scholarly journals Superspreading in early transmissions of COVID-19 in Indonesia

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Agus Hasan ◽  
Hadi Susanto ◽  
Muhammad Firmansyah Kasim ◽  
Nuning Nuraini ◽  
Bony Lestari ◽  
...  
Keyword(s):  
Basic Reproduction Number ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Negative Binomial ◽  
Reproduction Number ◽  
Early Stage ◽  
Secondary Case ◽  
Isolation And Identification ◽  
Overdispersion Parameter ◽  
The Basic Reproduction Number

AbstractThis paper presents a study of early epidemiological assessment of COVID-19 transmission dynamics in Indonesia. The aim is to quantify heterogeneity in the numbers of secondary infections. To this end, we estimate the basic reproduction number $$\mathscr {R}_0$$ R 0 and the overdispersion parameter $$\mathscr {K}$$ K at two regions in Indonesia: Jakarta–Depok and Batam. The method to estimate $$\mathscr {R}_0$$ R 0 is based on a sequential Bayesian method, while the parameter $$\mathscr {K}$$ K is estimated by fitting the secondary case data with a negative binomial distribution. Based on the first 1288 confirmed cases collected from both regions, we find a high degree of individual-level variation in the transmission. The basic reproduction number $$\mathscr {R}_0$$ R 0 is estimated at 6.79 and 2.47, while the overdispersion parameter $$\mathscr {K}$$ K of a negative-binomial distribution is estimated at 0.06 and 0.2 for Jakarta–Depok and Batam, respectively. This suggests that superspreading events played a key role in the early stage of the outbreak, i.e., a small number of infected individuals are responsible for large numbers of COVID-19 transmission. This finding can be used to determine effective public measures, such as rapid isolation and identification, which are critical since delay of diagnosis is the most common cause of superspreading events.

scholarly journals Superspreading in Early Transmissions of COVID-19 in Indonesia

2020 ◽  
Author(s):  
A. Hasan ◽  
H. Susanto ◽  
M.F. Kasim ◽  
N. Nuraini ◽  
D. Triany ◽  
...  
Keyword(s):  
Basic Reproduction Number ◽  
Negative Binomial Distribution ◽  
Negative Binomial ◽  
Reproduction Number ◽  
Early Stage ◽  
Level Variation ◽  
Individual Level ◽  
High Degree ◽  
Overdispersion Parameter ◽  
The Basic Reproduction Number

AbstractWe estimate the basic reproduction number ℛ0 and the overdispersion parameter K at two COVID-19 clusters in Indonesia: Jakarta-Depok and Batam. Based on the first 397 confirmed cases in both clusters, we find a high degree of individual-level variation in the transmission. The basic reproduction number ℛ0 is estimated at 6.79 and 2.47, while the overdispersion parameter K of a negative-binomial distribution is estimated at 0.08 and 0.2 for Jakarta-Depok and Batam, respectively. This suggests that superspreading events played a key role in the early stage of the outbreak, i.e., a small number of infected individuals are responsible for large amounts of COVID-19 transmission.

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 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 Multi-Type Branching Processes Modeling of Nosocomial Epidemics

2017 ◽  
Vol 32 (2) ◽  
Author(s):  
Zeinab Mohamed ◽  
Tamer Oraby
Keyword(s):  
Health Care ◽  
Basic Reproduction Number ◽  
Branching Process ◽  
Negative Binomial ◽  
Reproduction Number ◽  
Branching Processes ◽  
Care Facility ◽  
Health Care Facility ◽  
Different Types ◽  
The Basic Reproduction Number

AbstractNosocomial epidemics are infectious diseases which spread among different types of susceptible individuals in a health-care facility. To model this type of epidemics, we use a multi-type branching process with a multivariate negative binomial offspring distribution. In particular, we estimate the basic reproduction number

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 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 Estimating the overdispersion in COVID-19 transmission using outbreak sizes outside China

