Estimation of the serial interval and basic reproduction number of COVID‐19 in Qom, Iran, and three other countries: A data‐driven analysis in the early phase of the outbreak

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.

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COVID-19 pandemic scenario in India compared to China and rest of the world: a data driven and model analysis

10.1101/2020.04.20.20072744 ◽

2020 ◽

Cited By ~ 6

Author(s):

Manisha Mandal ◽

Shyamapada Mandal

Keyword(s):

Reproduction Number ◽

Case Fatality ◽

Respiratory Illness ◽

Data Driven ◽

Daily Growth Rate ◽

Daily Growth ◽

Epidemic Size ◽

Serial Interval ◽

The World ◽

Before And After

AbstractThe COVID-19 is a rapidly spreading respiratory illness caused with the infection of SARS-CoV-2. The COVID-19 data from India was compared with China and rest of the world. The average values of daily growth rate (DGR), case recovery rate (CRR), case fatality rate (CFR), serial interval (SI) of COVID-19 in India was 17%, 8.25%, and 1.87%, and 5.76 days respectively, as of April 9, 2020. The data driven estimates of basic reproduction number (R0), average reproduction number (R) and effective reproduction number (Re) were 1.03, 1.73, and 1.35, respectively. The results of exponential and SIR model showed higher estimates of R0, R and Re. The data driven as well as estimated COVID-19 cases reflect the growing nature of the epidemic in India and world excluding China, whereas the same in China reveal the involved population became infected with the disease and moved into the recovered stage. The epidemic size of India was estimated to be ∼30,284 (as of April 15, 2020 with 12,370 infectious cases) with an estimated end of the epidemic on June 9, 2020. The Re values in India before and after lockdown were 1.62 and 1.37 respectively, with SI 5.52 days and 5.98 days, respectively, as of April 17, 2020, reflecting the effectiveness of lockdown strategies. Beyond April 17, 2020, our estimate of 24,431 COVID-19 infected cases with lockdown is 78% lower compared to the 112,042 case estimates in absence of lockdown, on April 27, 2020. To early end of the COVID-19 epidemic, strong social distancing is important.

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The long-term dynamics of tuberculosis and other diseases with long serial intervals: implications of and for changing reproduction numbers

Epidemiology and Infection ◽

10.1017/s0950268898001113 ◽

1998 ◽

Vol 121 (2) ◽

pp. 309-324 ◽

Cited By ~ 36

Author(s):

E. VYNNYCKY ◽

P. E. M. FINE

Keyword(s):

Infectious Disease ◽

Basic Reproduction Number ◽

Reproduction Number ◽

England And Wales ◽

Transmission Potential ◽

Reproduction Numbers ◽

Serial Interval ◽

Net Reproduction ◽

Epidemiological Parameters ◽

The Basic Reproduction Number

The net and basic reproduction numbers are among the most widely-applied concepts in infectious disease epidemiology. A net reproduction number (the average number of secondary infectious cases resulting from each case in a given population) of above 1 is conventionally associated with an increase in incidence; the basic reproduction number (defined analogously for a ‘totally susceptible’ population) provides a standard measure of the ‘transmission potential’ of an infection. Using a model of the epidemiology of tuberculosis in England and Wales since 1900, we demonstrate that these measures are difficult to apply if disease can follow reinfection, and that they lose their conventional interpretations if important epidemiological parameters, such as the rate of contact between individuals, change over the time interval between successive cases in a chain of transmission (the serial interval).The net reproduction number for tuberculosis in England and Wales appears to have been approximately 1 from 1900 until 1950, despite concurrent declines in morbidity and mortality rates, and it declined rapidly in the second half of this century. The basic reproduction number declined from about 3 in 1900, reached 2 by 1950, and first fell below 1 in about 1960. Reductions in effective contact between individuals over this period, measured in terms of the average number of individuals to whom each case could transmit the infection, meant that the conventional basic reproduction number measure (which does not consider subsequent changes in epidemiological parameters) for a given year failed to reflect the ‘actual transmission potential’ of the infection. This latter property is better described by a variant of the conventional measure which takes secular trends in contact into account. These results are relevant for the interpretation of trends in any infectious disease for which epidemiological parameters change over time periods comparable to the infectious period, incubation period or serial interval.

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Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak

10.1101/2020.01.23.916395 ◽

2020 ◽

Cited By ~ 72

Author(s):

Shi Zhao ◽

Qianyin Lin ◽

Jinjun Ran ◽

Salihu S Musa ◽

Guangpu Yang ◽

...

Keyword(s):

Basic Reproduction Number ◽

Exponential Growth ◽

Early Phase ◽

Reproduction Number ◽

Fold Increase ◽

Reporting Rate ◽

The Mean ◽

Novel Coronavirus ◽

The Impact ◽

The Basic Reproduction Number

AbstractBackgroundsAn ongoing outbreak of a novel coronavirus (2019-nCoV) pneumonia hit a major city of China, Wuhan, December 2019 and subsequently reached other provinces/regions of China and countries. We present estimates of the basic reproduction number, R0, of 2019-nCoV in the early phase of the outbreak.MethodsAccounting for the impact of the variations in disease reporting rate, we modelled the epidemic curve of 2019-nCoV cases time series, in mainland China from January 10 to January 24, 2020, through the exponential growth. With the estimated intrinsic growth rate (γ), we estimated R0 by using the serial intervals (SI) of two other well-known coronavirus diseases, MERS and SARS, as approximations for the true unknown SI.FindingsThe early outbreak data largely follows the exponential growth. We estimated that the mean R0 ranges from 2.24 (95%CI: 1.96-2.55) to 3.58 (95%CI: 2.89-4.39) associated with 8-fold to 2-fold increase in the reporting rate. We demonstrated that changes in reporting rate substantially affect estimates of R0.ConclusionThe mean estimate of R0 for the 2019-nCoV ranges from 2.24 to 3.58, and significantly larger than 1. Our findings indicate the potential of 2019-nCoV to cause outbreaks.

