scholarly journals Real-Time Estimation of the Risk of Death from Novel Coronavirus (COVID-19) Infection: Inference Using Exported Cases

2020 ◽  
Vol 9 (2) ◽  
pp. 523 ◽  
Author(s):  
Sung-mok Jung ◽  
Andrei R. Akhmetzhanov ◽  
Katsuma Hayashi ◽  
Natalie M. Linton ◽  
Yichi Yang ◽  
...  
Keyword(s):  
Growth Rate ◽  
Basic Reproduction Number ◽  
Cumulative Incidence ◽  
Reproduction Number ◽  
Early Stage ◽  
Case Fatality ◽  
Primary Case ◽  
Risk Of Death ◽  
Novel Coronavirus ◽  
The Basic Reproduction Number

The exported cases of 2019 novel coronavirus (COVID-19) infection that were confirmed outside China provide an opportunity to estimate the cumulative incidence and confirmed case fatality risk (cCFR) in mainland China. Knowledge of the cCFR is critical to characterize the severity and understand the pandemic potential of COVID-19 in the early stage of the epidemic. Using the exponential growth rate of the incidence, the present study statistically estimated the cCFR and the basic reproduction number—the average number of secondary cases generated by a single primary case in a naïve population. We modeled epidemic growth either from a single index case with illness onset on 8 December 2019 (Scenario 1), or using the growth rate fitted along with the other parameters (Scenario 2) based on data from 20 exported cases reported by 24 January 2020. The cumulative incidence in China by 24 January was estimated at 6924 cases (95% confidence interval [CI]: 4885, 9211) and 19,289 cases (95% CI: 10,901, 30,158), respectively. The latest estimated values of the cCFR were 5.3% (95% CI: 3.5%, 7.5%) for Scenario 1 and 8.4% (95% CI: 5.3%, 12.3%) for Scenario 2. The basic reproduction number was estimated to be 2.1 (95% CI: 2.0, 2.2) and 3.2 (95% CI: 2.7, 3.7) for Scenarios 1 and 2, respectively. Based on these results, we argued that the current COVID-19 epidemic has a substantial potential for causing a pandemic. The proposed approach provides insights in early risk assessment using publicly available data.

scholarly journals Real-Time Estimation of the Risk of Death from Novel Coronavirus (COVID-19) Infection: Inference Using Exported Cases

2020 ◽  
Author(s):  
Sung-mok Jung ◽  
Andrei R. Akhmetzhanov ◽  
Katsuma Hayashi ◽  
Natalie M. Linton ◽  
Yichi Yang ◽  
...  
Keyword(s):  
Growth Rate ◽  
Basic Reproduction Number ◽  
Cumulative Incidence ◽  
Reproduction Number ◽  
Early Stage ◽  
Case Fatality ◽  
Primary Case ◽  
Risk Of Death ◽  
Novel Coronavirus ◽  
The Basic Reproduction Number

AbstractThe exported cases of 2019 novel coronavirus (COVID-19) infection that were confirmed outside of China provide an opportunity to estimate the cumulative incidence and confirmed case fatality risk (cCFR) in mainland China. Knowledge of the cCFR is critical to characterize the severity and understand the pandemic potential of COVID-19 in the early stage of the epidemic. Using the exponential growth rate of the incidence, the present study statistically estimated the cCFR and the basic reproduction number—the average number of secondary cases generated by a single primary case in a naïve population. We modeled epidemic growth either from a single index case with illness onset on 8 December, 2019 (Scenario 1), or using the growth rate fitted along with the other parameters (Scenario 2) based on data from 20 exported cases reported by 24 January, 2020. The cumulative incidence in China by 24 January was estimated at 6924 cases (95% CI: 4885, 9211) and 19,289 cases (95% CI: 10,901, 30,158), respectively. The latest estimated values of the cCFR were 5.3% (95% CI: 3.5%, 7.5%) for Scenario 1 and 8.4% (95% CI: 5.3%, 12.3%) for Scenario 2. The basic reproduction number was estimated to be 2.1 (95% CI: 2.0, 2.2) and 3.2 (95% CI: 2.7, 3.7) for Scenarios 1 and 2, respectively. Based on these results, we argued that the current COVID-19 epidemic has a substantial potential for causing a pandemic. The proposed approach provides insights in early risk assessment using publicly available data.

scholarly journals The basic reproduction number and prediction of the epidemic size of the novel coronavirus (COVID-19) in Shahroud, Iran

2020 ◽  
Vol 148 ◽  
Author(s):  
A. Khosravi ◽  
R. Chaman ◽  
M. Rohani-Rasaf ◽  
F. Zare ◽  
S. Mehravaran ◽  
...  
Keyword(s):  
Basic Reproduction Number ◽  
Reproduction Number ◽  
Early Stage ◽  
Screening Tests ◽  
The Novel ◽  
Epidemic Size ◽  
Serial Interval ◽  
Novel Coronavirus ◽  
Interval Distribution ◽  
The Basic Reproduction Number

