scholarly journals Incubation Period and Other Epidemiological Characteristics of 2019 Novel Coronavirus Infections with Right Truncation: A Statistical Analysis of Publicly Available Case Data

2020 ◽  
Author(s):  
Natalie M. Linton ◽  
Tetsuro Kobayashi ◽  
Yichi Yang ◽  
Katsuma Hayashi ◽  
Andrei R. Akhmetzhanov ◽  
...  
Keyword(s):  
Incubation Period ◽  
Case Fatality ◽  
Illness Onset ◽  
Event Date ◽  
History Of ◽  
Novel Coronavirus ◽  
Case Fatality Risk ◽  
Right Truncation ◽  
Epidemiological Characteristics ◽  
Mean Time

AbstractThe geographic spread of 2019 novel coronavirus (COVID-19) infections from the epicenter of Wuhan, China, has provided an opportunity to study the natural history of the recently emerged virus. Using publicly available event-date data from the ongoing epidemic, the present study investigated the incubation period and other time intervals that govern the epidemiological dynamics of COVID-19 infections. Our results show that the incubation period falls within the range of 2–14 days with 95% confidence and has a mean of around 5 days when approximated using the best-fit lognormal distribution. The mean time from illness onset to hospital admission (for treatment and/or isolation) was estimated at 3–4 days without truncation and at 5–9 days when right truncated. Based on the 95th percentile estimate of the incubation period, we recommend that the length of quarantine should be at least 14 days. The median time delay of 13 days from illness onset to death (17 days with right truncation) should be considered when estimating the COVID-19 case fatality risk.

scholarly journals Incubation Period and Other Epidemiological Characteristics of 2019 Novel Coronavirus Infections with Right Truncation: A Statistical Analysis of Publicly Available Case Data

2020 ◽  
Vol 9 (2) ◽  
pp. 538 ◽  
Author(s):  
Natalie Linton ◽  
Tetsuro Kobayashi ◽  
Yichi Yang ◽  
Katsuma Hayashi ◽  
Andrei Akhmetzhanov ◽  
...  
Keyword(s):  
Incubation Period ◽  
Case Fatality ◽  
Illness Onset ◽  
Event Date ◽  
History Of ◽  
Novel Coronavirus ◽  
Case Fatality Risk ◽  
Right Truncation ◽  
Epidemiological Characteristics ◽  
Mean Time

The geographic spread of 2019 novel coronavirus (COVID-19) infections from the epicenter of Wuhan, China, has provided an opportunity to study the natural history of the recently emerged virus. Using publicly available event-date data from the ongoing epidemic, the present study investigated the incubation period and other time intervals that govern the epidemiological dynamics of COVID-19 infections. Our results show that the incubation period falls within the range of 2–14 days with 95% confidence and has a mean of around 5 days when approximated using the best-fit lognormal distribution. The mean time from illness onset to hospital admission (for treatment and/or isolation) was estimated at 3–4 days without truncation and at 5–9 days when right truncated. Based on the 95th percentile estimate of the incubation period, we recommend that the length of quarantine should be at least 14 days. The median time delay of 13 days from illness onset to death (17 days with right truncation) should be considered when estimating the COVID-19 case fatality risk.

scholarly journals Serial interval of novel coronavirus (COVID-19) infections

2020 ◽  
Author(s):  
Hiroshi Nishiura ◽  
Natalie M. Linton ◽  
Andrei R. Akhmetzhanov
Keyword(s):  
Incubation Period ◽  
Credible Interval ◽  
Substantial Proportion ◽  
List Type ◽  
Illness Onset ◽  
Full Dataset ◽  
Serial Interval ◽  
Novel Coronavirus ◽  
Right Truncation ◽  
Published Research

AbstractObjectiveTo estimate the serial interval of novel coronavirus (COVID-19) from information on 28 infector-infectee pairs.MethodsWe 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 adjusting for right truncation of the data as the epidemic is still in its growth phase.ResultsAccounting 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).ConclusionsThe serial interval of COVID-19 is 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.Highlights-The serial interval of novel coronavirus (COVID-19) infections was estimated from a total of 28 infector-infectee pairs.-The median serial interval is shorter than the median incubation period, suggesting a substantial proportion of pre-symptomatic transmission.-A short serial interval makes it difficult to trace contacts due to the rapid turnover of case generations.

