China’s effective control and other countries’ uncharted challenge against COVID-19: an epidemiological and modelling study

Abstract Objective: In this study, we use the time-dependent reproduction number (Rt) to comprise the COVID transmissibility across different countries.Methods: We used data from Jan 20, 2019, to Feb 29, 2020, on the number of newly confirmed cases, obtained from the reports published by the CDC, to infer the incidence of infectious over time. A two-step procedure was used to estimate the Rt. The first step used data on known index-secondary cases pairs, from publicly available case reports, to estimate the serial interval distribution. The second step estimated the Rt jointly from the incidence data and the information data in the first step. Rt was then used to simulate the epidemics across all major cities in China and typical countries worldwide. Results: Based on a total of 126 index-secondary cases pairs from 4 international regions, we estimated that the serial interval for SARS-2-CoV was 4.18 (IQR 1.92 – 6.65) days. Domestically, Rt of China, Hubei province, Wuhan had fallen below 1.0 on 9 Feb, 10 Feb and 13 Feb (Rt were 0.99±0.02, 0.99±0.02 and 0.96±0.02), respectively. Internationally, as of 26 Feb, statistically significant periods of COVID spread (Rt >1) were identified for most regions, except for Singapore (Rt was 0.92±0.17).Conclusions: The epidemic in China has been well controlled, but the worldwide pandemic has not been well controlled. Worldwide preparedness and vulnerability against COVID-19 should be regarded with more care.

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China’s effective control and other countries’ uncharted challenge against COVID-19: an epidemiological and modelling study (Preprint)

10.2196/preprints.18661 ◽

2020 ◽

Author(s):

Lingling Zheng ◽

Kang Qin ◽

Xiujuan Chen ◽

Shuai Huang ◽

Dong Liu ◽

...

Keyword(s):

Reproduction Number ◽

Case Reports ◽

Effective Control ◽

Second Step ◽

Global Epidemic ◽

Incidence Data ◽

Step Procedure ◽

Serial Interval ◽

More Care ◽

Interval Distribution

BACKGROUND On the present trajectory, COVID is inevitably becoming a global epidemic, leading to concerns regarding the pandemic potential in China and other countries. OBJECTIVE In this study, we use the time-dependent reproduction number (Rt) to comprise the COVID transmissibility across different countries. METHODS We used data from Jan 20, 2019, to Feb 29, 2020, on the number of newly confirmed cases, obtained from the reports published by the CDC, to infer the incidence of infectious over time. A two-step procedure was used to estimate the Rt. The first step used data on known index-secondary cases pairs, from publicly available case reports, to estimate the serial interval distribution. The second step estimated the Rt jointly from the incidence data and the information data in the first step. Rt was then used to simulate the epidemics across all major cities in China and typical countries worldwide. RESULTS Based on a total of 126 index-secondary cases pairs from 4 international regions, we estimated that the serial interval for SARS-2-CoV was 4.18. Domestically, Rt of China, Hubei province, Wuhan had fallen below 1.0 on 9 Feb, 10 Feb and 13 Feb, respectively. Internationally, as of 26 Feb, statistically significant periods of COVID spread (Rt >1) were identified for most regions, except for Singapore. CONCLUSIONS The epidemic in China has been well controlled, but the worldwide pandemic has not been well controlled. Worldwide preparedness and vulnerability against COVID-19 should be regarded with more care.

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China’s effective control and other countries’ uncharted challenge against COVID-19: an epidemiological and modelling study

10.1101/2020.04.28.20083899 ◽

2020 ◽

Author(s):

Lingling Zheng ◽

Qin Kang ◽

Weiyao Liao ◽

Xiujuan Chen ◽

Shuai Huang ◽

...

Keyword(s):