Abstract The aim of this study was to estimate the basic reproduction number (R0) of COVID-19 in the early stage of the epidemic and predict the expected number of new cases in Shahroud in Northeastern Iran. The R0 of COVID-19 was estimated using the serial interval distribution and the number of incidence cases. The 30-day probable incidence and cumulative incidence were predicted using the assumption that daily incidence follows a Poisson distribution determined by daily infectiousness. Data analysis was done using ‘earlyR’ and ‘projections’ packages in R software. The maximum-likelihood value of R0 was 2.7 (95% confidence interval (CI): 2.1−3.4) for the COVID-19 epidemic in the early 14 days and decreased to 1.13 (95% CI 1.03–1.25) by the end of day 42. The expected average number of new cases in Shahroud was 9.0 ± 3.8 cases/day, which means an estimated total of 271 (95% CI: 178–383) new cases for the period between 02 April to 03 May 2020. By day 67 (27 April), the effective reproduction number (Rt), which had a descending trend and was around 1, reduced to 0.70. Based on the Rt for the last 21 days (days 46–67 of the epidemic), the prediction for 27 April to 26 May is a mean daily cases of 2.9 ± 2.0 with 87 (48–136) new cases. In order to maintain R below 1, we strongly recommend enforcing and continuing the current preventive measures, restricting travel and providing screening tests for a larger proportion of the population.

scholarly journals The basic reproduction number and prediction of the epidemic size of the novel coronavirus (COVID-19) in Shahroud, Iran

2020 ◽  
Author(s):  
Ahmad Khosravi ◽  
Reza Chaman ◽  
Marzieh Rohani-Rasaf ◽  
Fariba Zare ◽  
Shiva Mehravaran ◽  
...  
Keyword(s):  
Basic Reproduction Number ◽  
Reproduction Number ◽  
Early Stage ◽  
Screening Tests ◽  
The Novel ◽  
Epidemic Size ◽  
Serial Interval ◽  
Novel Coronavirus ◽  
Interval Distribution ◽  
The Basic Reproduction Number

AbstractObjectivesTo estimate the basic reproduction number (R0) of COVID-19 in the early stage of the epidemic and predict the expected number of new cases in Shahroud, Northeast of Iran.MethodsThe R0 of COVID-19 was estimated using the serial interval distribution and the number of incidence cases. The serial interval was fit with a gamma distribution. The probable incidence and cumulative incidence in the next 30 days were predicted using the assumption that daily incidence follows a Poisson distribution determined by daily infectiousness. Data analysis was done using “earlyR” and “projections” packages in R software.ResultsThe maximum-likelihood value of R0 was 2.7 (95% confidence interval (CI): 2.1 to 3.4) for the COVID-19 epidemic in the early 14 days and decreased to 1.13 (95% CI: 1.03 to 1.25) by the end of the day 41. The expected average number of new cases in Shahroud is 9.0±3.8 case/day, which means an estimated total of 271 (95% CI: 178-383) new cases in the next 30 days.ConclusionsIt is essential to reduce the R0 to values below one. Therefore, we strongly recommend enforcing and continuing the current preventive measures, restricting travel, and providing screening tests for a larger proportion of the population.

scholarly journals A statistical analysis of the novel coronavirus (COVID-19) in Italy and Spain

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0249037
Author(s):  
Jeffrey Chu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Basic Reproduction Number ◽  
Linear Regression Model ◽  
Reproduction Number ◽  
Early Stage ◽  
The Novel ◽  
Novel Coronavirus ◽  
Log Linear ◽  
The Basic Reproduction Number

The novel coronavirus (COVID-19) that was first reported at the end of 2019 has impacted almost every aspect of life as we know it. This paper focuses on the incidence of the disease in Italy and Spain—two of the first and most affected European countries. Using two simple mathematical epidemiological models—the Susceptible-Infectious-Recovered model and the log-linear regression model, we model the daily and cumulative incidence of COVID-19 in the two countries during the early stage of the outbreak, and compute estimates for basic measures of the infectiousness of the disease including the basic reproduction number, growth rate, and doubling time. Estimates of the basic reproduction number were found to be larger than 1 in both countries, with values being between 2 and 3 for Italy, and 2.5 and 4 for Spain. Estimates were also computed for the more dynamic effective reproduction number, which showed that since the first cases were confirmed in the respective countries the severity has generally been decreasing. The predictive ability of the log-linear regression model was found to give a better fit and simple estimates of the daily incidence for both countries were computed.

scholarly journals Estimating the basic reproduction number for COVID-19 in Western Europe

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248731
Author(s):  
Isabella Locatelli ◽  
Bastien Trächsel ◽  
Valentin Rousson
Keyword(s):  
Growth Rate ◽  
Basic Reproduction Number ◽  
Exponential Growth ◽  
Cumulative Incidence ◽  
Western Europe ◽  
Reproduction Number ◽  
European Population ◽  
Exponential Growth Rate ◽  
Official Statistics ◽  
The Basic Reproduction Number

