI at 3.0 days (95%CI: 1.8−5.8) by using the information of all 21 transmission chains in Hong Kong.ConclusionThe SI of COVID-19 may be shorter than the preliminary estimates in previous works. Given the likelihood that SI could be shorter than the incubatio...
Future of Food: Journal of Food, Agriculture & Society, 2021
It is common to hear and read about climate change in the literature, media, and interpersonal discussions among farmers and environmental groups. Farmers' understanding of climate change differs amid these discussions because of individual experiences and perceptions after many years of farming. Rainfall is declining, and the temperature is rising are the common perceptions farmers hold on climate change which they see as adversely affecting agriculture. In moments of such adversity, farmers think about what adaptation measures to implement. The objectives of this study were to find out what farmers perceive as climate change, what they consider as the causes of the change, and how they adapt to climate change. Methods used for collecting data were administering questionnaires to farmers in six towns in the Yendi Municipality, obtaining information through focused group discussions, and talking to agricultural extension officers. Data analysis was done using Excel software. The results show farmers are aware that the climate is changing. The changes are perceived as a result of bad farming practices, including cutting down trees, the influence of supernatural forces in preventing rainfall, and changes in wind direction which deprive communities of rainfall. Adaptation measures to cope with climate change mentioned by the farmers include crop diversification to plant drought-resistant crops and diversify from high grass consuming ruminants to low grass consuming ones. The paper concludes that the government should assist farmers to adapt fully to climate change, otherwise, food security will be hampered.
Oldenburger Jahrbuch für Philosophie 2021/2022, 2024
Вестник Томского государственного университета. Филология. 2024. No 89. С. 152–170 | Tomsk State University Journal of Philology. 2024. 89. рр. 152–170, 2024
BMC Health Services Research, 2012
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