Summary Background The current outbreak of coronavirus disease 2019 (COVID-19) has quickly spread across countries and become a global crisis. However, one of the most important clinical characteristics in epidemiology, the distribution of the incubation period, remains unclear. Different estimates of the incubation period of COVID-19 were reported in recent published studies, but all have their own limitations. In this study, we propose a novel low-cost and accurate method to estimate the incubation distribution. Methods We have conducted a cross-sectional and forward follow-up study by identifying those asymptomatic individuals at their time of departure from Wuhan and then following them until their symptoms developed. The renewal process is hence adopted by considering the incubation period as a renewal and the duration between departure and symptom onset as a forward recurrence time. Under mild assumptions, the observations of selected forward times can be used to consistently estimate the parameters in the distribution of the incubation period. Such a method enhances the accuracy of estimation by reducing recall bias and utilizing the abundant and readily available forward time data. Findings The estimated distribution of forward time fits the observations in the collected data well. The estimated median of incubation period is 8·13 days (95% confidence interval [CI]: 7·37-8·91), the mean is 8·62 days (95% CI: 8·02-9·28), the 90th percentile is 14·65 days (95% CI: 14·00-15·26), and the 99th percentile is 20·59 days (95% CI: 19·47, 21·62). Compared with results in other studies, the incubation period estimated in this study is longer. Interpretation Based on the estimated incubation distribution in this study, about 10% of patients with COVID-19 would not develop symptoms until 14 days after infection. Further study of the incubation distribution is warranted to directly estimate the proportion with long incubation periods. Funding This research is supported by National Natural Science Foundation of China grant 8204100362 and Zhejiang University special scientific research fund for COVID-19 prevention and control. Research in context Evidence before this study Before the current outbreak of coronavirus disease (COVID-19) in China, there were two other coronaviruses that have caused major global epidemics over the last two decades. Severe acute respiratory syndrome (SARS) spread to 37 countries and caused 8424 cases and 919 deaths in 2002-03, while Middle East respiratory syndrome (MERS) spread to 27 countries, causing 2494 cases and 858 deaths worldwide to date. Precise knowledge of the incubation period is crucial for the prevention and control of these diseases. We have searched PubMed and preprint archives for articles published as of February 22, 2020, which contain information about these diseases by using the key words of “COVID-19”, “SARS”, “MERS”, “2019-nCoV”, “coronavirus”, and “incubation”. We have found 15 studies that estimated the distribution of the incubation period. There are four articles focused on COVID-19, five on MERS, and six on SARS. Most of these studies had limited sample sizes and were potentially influenced by recall bias. The estimates for mean, median, and percentiles of the incubation period from these articles are summarized in Table 1. Added value of this study In the absence of complete and robust contact-tracing data, we have inferred the distribution of the incubation period of COVID-19 from the durations between departure from Wuhan and symptom onset for the confirmed cases. More than 1000 cases were collected from publicly available data. The proposed approach has a solid theoretical foundation and enhances the accuracy of estimation by reducing recall bias and utilizing a large pool of samples. Implications of all the available evidence Based on our model, about 10% of patients with COVID-19 do not develop symptoms until 14 days after infection. Further study of individuals with long incubation periods is warranted.

/pdf/estimation-of-incubation-period-distribution-of-covid-19-4d23f9z54w.pdf

Estimation of incubation period distribution of COVID-19 using disease onset forward time: a novel cross-sectional and forward follow-up study.

Community quarantine is controversial, and the decision to use and prepare for it should be informed by specific quantitative evidence of benefit. Case-study reports on 2002-2004 SARS outbreaks have discussed the role of quarantine in the community in transmission. However, this literature has not yielded quantitative estimates of the reduction in secondary cases attributable to quarantine as would be seen in other areas of health policy and cost-effectiveness analysis. Using data from the 2003 Ontario, Canada, SARS outbreak, two novel expressions for the impact of quarantine are presented. Secondary Case Count Difference (SCCD) reflects reduction in the average number of transmissions arising from a SARS case in quarantine, relative to not in quarantine, at onset of symptoms. SCCD was estimated using Poisson and negative binomial regression models (with identity link function) comparing the number of secondary cases to each index case for quarantine relative to non-quarantined index cases. The inverse of this statistic is proposed as the number needed to quarantine (NNQ) to prevent one additional secondary transmission. Our estimated SCCD was 0.133 fewer secondary cases per quarantined versus non-quarantined index case; and a NNQ of 7.5 exposed individuals to be placed in community quarantine to prevent one additional case of transmission in the community. This analysis suggests quarantine can be an effective preventive measure, although these estimates lack statistical precision. Relative to other health policy areas, literature on quarantine tends to lack in quantitative expressions of effectiveness, or agreement on how best to report differences in outcomes attributable to control measure. We hope to further this discussion through presentation of means to calculate and express the impact of population control measures. The study of quarantine effectiveness presents several methodological and statistical challenges. Further research and discussion are needed to understand the costs and benefits of enacting quarantine, and this includes a discussion of how quantitative benefit should be communicated to decision-makers and the public, and evaluated.