Objective To estimate the basic reproduction number (R0) for COVID-19 in Western Europe. Methods Data (official statistics) on the cumulative incidence of COVID-19 at the start of the outbreak (before any confinement rules were declared) were retrieved in the 15 largest countries in Western Europe, allowing us to estimate the exponential growth rate of the disease. The rate was then combined with estimates of the distribution of the generation interval as reconstructed from the literature. Results Despite the possible unreliability of some official statistics about COVID-19, the spread of the disease appears to be remarkably similar in most European countries, allowing us to estimate an average R0 in Western Europe of 2.2 (95% CI: 1.9–2.6). Conclusions The value of R0 for COVID-19 in Western Europe appears to be significantly lower than that in China. The proportion of immune persons in the European population required to stop the outbreak could thus be closer to 50% than to 70%.

scholarly journals Exploring COVID-19 Daily Records of Diagnosed Cases and Fatalities Based on Simple Non-parametric Methods

2020 ◽  
Author(s):  
Hans H. Diebner ◽  
Nina Timmesfeld
Keyword(s):  
Basic Reproduction Number ◽  
Reproduction Number ◽  
Time Lag ◽  
Case Fatality ◽  
Parametric Models ◽  
Intermediate Step ◽  
Parametric Methods ◽  
Temporal Behaviour ◽  
The Basic Reproduction Number ◽  
Non Parametric

Based on comprehensible non-parametric methods, estimates of crucial parameters that characterise the COVID-19 pandemic with a focus on the German epidemic are presented. Where appropriate, the estimates for Germany are compared with the results for six other countries (FR, IT, US, UK, ES, CH) to get an idea of the breadth of applicability and a relational understanding. Thereby, only prevalence data of daily reported new counts of diagnosed cases and fatalities provided by the ECDC are used. Where appropriate, the results are compared with conclusions drawn from using the dataset provided by the RKI. Drawing on uncertain a priori knowledge is avoided. Specifically, we present estimates for the duration from diagnosis to death being 13 days for Germany and about 2 days for Italy as the extremes. Furthermore, based on the knowledge of this time lag between diagnoses and deaths, properly delayed asymptotic as well as instantaneous fatality-case ratios are calculated having superiority compared to the commonly published case-fatality rate. The median of the time series of the instantaneous fatality-case ratio with proper delay of 13-days between cases and deaths for Germany turns out to be 0.024. Asymptotic values are presented for other countries with France ranking highest with a fatality-case ratio of almost 0.2 at its peak. The basic reproduction number, R_0, for Germany is estimated to be between 2.4 and 3.4. The uncertainty stems from uncertain knowledge of the generation time. A delay autocorrelation shows resonances at about 4 days and 7 days, where the latter resonance is at least partially attributable to the sampling process with weekly periodicity. The calculation of the basic reproduction number is based on an evaluation of cumulative numbers of cases yielding time-dependent doubling times as an intermediate step. This allows to infer to the reproduction number during the early phase of onset of the epidemic. In a second approach, the instantaneous basic reproduction number is derived from the incident (counts of new) cases and allows, in contrast to the first version, to infer to the temporal behaviour of the reproduction number during the later epidemic course. To conclude, by avoiding complicated parametric models we provide insights into basic features of the COVID-19 epidemic in an utmost transparent and comprehensible way.

scholarly journals SEIHCRD Model for COVID-19 spread scenarios, disease predictions and estimates the basic reproduction number, case fatality rate, hospital, and ICU beds requirement

2020 ◽  
Author(s):  
Avaneesh Singh ◽  
Manish Kumar Bajpai
Keyword(s):  
Basic Reproduction Number ◽  
Case Fatality Rate ◽  
Reproduction Number ◽  
Case Fatality ◽  
The United States ◽  
Fatality Rate ◽  
Hospital Beds ◽  
The Impact ◽  
Over Time ◽  
The Basic Reproduction Number

We have proposed a new mathematical method, SEIHCRD-Model that is an extension of the SEIR-Model adding hospitalized and critical twocompartments. SEIHCRD model has seven compartments: susceptible (S), exposed (E), infected (I), hospitalized (H), critical (C), recovered (R), and deceased or death (D), collectively termed SEIHCRD. We have studied COVID- 19 cases of six countries, where the impact of this disease in the highest are Brazil, India, Italy, Spain, the United Kingdom, and the United States. SEIHCRD model is estimating COVID-19 spread and forecasting under uncertainties, constrained by various observed data in the present manuscript. We have first collected the data for a specific period, then fit the model for death cases, got the values of some parameters from it, and then estimate the basic reproduction number over time, which is nearly equal to real data, infection rate, and recovery rate of COVID-19. We also compute the case fatality rate over time of COVID-19 most affected countries. SEIHCRD model computes two types of Case fatality rate one is CFR daily and the second one is total CFR. We analyze the spread and endpoint of COVID-19 based on these estimates. SEIHCRD model is time-dependent hence we estimate the date and magnitude of peaks of corresponding to the number of exposed cases, infected cases, hospitalized cases, critical cases, and the number of deceased cases of COVID-19 over time. SEIHCRD model has incorporated the social distancing parameter, different age groups analysis, number of ICU beds, number of hospital beds, and estimation of how much hospital beds and ICU beds are required in near future.

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