Disease Transmission ◽

Reproduction Number ◽

Case Reports ◽

Epidemiological Data ◽

Control Measures ◽

Time Dependent ◽

Effective Control ◽

List Type ◽

Global Epidemic ◽

Serial Interval

AbstractBackgroundOn the present trajectory, COVID is inevitably becoming a global epidemic, leading to concerns regarding the pandemic potential in China and other countries.ObjectiveIn this study, we use the time-dependent reproduction number (Rt) to comprise the COVID transmissibility across different countries.MethodsWe used data from Jan 20, 2019, to Feb 29, 2020, on the number of newly confirmed cases, obtained from the reports published by the CDC, to infer the incidence of infectious over time. A two-step procedure was used to estimate the Rt. The first step used data on known index-secondary cases pairs, from publicly available case reports, to estimate the serial interval distribution. The second step estimated the Rt jointly from the incidence data and the information data in the first step. Rt was then used to simulate the epidemics across all major cities in China and typical countries worldwide.ResultsBased on a total of 126 index-secondary cases pairs from 4 international regions, we estimated that the serial interval for SARS-2-CoV was 4.18 (IQR 1.92 – 6.65) days. Domestically, Rt of China, Hubei province, Wuhan had fallen below 1.0 on 9 Feb, 10 Feb and 13 Feb (Rt were 0.99±0.02, 0.99±0.02 and 0.96±0.02), respectively. Internationally, as of 26 Feb, statistically significant periods of COVID spread (Rt >1) were identified for most regions, except for Singapore (Rt was 0.92±0.17).ConclusionsThe epidemic in China has been well controlled, but the worldwide pandemic has not been well controlled. Worldwide preparedness and vulnerability against COVID-19 should be regarded with more care.What is already known on this subject?The basic reproduction number (R0) and the-time-dependent reproduction number (Rt) are two important indicators of infectious disease transmission. In addition, Rt as a derivative of R0 could be used to assess the epidemiological development of the disease and effectiveness of control measures. Most current researches used data from earlier periods in Wuhan and refer to the epidemiological features of SARS, which are possibly biased. Meanwhile, there are fewer studies discussed the Rt of COVID-19. Current clinical and epidemiological data are insufficient to help us understand the full view of the potential transmission of this disease.What this study adds?We use up-to-data observation of the serial interval and cases arising from local transmission to calculate the Rt in different outbreak level area and every province in China as well as five-top sever outbreak countries and other overseas. By comparing the Rt, we discussed the situation of outbreak around the world.

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Type and quantity of data needed for an early estimate of transmissibility when an infectious disease emerges

Eurosurveillance ◽

10.2807/ese.15.26.19603-en ◽

2010 ◽

Vol 15 (26) ◽

Author(s):

N G Becker ◽

D Wang ◽

M Clements

Keyword(s):

Public Health ◽

Infectious Disease ◽

Reproduction Number ◽

Wave Pattern ◽

Public Health Response ◽

Incidence Data ◽

Early Estimate ◽

Health Response ◽

Serial Interval ◽

Interval Distribution

An early estimate of disease transmissibility is essential for a well-informed public health response to a newly emerged infectious disease. In this study, we ask what type and quantity of data are needed for useful estimation of the initial reproduction number (R). It is possible to estimate R from case incidence data alone when the growing incidence of cases displays a wave pattern, because the pattern provides information about the serial interval (the time elapsed between the onset of symptoms of a case and symptom onset in individuals infected by that case). When the mode of the serial interval distribution is small, 1.5 days or less, there is generally no informative wave pattern in the observed series of daily incidences. The precision of the estimate of R is then improved substantially by having some observations on the serial interval. For an infectious disease with characteristics such as those of influenza, an estimate of R able to inform plans to mitigate transmission is obtained when the cumulative incidence of cases reaches about 300 and about 10 observations on the serial interval are available.

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

Epidemiology and Infection ◽

10.1017/s0950268820001247 ◽

2020 ◽

Vol 148 ◽

Cited By ~ 1

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.

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Theoretical investigation of pre-symptomatic SARS-CoV-2 person-to-person transmission in households

Scientific Reports ◽

10.1038/s41598-021-93579-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yehuda Arav ◽

Ziv Klausner ◽

Eyal Fattal

Keyword(s):

Scientific Basis ◽

Control Measures ◽

Physical Contact ◽

Effective Control ◽

Mathematical Framework ◽

Effective Prevention ◽

Modes Of Transmission ◽

Serial Interval ◽

The Common ◽

Interval Distribution

AbstractSince its emergence, the phenomenon of SARS-CoV-2 transmission by seemingly healthy individuals has become a major challenge in the effort to achieve control of the pandemic. Identifying the modes of transmission that drive this phenomenon is a perquisite in devising effective control measures, but to date it is still under debate. To address this problem, we have formulated a detailed mathematical model of discrete human actions (such as coughs, sneezes, and touching) and the continuous decay of the virus in the environment. To take into account those discrete and continuous events we have extended the common modelling approach and employed a hybrid stochastic mathematical framework. This allowed us to calculate higher order statistics which are crucial for the reconstruction of the observed distributions. We focused on transmission within a household, the venue with the highest risk of infection and validated the model results against the observed secondary attack rate and the serial interval distribution. Detailed analysis of the model results identified the dominant driver of pre-symptomatic transmission as the contact route via hand-face transfer and showed that wearing masks and avoiding physical contact are an effective prevention strategy. These results provide a sound scientific basis to the present recommendations of the WHO and the CDC.

