Meta-analysis of several epidemic characteristics of COVID-19

Zhang, Panpan; Wang, Tiandong; Xie, Sharon X.

doi:10.1101/2020.05.31.20118448

Cited by 14 publications

(19 citation statements)

References 59 publications

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“…There are only a small number of records from the FF100 data which suggest the mean of the incubation period is between 1.8 and 2 days. The data from the Open COVID-19 Data Working Group suggests the incubation period is longer with a mean around 5.5 days depending on the distribution chosen, and this agrees better with other estimates in the literature 13,24,25 . The best fit to the Open COVID-19 data is obtained with a log normal distribution as seen in Table 2 (Full details of the fitting parameters are in Supplemental table 3).…”

Section: Resultssupporting

confidence: 86%

“…Firstly we conducted a literature review for studies that describe serial interval estimates using PubMed and the search terms "(SARS-CoV-2 or COVID-19) and 'Serial interval' " and reviewed the abstracts of relevant original research papers. These were compared to papers reported on the MIDAS Online Portal for COVID-19 Modeling Research 12 , and with existing meta-analyses 13 . From these papers, serial interval mean and standard deviation estimates were extracted, along with information about assumed statistical distributions, and the sample size of the study.…”

Section: Serial Interval Estimationmentioning

confidence: 99%

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Meta-analysis of the SARS-CoV-2 serial interval and the impact of parameter uncertainty on the COVID-19 reproduction number

Challen

Brooks-Pollock

Tsaneva-Atanasova

et al. 2020

Preprint

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The serial interval of an infectious disease, commonly interpreted as the time between onset of symptoms in sequentially infected individuals within a chain of transmission, is a key epidemiological quantity involved in estimating the reproduction number. The serial interval is closely related to other key quantities, including the incubation period, the generation interval (the time between sequential infections) and time delays between infection and the observations associated with monitoring an outbreak such as confirmed cases, hospital admissions and deaths. Estimates of these quantities are often based on small data sets from early contact tracing and are subject to considerable uncertainty, which is especially true for early COVID-19 data. In this paper we estimate these key quantities in the context of COVID-19 for the UK, including a meta-analysis of early estimates of the serial interval. We estimate distributions for the serial interval with a mean 5.6 (95% CrI 5.1–6.2) and SD 4.2 (95% CrI 3.9–4.6) days (empirical distribution), the generation interval with a mean 4.8 (95% CrI 4.3–5.41) and SD 1.7 (95% CrI 1.0–2.6) days (fitted gamma distribution), and the incubation period with a mean 5.5 (95% CrI 5.1–5.8) and SD 4.9 (95% CrI 4.5–5.3) days (fitted log normal distribution). We quantify the impact of the uncertainty surrounding the serial interval, generation interval, incubation period and time delays, on the subsequent estimation of the reproduction number, when pragmatic and more formal approaches are taken. These estimates place empirical bounds on the estimates of most relevant model parameters and are expected to contribute to modelling COVID-19 transmission.

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Section: Resultssupporting

confidence: 86%

Section: Serial Interval Estimationmentioning

confidence: 99%

Meta-analysis of the SARS-CoV-2 serial interval and the impact of parameter uncertainty on the COVID-19 reproduction number

Challen

Brooks-Pollock

Tsaneva-Atanasova

et al. 2020

Preprint

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“…More details of those 71 studies were summarized in Supplementary Table 3. The pooled mean incubation period calculated in this paper is more prolonged than Malahat`s meta-analysis (5.68 days) [23] and Zhang (5.35 days) [24]. The reason for this inconsistency may be due to the incomprehensiveness of literature inclusion in those two papers.…”

Section: Discussioncontrasting

confidence: 64%

Estimation of Incubation Period and Serial Interval for SARS-CoV-2 in Jiangxi, China, and an Updated Meta-Analysis

Zhang

Ding

Zeng

et al. 2021

J Infect Dev Ctries

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Introduction: This paper aims to estimate the incubation period and serial intervals for SARS-CoV-2 based on confirmed cases in Jiangxi Province of China and meta-analysis method. Methodology: Distributions of incubation period and serial interval of Jiangxi epidemic data were fitted by “fitdistrplus” package of R software, and the meta-analysis was conducted by “meta” package of R software. Results: Based on the epidemic data of Jiangxi, we found the median days of incubation period and serial interval were 5.9 days [IQR: 3.8 – 8.6] and 5.7 days [IQR: 3.6 – 8.3], respectively. The median days of the infectivity period at pre-symptomatic was 1.7 days [IQR: 1.1 – 2.4]. The meta-analysis based on 64 papers showed the pooled means of the incubation period and serial interval were 6.25 days (95% CrI: 5.75 – 6.75) and 5.15 days (95% CrI: 4.73 – 5.57), respectively. Conclusions: Our results contribute to a better understanding of COVID-19 and provide useful parameters for modelling the dynamics of disease transmission. The serial interval is shorter than the incubation period, which indicates that the patients are infectious at pre-symptomatic period, and isolation of detected cases alone is likely to be difficult to halt the spread of SARS-CoV-2.