scholarly journals Communicating the Risk of Death from Novel Coronavirus Disease (COVID-19)

2020 ◽  
Vol 9 (2) ◽  
pp. 580 ◽  
Author(s):  
Tetsuro Kobayashi ◽  
Sung-mok Jung ◽  
Natalie M. Linton ◽  
Ryo Kinoshita ◽  
Katsuma Hayashi ◽  
...  
Keyword(s):  
Virus Detection ◽  
Case Fatality ◽  
Cumulative Number ◽  
Diagnostic Laboratory ◽  
Illness Onset ◽  
Risk Of Death ◽  
Health Authorities ◽  
Novel Coronavirus ◽  
Case Fatality Risk ◽  
Diagnostic Laboratory Tests

To understand the severity of infection for a given disease, it is common epidemiological practice to estimate the case fatality risk, defined as the risk of death among cases. However, there are three technical obstacles that should be addressed to appropriately measure this risk. First, division of the cumulative number of deaths by that of cases tends to underestimate the actual risk because deaths that will occur have not yet observed, and so the delay in time from illness onset to death must be addressed. Second, the observed dataset of reported cases represents only a proportion of all infected individuals and there can be a substantial number of asymptomatic and mildly infected individuals who are never diagnosed. Third, ascertainment bias and risk of death among all those infected would be smaller when estimated using shorter virus detection windows and less sensitive diagnostic laboratory tests. In the ongoing COVID-19 epidemic, health authorities must cope with the uncertainty in the risk of death from COVID-19, and high-risk individuals should be identified using approaches that can address the abovementioned three problems. Although COVID-19 involves mostly mild infections among the majority of the general population, the risk of death among young adults is higher than that of seasonal influenza, and elderly with underlying comorbidities require additional care.

scholarly journals A Comparison of Case Fatality Risk of COVID-19 between Singapore and Japan

2020 ◽  
Vol 9 (10) ◽  
pp. 3326
Author(s):  
Taishi Kayano ◽  
Hiroshi Nishiura
Keyword(s):  
Age Distribution ◽  
Case Fatality ◽  
Standardized Mortality Ratio ◽  
Fatality Risk ◽  
Illness Onset ◽  
High Virulence ◽  
Mortality Ratio ◽  
The Many ◽  
Case Fatality Risk ◽  
Protection Against Infection

The crude case fatality risk (CFR) for coronavirus disease (COVID-19) in Singapore is remarkably small. We aimed to estimate the unbiased CFR by age for Singapore and Japan and compare these estimates by calculating the standardized mortality ratio (SMR). Age-specific CFRs for COVID-19 were estimated in real time, adjusting for the delay from illness onset to death. The SMR in Japan was estimated by using the age distribution of the Singapore population. Among cases aged 60–69 years and 70–79 years, the age-specific CFRs in Singapore were estimated as 1.84% (95% confidence interval: 0.46–4.72%) and 5.57% (1.41–13.97%), respectively, and those in Japan as 5.52% (4.55–6.62%) and 15.49% (13.81–17.27%), respectively. The SMR of COVID-19 in Japan, when compared with Singapore as the baseline, was estimated to be 1.46 (1.09–2.96). The overall CFR for Singapore is lower than that for Japan. It is possible that the circulating variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Singapore causes a milder clinical course of COVID-19 infection compared with other strains. If infection with a low-virulence SARS-CoV-2 variant provides protection against infection by high-virulence strains, the existence of such a strain is encouraging news for the many countries struggling to suppress this virus.

scholarly journals Epidemiological and clinical characteristics of 671 COVID-19 patients in Henan Province, China

2020 ◽  
Vol 49 (4) ◽  
pp. 1085-1095
Author(s):  
Yifei Nie ◽  
Jitian Li ◽  
Xueyong Huang ◽  
Wanshen Guo ◽  
Xiaobai Zhang ◽  
...  
Keyword(s):  
Clinical Laboratory ◽  
Case Series ◽  
Henan Province ◽  
X Rays ◽  
Illness Onset ◽  
Medical Disorders ◽  
History Of ◽  
The Mean ◽  
Wet Market ◽  
Mean Time

