Serial interval and time-varying reproduction number estimation for COVID-19 in western Iran

BackgroundThe 2019-nCoV outbreak in Wuhan, China has attracted world-wide attention. As of February 11, 2020, a total of 44730 cases of novel coronavirus-infected pneumonia associated with COVID-19 were confirmed by the National Health Commission of China.MethodsThree approaches, namely Poisson likelihood-based method (ML), exponential growth rate-based method (EGR) and stochastic Susceptible-Infected-Removed dynamic model-based method (SIR), were implemented to estimate the basic and controlled reproduction numbers.ResultsA total of 71 chains of transmission together with dates of symptoms onset and 67 dates of infections were identified among 5405 confirmed cases outside Hubei as reported by February 2, 2020. Based on this information, we find the serial interval having an average of 4.41 days with a standard deviation of 3.17 days and the infectious period having an average of 10.91 days with a standard deviation of 3.95 days.ConclusionsThe controlled reproduction number is declining. It is lower than one in most regions of China, but is still larger than one in Hubei Province. Sustained efforts are needed to further reduce the Rc to below one in order to end the current epidemic.

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Nine-month Trend of Time-Varying Reproduction Numbers of COVID-19 in West of Iran

Journal of Research in Health Sciences ◽

10.34172/jrhs.2021.54 ◽

2021 ◽

Vol 21 (2) ◽

pp. e00517-e00517

Author(s):

Ebrahim Rahimi ◽

Seyed Saeed Hashemi Nazari ◽

Yaser Mokhayeri ◽

Asaad Sharhani ◽

Rasool Mohammadi

Keyword(s):

Reproduction Number ◽

Infectious Period ◽

Time Varying ◽

Cross Sectional ◽

Disease Epidemiology ◽

Infectious Disease Epidemiology ◽

Contact Rates ◽

Reproduction Numbers ◽

Serial Interval ◽

Log Normal

Background: The basic reproduction number (R0) is an important concept in infectious disease epidemiology and the most important parameter to determine the transmissibility of a pathogen. This study aimed to estimate the nine-month trend of time-varying R of COVID-19 epidemic using the serial interval (SI) and Markov Chain Monte Carlo in Lorestan, west of Iran. Study design: Descriptive study. Methods: This study was conducted based on a cross-sectional method. The SI distribution was extracted from data and log-normal, Weibull, and Gamma models were fitted. The estimation of time-varying R0, a likelihood-based model was applied, which uses pairs of cases to estimate relative likelihood. Results: In this study, Rt was estimated for SI 7-day and 14-day time-lapses from 27 February-14 November 2020. To check the robustness of the R0 estimations, sensitivity analysis was performed using different SI distributions to estimate the reproduction number in 7-day and 14-day time-lapses. The R0 ranged from 0.56 to 4.97 and 0.76 to 2.47 for 7-day and 14-day time-lapses. The doubling time was estimated to be 75.51 days (95% CI: 70.41, 81.41). Conclusions: Low R0 of COVID-19 in some periods in Lorestan, west of Iran, could be an indication of preventive interventions, namely quarantine and isolation. To control the spread of the disease, the reproduction number should be reduced by decreasing the transmission and contact rates and shortening the infectious period.

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LOCKDOWN AS A PANDEMIC MITIGATING POLICY INTERVENTION IN INDIA

10.1101/2020.06.19.20134437 ◽

2020 ◽

Cited By ~ 1

Author(s):

Subhayan Mandal ◽

Manoj Kumar ◽

Debasish Sarkar

Keyword(s):

Reproduction Number ◽

Interval Data ◽

Viral Disease ◽

Policy Intervention ◽

Time Varying ◽

Indian States ◽

Serial Interval ◽

Nature Of Time ◽

Containment Policy ◽

Positive Effect

AbstractWe use publicly available timeline data on the Covid-19 outbreak for nine indian states to calculate the important quantifier of the outbreak, the sought after Rt or the time varying reproduction number of the outbreak. This quantity can be measured in in several ways, e.g. by application of Stochastic compartmentalised SIR (DCM) model, Poissonian likelihood based (ML) model & the exponential growth rate (EGR) model. The third one is known as the effective reproduction number of an outbreak. Here we use, mostly, the second one. It is known as the instantaneous reproduction number for an outbreak. This number can faithfully tell us the success of lockdown measures inside indian states, as containment policy for the spread of Covid-19 viral disease. This can also, indirectly yield notional value of the generation time inteval in different states. In doing this work we employ, pan India serial interval of the outbreak estimated directly from data from January 30th to April 19th, 2020. Simultaneously, in conjunction with the serial interval data, our result is derived from incidences data between March 14th, 2020 to June 1st, 2020, for the said states. We find the lockdown had marked positive effect on the nature of time dependent reproduction number in most of the Indian states, barring a couple. The possible reason for such failures have been investigated.