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Transmission interval estimates suggest pre-symptomatic spread of COVID-19

10.1101/2020.03.03.20029983 ◽

2020 ◽

Cited By ~ 76

Author(s):

Lauren C. Tindale ◽

Michelle Coombe ◽

Jessica E. Stockdale ◽

Emma S. Garlock ◽

Wing Yin Venus Lau ◽

...

Keyword(s):

Incubation Period ◽

Basic Reproduction Number ◽

Reproduction Number ◽

Interval Estimates ◽

Secondary Infections ◽

Serial Interval ◽

Incubation Periods ◽

Key Variables ◽

The Mean ◽

Interval Distribution

AbstractBackgroundAs the COVID-19 epidemic is spreading, incoming data allows us to quantify values of key variables that determine the transmission and the effort required to control the epidemic. We determine the incubation period and serial interval distribution for transmission clusters in Singapore and in Tianjin. We infer the basic reproduction number and identify the extent of pre-symptomatic transmission.MethodsWe collected outbreak information from Singapore and Tianjin, China, reported from Jan.19-Feb.26 and Jan.21-Feb.27, respectively. We estimated incubation periods and serial intervals in both populations.ResultsThe mean incubation period was 7.1 (6.13, 8.25) days for Singapore and 9 (7.92, 10.2) days for Tianjin. Both datasets had shorter incubation periods for earlier-occurring cases. The mean serial interval was 4.56 (2.69, 6.42) days for Singapore and 4.22 (3.43, 5.01) for Tianjin. We inferred that early in the outbreaks, infection was transmitted on average 2.55 and 2.89 days before symptom onset (Singapore, Tianjin). The estimated basic reproduction number for Singapore was 1.97 (1.45, 2.48) secondary cases per infective; for Tianjin it was 1.87 (1.65, 2.09) secondary cases per infective.ConclusionsEstimated serial intervals are shorter than incubation periods in both Singapore and Tianjin, suggesting that pre-symptomatic transmission is occurring. Shorter serial intervals lead to lower estimates of R0, which suggest that half of all secondary infections should be prevented to control spread.

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Epidemiologic Characteristics of COVID-19 in Guizhou, China

10.1101/2020.03.01.20028944 ◽

2020 ◽

Cited By ~ 4

Author(s):

Kaike Ping ◽

Mingyu Lei ◽

Yun Gou ◽

Ying Tao ◽

Yan Huang

Keyword(s):

Incubation Period ◽

Basic Reproduction Number ◽

Reproduction Number ◽

Demographic Characteristics ◽

Guizhou Province ◽

Serial Interval ◽

Epidemiologic Characteristics

AbstractAt the end of 2019, a coronavirus disease 2019 (COVID-19) outbroke in Wuhan, China, and spread to Guizhou province on January of 2020. To acquire the epidemiologic characteristics of COVID-19 in Guizhou, China, we collected data on 162 laboratory-confirmed cases related to COVID-19. We described the demographic characteristics of the cases and estimated the incubation period, serial interval and basic reproduction number. With an estimation of 8 days incubation period and 6 days serial interval, our results indicate that there may exist infectiousness during the incubation period for 2019-nCoV. This increases the difficulty of screening or identifying cases related to COVID-19.

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Serial interval, basic reproduction number and prediction of COVID-19 epidemic size in Jodhpur, India

10.1101/2020.07.03.20146167 ◽

2020 ◽

Author(s):

Suman Saurabh ◽

Mahendra Kumar Verma ◽

Vaishali Gautam ◽

Akhil Goel ◽

Manoj Kumar Gupta ◽

...

Keyword(s):

Basic Reproduction Number ◽

Capacity Planning ◽

Reproduction Number ◽

Local Level ◽

Substantial Reduction ◽

Control Measures ◽

Contact Tracing ◽

Effective Control ◽

Epidemic Size ◽

Serial Interval

ABSTRACTBackgroundUnderstanding the epidemiology of COVID-19 is important for design of effective control measures at local level. We aimed to estimate the serial interval and basic reproduction number for Jodhpur, India and to use it for prediction of epidemic size for next one month.MethodsContact tracing of SARS-CoV-2 infected individuals was done to obtain the serial intervals. Aggregate and instantaneous R0 values were derived and epidemic projection was done using R software v4.0.0.ResultsFrom among 79 infector-infectee pairs, the estimated median and 95 percentile values of serial interval were 5.98 days (95% CI 5.39 – 6.65) and 13.17 days (95% CI 11.27 – 15.57), respectively. The overall R0 value in the first 30 days of outbreak was 1.64 (95% CI 1.12 – 2.25) which subsequently decreased to 1.07 (95% CI 1.06 – 1.09). The instantaneous R0 value over 14 days window ranged from a peak of 3.71 (95% CI 1.85 -2.08) to 0.88 (95% CI 0.81 – 0.96) as on 24 June 2020. The projected COVID-19 case-load over next one month was 1881 individuals. Reduction of R0 from 1.17 to 1.085 could result in 23% reduction in projected epidemic size over the next one month.ConclusionAggressive testing, contact-tracing and isolation of infected individuals in Jodhpur district resulted in reduction of R0. Further strengthening of control measures could lead to substantial reduction of COVID-19 epidemic size. A data-driven strategy was found useful in surge capacity planning and guiding the public health strategy at local level.

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