Abstract Background Despite many reports on the characteristics of coronavirus disease 2019 (COVID-19) in Wuhan, China, relatively little is known about the transmission features of COVID-19 outside Wuhan, especially at the provincial level. Methods We collected epidemiological, demographic, clinical, laboratory, radiological and occupation information, along with contact history, of 671 patients with laboratory-confirmed COVID-19 reported from January 23 to February 5, 2020, in Henan province, China. We described characteristics of these cases, compared the diagnostic accuracy and features of blood testing, computed tomography (CT) scans and X-rays, and analysed SARS-CoV-2 transmission sources and patients’ occupations in Henan province. Results The mean age of patients in this case series was 43 years, 56.2% were male and 22.4% had coexisting medical disorders. The death rate was 0.3%. Fourteen patients did not show any symptoms. Lymphocyte percentage was associated with disease severity (χ2 = 6.71, P = 0.035) but had a large variation in each sample group. The mean time from illness onset to diagnosis was 5.6 days. A total of 330 patients had ever lived in or visited Wuhan, 150 had contact with confirmed cases, 323 had been to a hospital and 119 had been to a wet market. There were 33 patients who did not have a traceable transmission source, with 21.2% of these being farmers and 15.2% being workmen. Conclusions Lymphocyte percentage was a sign of severe COVID-19 in general but was not a good diagnostic index. Longer time from illness onset to diagnosis was associated with higher COVID-19 severity, older age, higher likelihood of having coexisting cardiovascular diseases including hypertension, and being male. Farming was found to be a high-risk occupation in Henan province, China.

scholarly journals Retrospective methodology to estimate daily infections from deaths (REMEDID) in COVID-19: the Spain case study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
David García-García ◽  
María Isabel Vigo ◽  
Eva S. Fonfría ◽  
Zaida Herrador ◽  
Miriam Navarro ◽  
...  
Keyword(s):  
Growth Rate ◽  
Incubation Period ◽  
Virus Transmission ◽  
Case Fatality ◽  
National Government ◽  
Data Accuracy ◽  
Illness Onset ◽  
Case Fatality Ratio ◽  
Official Data

AbstractThe number of new daily infections is one of the main parameters to understand the dynamics of an epidemic. During the COVID-19 pandemic in 2020, however, such information has been underestimated. Here, we propose a retrospective methodology to estimate daily infections from daily deaths, because those are usually more accurately documented. Given the incubation period, the time from illness onset to death, and the case fatality ratio, the date of death can be estimated from the date of infection. We apply this idea conversely to estimate infections from deaths. This methodology is applied to Spain and its 19 administrative regions. Our results showed that probable daily infections during the first wave were between 35 and 42 times more than those officially documented on 14 March, when the national government decreed a national lockdown and 9 times more than those documented by the updated version of the official data. The national lockdown had a strong effect on the growth rate of virus transmission, which began to decrease immediately. Finally, the first inferred infection in Spain is about 43 days before the official data were available during the first wave. The current official data show delays of 15–30 days in the first infection relative to the inferred infections in 63% of the regions. In summary, we propose a methodology that allows reinterpretation of official daily infections, improving data accuracy in infection magnitude and dates because it assimilates valuable information from the National Seroprevalence Studies.

scholarly journals Estimating age-specific COVID-19 fatality risk and time to death by comparing population diagnosis and death patterns: Australian data

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ian C. Marschner
Keyword(s):  
Age Groups ◽  
Case Fatality ◽  
The Elderly ◽  
Specific Model ◽  
Fatality Risk ◽  
Individual Level ◽  
Time To Death ◽  
History Of ◽  
Case Fatality Risk ◽  
Dynamics Models