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Estimation the state of the Covid-19 epidemic curve in Mayotte

10.1101/2021.12.30.21268571 ◽

2022 ◽

Author(s):

Solym Mawaki MANOU-ABI ◽

Yousri SLAOUI ◽

Julien BALICCHI

Keyword(s):

Reproduction Number ◽

Time Varying ◽

Pharmacological Interventions ◽

Epidemic Curve ◽

Estimated Parameters ◽

Serial Interval ◽

Probable Impact ◽

Age Structured ◽

Best Fit ◽

Interval Distribution

We study in this work some statistical methods to estimate the parameters resulting from the use of an age-structured contact mathematical epidemic model in order to analyze the evolution of the epidemic curve of Covid-19 in the French overseas department Mayotte from march 13, 2020 to february 26,2021. Using several statistic methods based on time dependent method, maximum likelihood, mixture method, we fit the probability distribution which underlines the serial interval distribution and we give an adapted version of the generation time distribution from Package R0. The best-fit model of the serial interval was given by a mixture of Weibull distribution. Furthermore this estimation allows to obtain the evolution of the time varying effective reproduction number and hence the temporal transmission rates. Finally based on others known estimates parameters we incorporate the estimated parameters in the model in order to give an approximation of the epidemic curve in Mayotte under the conditions of the model. We also discuss the limit of our study and the conclusion concerned a probable impact of non pharmacological interventions of the Covid-19 in Mayotte such us the re-infection cases and the introduction of the variants which probably affect the estimates.

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Serial interval and generation interval for respectively the imported and local infectors estimated using reported contact-tracing data of COVID-19 in China

10.1101/2020.04.15.20065946 ◽

2020 ◽

Cited By ~ 3

Author(s):

Menghui Li ◽

Kai Liu ◽

Yukun Song ◽

Ming Wang ◽

Jinshan Wu

Keyword(s):

Reproduction Number ◽

Contact Tracing ◽

Time Varying ◽

Generation Interval ◽

2Nd Generation ◽

Serial Interval ◽

Interval Censored ◽

Imported Cases ◽

Emerging Virus ◽

Censored Likelihood

AbstractBackgroundsThe emerging virus, COVID-19, has caused a massive out-break worldwide. Based on the publicly available contact-tracing data, we identified 337 transmission chains from 10 provinces in China and estimated the serial interval (SI) and generation interval (GI) of COVID-19 in China.MethodsInspired by possibly different values of the time-varying reproduction number for the imported cases and the local cases in China, we divided all transmission events into three subsets: imported (the zeroth generation) infecting 1st-generation locals, 1st-generation locals infecting 2nd-generation locals, and others transmissions among 2+ generations. The corresponding SI (GI) is respec-tively denoted as , and . A Bayesian approach with doubly interval-censored likelihood is employed to fit the lognormal, gamma, and Weibull distribution function of the SI and GI using the identified 337 transmission chains.FindingsIt is found that the estimated , and , thus overall both SI and GI decrease when generation increases.

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Heterogeneity and effectiveness analysis of COVID-19 prevention and control in major cities in China through time-varying reproduction number estimation

Scientific Reports ◽

10.1038/s41598-020-79063-x ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Qing Cheng ◽

Zeyi Liu ◽

Guangquan Cheng ◽

Jincai Huang

Keyword(s):

Prevention And Control ◽

Large Scale ◽

Reproduction Number ◽

Correlation Method ◽

Control Measures ◽

Time Varying ◽

Short Period ◽

Significant Difference ◽

Prevention And Control Measures ◽

And Control

AbstractBeginning on December 31, 2019, the large-scale novel coronavirus disease 2019 (COVID-19) emerged in China. Tracking and analysing the heterogeneity and effectiveness of cities’ prevention and control of the COVID-19 epidemic is essential to design and adjust epidemic prevention and control measures. The number of newly confirmed cases in 25 of China’s most-affected cities for the COVID-19 epidemic from January 11 to February 10 was collected. The heterogeneity and effectiveness of these 25 cities’ prevention and control measures for COVID-19 were analysed by using an estimated time-varying reproduction number method and a serial correlation method. The results showed that the effective reproduction number (R) in 25 cities showed a downward trend overall, but there was a significant difference in the R change trends among cities, indicating that there was heterogeneity in the spread and control of COVID-19 in cities. Moreover, the COVID-19 control in 21 of 25 cities was effective, and the risk of infection decreased because their R had dropped below 1 by February 10, 2020. In contrast, the cities of Wuhan, Tianmen, Ezhou and Enshi still had difficulty effectively controlling the COVID-19 epidemic in a short period of time because their R was greater than 1.

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Development of the reproduction number from coronavirus SARS-CoV-2 case data in Germany and implications for political measures

BMC Medicine ◽

10.1186/s12916-020-01884-4 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Sahamoddin Khailaie ◽

Tanmay Mitra ◽

Arnab Bandyopadhyay ◽

Marta Schips ◽

Pietro Mascheroni ◽

...