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“…Our observation of the mean serial interval fell within a range of 4 to 8 days, as estimated by a meta-analysis of 7 studies conducted during the early phase of the COVID-19 pandemic [ 25 ]. Another meta-analysis including studies only from China estimated a range of serial intervals from 4.10 to 7.5 days [ 26 ]. Our experience suggests that the median and 95% CI estimates of the serial interval should be reported alongside the mean and SD, as the latter approach is more susceptible to influence by extreme values.…”

Section: Discussionmentioning

confidence: 99%

Transmission Dynamics of the COVID-19 Epidemic at the District Level in India: Prospective Observational Study

Saurabh¹,

Verma²,

Gautam³

et al. 2020

JMIR Public Health Surveill

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Background On March 9, 2020, the first COVID-19 case was reported in Jodhpur, Rajasthan, in the northwestern part of India. Understanding the epidemiology of COVID-19 at a local level is becoming increasingly important to guide measures to control the pandemic. Objective The aim of this study was to estimate the serial interval and basic reproduction number (R0) to understand the transmission dynamics of the COVID-19 outbreak at a district level. We used standard mathematical modeling approaches to assess the utility of these factors in determining the effectiveness of COVID-19 responses and projecting the size of the epidemic. Methods Contact tracing of individuals infected with SARS-CoV-2 was performed to obtain the serial intervals. The median and 95th percentile values of the SARS-CoV-2 serial interval were obtained from the best fits with the weibull, log-normal, log-logistic, gamma, and generalized gamma distributions. Aggregate and instantaneous R0 values were derived with different methods using the EarlyR and EpiEstim packages in R software. Results The median and 95th percentile values of the serial interval were 5.23 days (95% CI 4.72-5.79) and 13.20 days (95% CI 10.90-18.18), respectively. R0 during the first 30 days of the outbreak was 1.62 (95% CI 1.07-2.17), which subsequently decreased to 1.15 (95% CI 1.09-1.21). The peak instantaneous R0 values obtained using a Poisson process developed by Jombert et al were 6.53 (95% CI 2.12-13.38) and 3.43 (95% CI 1.71-5.74) for sliding time windows of 7 and 14 days, respectively. The peak R0 values obtained using the method by Wallinga and Teunis were 2.96 (95% CI 2.52-3.36) and 2.92 (95% CI 2.65-3.22) for sliding time windows of 7 and 14 days, respectively. R0 values of 1.21 (95% CI 1.09-1.34) and 1.12 (95% CI 1.03-1.21) for the 7- and 14-day sliding time windows, respectively, were obtained on July 6, 2020, using method by Jombert et al. Using the method by Wallinga and Teunis, values of 0.32 (95% CI 0.27-0.36) and 0.61 (95% CI 0.58-0.63) were obtained for the 7- and 14-day sliding time windows, respectively. The projection of cases over the next month was 2131 (95% CI 1799-2462). Reductions of transmission by 25% and 50% corresponding to reasonable and aggressive control measures could lead to 58.7% and 84.0% reductions in epidemic size, respectively. Conclusions The projected transmission reductions indicate that strengthening control measures could lead to proportionate reductions of the size of the COVID-19 epidemic. Time-dependent instantaneous R0 estimation based on the process by Jombart et al was found to be better suited for guiding COVID-19 response at the district level than overall R0 or instantaneous R0 estimation by the Wallinga and Teunis method. A data-driven approach at the local level is proposed to be useful in guiding public health strategy and surge capacity planning.

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Meta-analysis of several epidemic characteristics of COVID-19

Cited by 14 publications

References 59 publications

Meta-analysis of the SARS-CoV-2 serial interval and the impact of parameter uncertainty on the COVID-19 reproduction number

Meta-analysis of the SARS-CoV-2 serial interval and the impact of parameter uncertainty on the COVID-19 reproduction number

Estimation of Incubation Period and Serial Interval for SARS-CoV-2 in Jiangxi, China, and an Updated Meta-Analysis

Transmission Dynamics of the COVID-19 Epidemic at the District Level in India: Prospective Observational Study

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