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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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On realized serial and generation intervals given control measures: The COVID-19 pandemic case

PLoS Computational Biology ◽

10.1371/journal.pcbi.1008892 ◽

2021 ◽

Vol 17 (3) ◽

pp. e1008892

Author(s):

Andrea Torneri ◽

Pieter Libin ◽

Gianpaolo Scalia Tomba ◽

Christel Faes ◽

James G. Wood ◽

...

Keyword(s):

Reproduction Number ◽

Transmission Probability ◽

Theoretical Explanation ◽

Control Measures ◽

Strong Impact ◽

Generation Interval ◽

Incidence Data ◽

Intervention Measures ◽

Serial Interval ◽

The Mean

The SARS-CoV-2 pathogen is currently spreading worldwide and its propensity for presymptomatic and asymptomatic transmission makes it difficult to control. The control measures adopted in several countries aim at isolating individuals once diagnosed, limiting their social interactions and consequently their transmission probability. These interventions, which have a strong impact on the disease dynamics, can affect the inference of the epidemiological quantities. We first present a theoretical explanation of the effect caused by non-pharmaceutical intervention measures on the mean serial and generation intervals. Then, in a simulation study, we vary the assumed efficacy of control measures and quantify the effect on the mean and variance of realized generation and serial intervals. The simulation results show that the realized serial and generation intervals both depend on control measures and their values contract according to the efficacy of the intervention strategies. Interestingly, the mean serial interval differs from the mean generation interval. The deviation between these two values depends on two factors. First, the number of undiagnosed infectious individuals. Second, the relationship between infectiousness, symptom onset and timing of isolation. Similarly, the standard deviations of realized serial and generation intervals do not coincide, with the former shorter than the latter on average. The findings of this study are directly relevant to estimates performed for the current COVID-19 pandemic. In particular, the effective reproduction number is often inferred using both daily incidence data and the generation interval. Failing to account for either contraction or mis-specification by using the serial interval could lead to biased estimates of the effective reproduction number. Consequently, this might affect the choices made by decision makers when deciding which control measures to apply based on the value of the quantity thereof.

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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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Estimating effective reproduction number using generation time versus serial interval, with application to COVID-19 in the Greater Toronto Area, Canada

10.1101/2020.05.24.20109215 ◽

2020 ◽

Cited By ~ 1

Author(s):

Jesse Knight ◽

Sharmistha Mishra

Keyword(s):

Time Series ◽

Generation Time ◽

Time Distribution ◽

Reproduction Number ◽

Reproductive Number ◽

Transmission Potential ◽

Greater Toronto Area ◽

Critical Measure ◽

Serial Interval ◽

Interval Distribution

AbstractBackgroundThe effective reproductive number Re(t) is a critical measure of epidemic potential. Re(t) can be calculated in near real time using an incidence time series and the generation time distribution—the time between infection events in an infector-infectee pair. In calculating Re(t), the generation time distribution is often approximated by the serial interval distribution—the time between symptom onset in an infector-infectee pair. However, while generation time must be positive by definition, serial interval can be negative if transmission can occur before symptoms, such as in covid-19, rendering such an approximation improper in some contexts.MethodsWe developed a method to infer the generation time distribution from parametric definitions of the serial interval and incubation period distributions. We then compared estimates of Re(t) for covid-19 in the Greater Toronto Area of Canada using: negative-permitting versus non-negative serial interval distributions, versus the inferred generation time distribution.ResultsWe estimated the generation time of covid-19 to be Gamma-distributed with mean 3.99 and standard deviation 2.96 days. Relative to the generation time distribution, non-negative serial interval distribution caused overestimation of Re(t) due to larger mean, while negative-permitting serial interval distribution caused underestimation of Re(t) due to larger variance.ImplicationsApproximation of the generation time distribution of covid-19 with non-negative or negative-permitting serial interval distributions when calculating Re(t) may result in over or underestimation of transmission potential, respectively.

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

10.1101/2020.04.04.20052308 ◽

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.

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