2020 ◽  
Vol 5 ◽  
pp. 67 ◽  
Author(s):  
Akira Endo ◽  
Sam Abbott ◽  
Adam J. Kucharski ◽  
Sebastian Funk ◽  
Keyword(s):  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Process Model ◽  
Negative Binomial ◽  
Reproduction Number ◽  
Joint Estimation ◽  
World Health ◽  
Level Variation ◽  
Individual Level ◽  
Novel Coronavirus

Background: A novel coronavirus disease (COVID-19) outbreak has now spread to a number of countries worldwide. While sustained transmission chains of human-to-human transmission suggest high basic reproduction number R0, variation in the number of secondary transmissions (often characterised by so-called superspreading events) may be large as some countries have observed fewer local transmissions than others. Methods: We quantified individual-level variation in COVID-19 transmission by applying a mathematical model to observed outbreak sizes in affected countries. We extracted the number of imported and local cases in the affected countries from the World Health Organization situation report and applied a branching process model where the number of secondary transmissions was assumed to follow a negative-binomial distribution. Results: Our model suggested a high degree of individual-level variation in the transmission of COVID-19. Within the current consensus range of R0 (2-3), the overdispersion parameter k of a negative-binomial distribution was estimated to be around 0.1 (median estimate 0.1; 95% CrI: 0.05-0.2 for R0 = 2.5), suggesting that 80% of secondary transmissions may have been caused by a small fraction of infectious individuals (~10%). A joint estimation yielded likely ranges for R0 and k (95% CrIs: R0 1.4-12; k 0.04-0.2); however, the upper bound of R0 was not well informed by the model and data, which did not notably differ from that of the prior distribution. Conclusions: Our finding of a highly-overdispersed offspring distribution highlights a potential benefit to focusing intervention efforts on superspreading. As most infected individuals do not contribute to the expansion of an epidemic, the effective reproduction number could be drastically reduced by preventing relatively rare superspreading events.

scholarly journals Estimating the overdispersion in COVID-19 transmission using outbreak sizes outside China

2020 ◽  
Vol 5 ◽  
pp. 67 ◽  
Author(s):  
Akira Endo ◽  
Sam Abbott ◽  
Adam J. Kucharski ◽  
Sebastian Funk ◽  
Keyword(s):  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Process Model ◽  
Negative Binomial ◽  
Reproduction Number ◽  
Joint Estimation ◽  
World Health ◽  
Level Variation ◽  
Individual Level ◽  
Novel Coronavirus

Background: A novel coronavirus disease (COVID-19) outbreak has now spread to a number of countries worldwide. While sustained transmission chains of human-to-human transmission suggest high basic reproduction number R0, variation in the number of secondary transmissions (often characterised by so-called superspreading events) may be large as some countries have observed fewer local transmissions than others. Methods: We quantified individual-level variation in COVID-19 transmission by applying a mathematical model to observed outbreak sizes in affected countries. We extracted the number of imported and local cases in the affected countries from the World Health Organization situation report and applied a branching process model where the number of secondary transmissions was assumed to follow a negative-binomial distribution. Results: Our model suggested a high degree of individual-level variation in the transmission of COVID-19. Within the current consensus range of R0 (2-3), the overdispersion parameter k of a negative-binomial distribution was estimated to be around 0.1 (median estimate 0.1; 95% CrI: 0.05-0.2 for R0 = 2.5), suggesting that 80% of secondary transmissions may have been caused by a small fraction of infectious individuals (~10%). A joint estimation yielded likely ranges for R0 and k (95% CrIs: R0 1.4-12; k 0.04-0.2); however, the upper bound of R0 was not well informed by the model and data, which did not notably differ from that of the prior distribution. Conclusions: Our finding of a highly-overdispersed offspring distribution highlights a potential benefit to focusing intervention efforts on superspreading. As most infected individuals do not contribute to the expansion of an epidemic, the effective reproduction number could be drastically reduced by preventing relatively rare superspreading events.