Abstract Background Mortality is a key component of the natural history of COVID-19 infection. Surveillance data on COVID-19 deaths and case diagnoses are widely available in the public domain, but they are not used to model time to death because they typically do not link diagnosis and death at an individual level. This paper demonstrates that by comparing the unlinked patterns of new diagnoses and deaths over age and time, age-specific mortality and time to death may be estimated using a statistical method called deconvolution. Methods Age-specific data were analysed on 816 deaths among 6235 cases over age 50 years in Victoria, Australia, from the period January through December 2020. Deconvolution was applied assuming logistic dependence of case fatality risk (CFR) on age and a gamma time to death distribution. Non-parametric deconvolution analyses stratified into separate age groups were used to assess the model assumptions. Results It was found that age-specific CFR rose from 2.9% at age 65 years (95% CI:2.2 – 3.5) to 40.0% at age 95 years (CI: 36.6 – 43.6). The estimated mean time between diagnosis and death was 18.1 days (CI: 16.9 – 19.3) and showed no evidence of varying by age (heterogeneity P = 0.97). The estimated 90% percentile of time to death was 33.3 days (CI: 30.4 – 36.3; heterogeneity P = 0.85). The final age-specific model provided a good fit to the observed age-stratified mortality patterns. Conclusions Deconvolution was demonstrated to be a powerful analysis method that could be applied to extensive data sources worldwide. Such analyses can inform transmission dynamics models and CFR assessment in emerging outbreaks. Based on these Australian data it is concluded that death from COVID-19 occurs within three weeks of diagnosis on average but takes five weeks in 10% of fatal cases. Fatality risk is negligible in the young but rises above 40% in the elderly, while time to death does not seem to vary by age.

scholarly journals Case fatality risk of novel coronavirus diseases 2019 in China

2020 ◽  
Author(s):  
Xiaowei Deng ◽  
Juan Yang ◽  
Wei Wang ◽  
Xiling Wang ◽  
Jiaxin Zhou ◽  
...  
Keyword(s):  
Risk Factors ◽  
Mortality Risk ◽  
Influenza Pandemic ◽  
Mainland China ◽  
Case Fatality ◽  
Right Censoring ◽  
Fatality Risk ◽  
Critical Patients ◽  
Novel Coronavirus ◽  
Case Fatality Risk

ABSTRACTObjectiveThe outbreak of novel coronavirus disease 2019 (COVID-19) imposed a substantial health burden in mainland China and remains a global epidemic threat. Our objectives are to assess the case fatality risk (CFR) among COVID-19 patients detected in mainland China, stratified by clinical category and age group.MethodsWe collected individual information on laboratory-confirmed COVID-19 cases from publicly available official sources from December 29, 2019 to February 23, 2020. We explored the risk factors associated with mortality. We used methods accounting for right-censoring and survival analyses to estimate the CFR among detected cases.ResultsOf 12,863 cases reported outside Hubei, we obtained individual records for 9,651 cases, including 62 deaths and 1,449 discharged cases. The deceased were significantly older than discharged cases (median age: 77 vs 39 years, p<0.001). 58% (36/62) were male. Older age (OR 1.18 per year; 95%CI: 1.14 to 1.22), being male (OR 2.02; 95%CI: 1.02 to 4.03), and being treated in less developed economic regions (e.g., West and Northeast vs. East, OR 3.93; 95%CI: 1.74 to 8.85) were mortality risk factors. The estimated CFR was 0.89-1.24% among all cases. The fatality risk among critical patients was 2-fold higher than that among severe and critical patients, and 24-fold higher than that among moderate, severe and critical patients.ConclusionsOur estimates of CFR based on laboratory-confirmed cases ascertained outside of Hubei suggest that COVID-19 is not as severe as severe acute respiratory syndrome and Middle East respiratory syndrome, but more similar to the mortality risk of 2009 H1N1 influenza pandemic in hospitalized patients. The fatality risk of COVID-19 is higher in males and increases with age. Our study improves the severity assessment of the ongoing epidemic and can inform the COVID-19 outbreak response in China and beyond.

scholarly journals Epidemiological and clinical profile of novel coronavirus disease (COVID 19) in a hospital in Metropolitan India