/pdf/quantifying-the-impact-of-community-quarantine-on-sars-1fw5eldeh9.pdf

Quantifying the impact of community quarantine on SARS transmission in Ontario: estimation of secondary case count difference and number needed to quarantine

The incubation period of an infectious disease is the time interval between the exposure to a disease-causing infectious agent and the onset of symptomatic (clinical) disease. The incubation period of a disease can be very variable among individuals and a single number, such as the mean or median incubation period, does not reveal the significant heterogeneity in incubation periods in a population. Estimating the distribution of incubation periods is important for a multitude of reasons including modeling the current and future scope of epidemics and evaluating disease control strategies. Important statistical issues arise in estimating incubation period distributions because of incomplete data and selection biases in epidemiological studies. Statistical methods for censored and truncated data are important for addressing those issues in the analyses of incubation periods. Syntheses of data from multiple studies of incubation periods can help overcome the limitations of any single study.


Keywords:

censoring;
epidemic;
incubation period;
infectious disease modeling;
truncation

Incubation Period of Infectious Diseases

On the Incubation Period of Typhoid Fever.

Data collection: Data were provided by review of patients’ charts, interview of family doctors, external hospitals and in single cases interview of family members. Patients who came for controlling of the stent were also interviewed. Methods: Bronchoscopic procedure was done under general anaesthesia, intubation with a rigid bronchoscope, dilation by balloon, rigid scope or combination. Stent placement was mostly done under fluoroscopy. Stent removal and/or replacement were also done with a rigid bronchoscope under general anaesthesia. Only the procedures for placement of a Montgomery t-tube were done under local anaesthesia. Results: During the observation period the longest survival time so far was 18 years. In 19 patients the stent led to a relief of symptoms, mainly dyspnoea and stridor and was tolerated without major complications (Successful stenting). In 12 patients the stent had to be removed because of complications and another four patients died of stenting related complications. In 16 patients therefore stenting was called “not successful”. Conclusions: In comparison to patients with advanced malignant diseases, where stenting is supposed only palliative, patients with tracheo-bronchial diseases are not limited in their life expectancy from the local tracheal disease, if this can be treated. But often these patients are high risk patients concerning co-morbidities, technical and functional operability. In these patients a stent can therefore be an alternative to surgery. Nevertheless because of the high rate of complications, which can be also life threatening, the decision must be done carefully. Purpose: Review of stenting in benign tracheal stenosis in the long run concerning indications, tolerance of stents,

Pulmonary & Respiratory Medicine

Many epidemiologists have agreed that a refined estimate of the incubation period of severe acute respiratory syndrome (SARS) would need a sample size of about 200 cases and appropriate statistical methods enabling the inclusion of cases with defined periods of exposure. However, no such studies have been reported so far. Besides, determinants of the SARS incubation period remain unclear. In this study, 209 probable SARS cases with documented episodes of exposure between March 1 and May 31, 2003, in mainland China were included. A nonparametric method was used to analyze these data with defined periods of exposure to obtain the refined estimate of the SARS incubation period. Furthermore, the authors also explored the influence of various factors on the SARS incubation period by analysis of variance, linear regression analysis, and analysis of covariance. The estimates of mean and variance of the SARS incubation period were 5.29 days and 12.33 days(2), respectively; 90% of patients would have an incubation period of less than 11.58 days with a probability of 0.8, and 99% of patients would have an incubation of less than 22.22 days with a probability of 0.9. The affected area showed a highly significant effect on the incubation period (p < 0.001), but the contact pattern, occupation, gender, and age did not.

/pdf/refined-estimate-of-the-incubation-period-of-severe-acute-5fu4shvdai.pdf

Refined Estimate of the Incubation Period of Severe Acute Respiratory Syndrome and Related Influencing Factors

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Refined Estimate of the Incubation Period of Severe Acute Respiratory Syndrome and Related Influencing Factors