scholarly journals The basic reproduction number can be accurately estimated within 14 days after societal lockdown: The early stage of the COVID-19 epidemic in Denmark

2020 ◽  
Author(s):  
Jan Brink Valentin ◽  
Henrik Møller ◽  
Søren Paaske Johnsen
Keyword(s):  
Basic Reproduction Number ◽  
Reproduction Number ◽  
Early Stage ◽  
Monte Carlo Algorithm ◽  
Reliable Estimate ◽  
Model Parameters ◽  
Sigmoid Function ◽  
Cumulative Number ◽  
Number Of Patients ◽  
The Basic Reproduction Number

Objective: Early identification of the basic reproduction number (BRN) is imperative to political decision making during an epidemic. In this study, we estimated the BRN 7, 14, 21 and 28 days after societal lockdown of Denmark during the early stage of the COVID-19 epidemic. Method: We implemented the SEIR dynamical system for disease spread without vital dynamics. The BRN was modulated using a sigmoid function. Model parameters were estimated on number of admitted patients, number of patients in intensive care and cumulative number of deaths using the simulated annealing Monte Carlo algorithm. Results are presented with 95% prediction intervals (PI). Results: We were unable to determine any reliable estimate of the BRN at 7 days following lockdown. The BRN had stabilised at day 14 throughout day 28, with the estimate ranging from 0.95 (95% PI: 0.92-0.98) at day 7 to 0.92 (95% PI: 0.92-0.93) at day 28. We estimated the BRN prior to lockdown to be 3.32 (95% PI: 3.31-3.33). The effect of the lockdown was occurring over a period of a few days centred at March 18th (95% PI: 17th-18th) 2020. Conclusion: We believe our model provides a valuable tool for decision makers to reliably estimate the effect of a politically determined lockdown during an epidemic.

scholarly journals Estimating the overdispersion in COVID-19 transmission using outbreak sizes outside China

2020 ◽  
Vol 5 ◽  
pp. 67 ◽  
Author(s):  
Akira Endo ◽  
Sam Abbott ◽  
Adam J. Kucharski ◽  
Sebastian Funk ◽  
Keyword(s):  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Process Model ◽  
Negative Binomial ◽  
Reproduction Number ◽  
Joint Estimation ◽  
World Health ◽  
Level Variation ◽  
Individual Level ◽  
Novel Coronavirus

Background: A novel coronavirus disease (COVID-19) outbreak has now spread to a number of countries worldwide. While sustained transmission chains of human-to-human transmission suggest high basic reproduction number R0, variation in the number of secondary transmissions (often characterised by so-called superspreading events) may be large as some countries have observed fewer local transmissions than others. Methods: We quantified individual-level variation in COVID-19 transmission by applying a mathematical model to observed outbreak sizes in affected countries. We extracted the number of imported and local cases in the affected countries from the World Health Organization situation report and applied a branching process model where the number of secondary transmissions was assumed to follow a negative-binomial distribution. Results: Our model suggested a high degree of individual-level variation in the transmission of COVID-19. Within the current consensus range of R0 (2-3), the overdispersion parameter k of a negative-binomial distribution was estimated to be around 0.1 (median estimate 0.1; 95% CrI: 0.05-0.2 for R0 = 2.5), suggesting that 80% of secondary transmissions may have been caused by a small fraction of infectious individuals (~10%). A joint estimation yielded likely ranges for R0 and k (95% CrIs: R0 1.4-12; k 0.04-0.2); however, the upper bound of R0 was not well informed by the model and data, which did not notably differ from that of the prior distribution. Conclusions: Our finding of a highly-overdispersed offspring distribution highlights a potential benefit to focusing intervention efforts on superspreading. As most infected individuals do not contribute to the expansion of an epidemic, the effective reproduction number could be drastically reduced by preventing relatively rare superspreading events.

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