2020 ◽  
Vol 8 (1) ◽  
pp. 254-262
Author(s):  
Priti Chowdhary ◽  
Ritesh Ranjan ◽  
Cecil C Khakha ◽  
Deepika Govil ◽  
Munesh Kasana ◽  
...  
Keyword(s):  
Health Care ◽  
Health Care Workers ◽  
Case Fatality ◽  
Clinical Profile ◽  
Family Welfare ◽  
Asymptomatic Patients ◽  
Care Workers ◽  
Single Centre Study ◽  
History Of ◽  
Novel Coronavirus

Context: To understand the epidemiological and clinical profile of COVID 19. Aims: To study the epidemiological and clinical profile of Novel Coronavirus disease with comorbidities and outcome. Settings and Design: In this single centre study, we included patients with suspected and confirmed cases of COVID 19 Methods and Material: We followed the testing criteria for COVID 19 laid down by Ministry of Health and Family Welfare (MoHFW), Government of India. COVID 19 positive patients were divided as mild, moderate and severe and followed till discharge or death. Data was collected through interview of patients and hospital records. Results: A total of 178 suspected cases of COVID 19 reported to our hospital in this duration. Out of these, confirmed positive cases were 66 in number. Among 66 COVID 19 positive patients 11 were Health Care Workers. The Median age of the patients was 37 years. Most of the confirmed COVID patients were young and middle aged between 15 to 49 years age (69.69 %). 66.66 % were males and 33.33% were females. Case fatality rate was 4.54%. In our study only 15.15% had history of contact with COVID 19 positive patients. Conclusions: Fever is the most common presenting symptom but fever should not be the exclusion criteria. Since there is silent spread of virus in the community among the asymptomatic patients there is need to increase the number of testing. Containment and mitigation strategy like Social distancing should be maintained. Routine screening of Health Care Workers should be done.

scholarly journals Characteristics of discharged patients from hospitals - Georgian descriptive study

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
N Skhvitaridze ◽  
A Gamkrelidze ◽  
M Kereselidze ◽  
K Gambashidze ◽  
L Kandelaki ◽  
...  
Keyword(s):  
Case Fatality ◽  
Severity Of Illness ◽  
Descriptive Study ◽  
Positive Outcome ◽  
Health Conditions ◽  
The Novel ◽  
Discharged Patients ◽  
Novel Coronavirus ◽  
Long Term Impact ◽  
Epidemiological Characteristics

Abstract COVID-19, the disease caused by the novel coronavirus, started spreading rapidly across the world. Although, many aspects of disease is still under the observation. Therefore, in this study retrospectively was investigated the data on patients discharged from Georgian hospitals between February and June. According to Georgian regulation, all patients with confirmed COVID-19 should be hospitalized. The data sources were hospitalization registry and the standardized case report forms. In this descriptive study, epidemiological characteristics of 500 COVID-19 cases confirmed with PCR tests were analyzed. Among variables were: gender, age and geographic distribution, disease characteristics, underlying health conditions, test-related features, etc. Majority of patients (72.0%) were admitted to hospitals by the ambulance. The mean age of the patients was 43 years. The sex distribution was merely the same for both genders (49.4% male and 50.6% female). The most common symptoms were fever (82.4%, 95% CI 78.4-85.9), fatigue (49.6%, 95% CI 44.7-54.5), and cough (38.3%, 95% CI 33.6-43.1). The proportion of asymptomatic cases during the PCR testing were 16.6%. More than one symptom was observed in 57.6% of patients. Most common underlying health conditions were cardiovascular diseases (21.8%), diabetes (7.6%), kidney disease (3.6%), chronic lung disease (2.4%), cancer (2.2%), and chronic hepatitis (1.8%). Disease severity significantly differ among cases. Of those without comorbidity conditions, 94% had mild severity. However, among those with comorbidity conditions 35% were sever or critical. 50% of patients were reported as obese. The proportion of severe or critical patients was higher with BMI-18.5-24.9 and BMI ≥30. The case-fatality rate was 2.6%, with majority of deaths among aged ≥70. The severity of illness was strongly associated with comorbidity conditions and BMI. These findings are important to contribute and improve evidence-based knowledge for the novel coronavirus. Key messages Hospitalization of all COVID-19 cases, despite their severity, improving positive outcome of patients. Follow-up for discharged patients is necessary to control medium and long-term impact of COVID-19.

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