Keyword(s):

Structural Changes ◽

Reproduction Number ◽

National Level ◽

Transmission Dynamics ◽

Federal State ◽

Parameter Estimates ◽

Time Varying ◽

Short Term ◽

Robert Koch Institute ◽

Federal States

Abstract Background SARS-CoV-2 has induced a worldwide pandemic and subsequent non-pharmaceutical interventions (NPIs) to control the spread of the virus. As in many countries, the SARS-CoV-2 pandemic in Germany has led to a consecutive roll-out of different NPIs. As these NPIs have (largely unknown) adverse effects, targeting them precisely and monitoring their effectiveness are essential. We developed a compartmental infection dynamics model with specific features of SARS-CoV-2 that allows daily estimation of a time-varying reproduction number and published this information openly since the beginning of April 2020. Here, we present the transmission dynamics in Germany over time to understand the effect of NPIs and allow adaptive forecasts of the epidemic progression. Methods We used a data-driven estimation of the evolution of the reproduction number for viral spreading in Germany as well as in all its federal states using our model. Using parameter estimates from literature and, alternatively, with parameters derived from a fit to the initial phase of COVID-19 spread in different regions of Italy, the model was optimized to fit data from the Robert Koch Institute. Results The time-varying reproduction number (Rt) in Germany decreased to <1 in early April 2020, 2–3 weeks after the implementation of NPIs. Partial release of NPIs both nationally and on federal state level correlated with moderate increases in Rt until August 2020. Implications of state-specific Rt on other states and on national level are characterized. Retrospective evaluation of the model shows excellent agreement with the data and usage of inpatient facilities well within the healthcare limit. While short-term predictions may work for a few weeks, long-term projections are complicated by unpredictable structural changes. Conclusions The estimated fraction of immunized population by August 2020 warns of a renewed outbreak upon release of measures. A low detection rate prolongs the delay reaching a low case incidence number upon release, showing the importance of an effective testing-quarantine strategy. We show that real-time monitoring of transmission dynamics is important to evaluate the extent of the outbreak, short-term projections for the burden on the healthcare system, and their response to policy changes.

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Quantifying TB transmission: a systematic review of reproduction number and serial interval estimates for tuberculosis

Epidemiology and Infection ◽

10.1017/s0950268818001760 ◽

2018 ◽

Vol 146 (12) ◽

pp. 1478-1494 ◽

Cited By ~ 12

Author(s):

Y. Ma ◽

C. R. Horsburgh ◽

L. F. White ◽

H. E. Jenkins

Keyword(s):

Systematic Review ◽

Parameter Estimation ◽

The Netherlands ◽

Cause Of Death ◽

Reproduction Number ◽

Reproductive Number ◽

Interval Estimates ◽

Transmission Parameters ◽

Serial Interval ◽

Number Of Publications

AbstractTuberculosis (TB) is the leading global infectious cause of death. Understanding TB transmission is critical to creating policies and monitoring the disease with the end goal of TB elimination. To our knowledge, there has been no systematic review of key transmission parameters for TB. We carried out a systematic review of the published literature to identify studies estimating either of the two key TB transmission parameters: the serial interval (SI) and the reproductive number. We identified five publications that estimated the SI and 56 publications that estimated the reproductive number. The SI estimates from four studies were: 0.57, 1.42, 1.44 and 1.65 years; the fifth paper presented age-specific estimates ranging from 20 to 30 years (for infants <1 year old) to <5 years (for adults). The reproductive number estimates ranged from 0.24 in the Netherlands (during 1933–2007) to 4.3 in China in 2012. We found a limited number of publications and many high TB burden settings were not represented. Certain features of TB dynamics, such as slow transmission, complicated parameter estimation, require novel methods. Additional efforts to estimate these parameters for TB are needed so that we can monitor and evaluate interventions designed to achieve TB elimination.

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Early Transmission Dynamics of Novel Coronavirus (COVID-19) in Nigeria

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17093054 ◽

2020 ◽

Vol 17 (9) ◽

pp. 3054 ◽

Cited By ~ 14

Author(s):

Oyelola A. Adegboye ◽

Adeshina I. Adekunle ◽

Ezra Gayawan

Keyword(s):

Doubling Time ◽

Reproduction Number ◽

Infected Individual ◽

Credible Interval ◽

World Health ◽

Time Interval ◽

Serial Interval ◽

Imported Cases ◽

Novel Coronavirus ◽

Health Organization

On 31 December 2019, the World Health Organization (WHO) was notified of a novel coronavirus disease in China that was later named COVID-19. On 11 March 2020, the outbreak of COVID-19 was declared a pandemic. The first instance of the virus in Nigeria was documented on 27 February 2020. This study provides a preliminary epidemiological analysis of the first 45 days of COVID-19 outbreak in Nigeria. We estimated the early transmissibility via time-varying reproduction number based on the Bayesian method that incorporates uncertainty in the distribution of serial interval (time interval between symptoms onset in an infected individual and the infector), and adjusted for disease importation. By 11 April 2020, 318 confirmed cases and 10 deaths from COVID-19 have occurred in Nigeria. At day 45, the exponential growth rate was 0.07 (95% confidence interval (CI): 0.05–0.10) with a doubling time of 9.84 days (95% CI: 7.28–15.18). Separately for imported cases (travel-related) and local cases, the doubling time was 12.88 days and 2.86 days, respectively. Furthermore, we estimated the reproduction number for each day of the outbreak using a three-weekly window while adjusting for imported cases. The estimated reproduction number was 4.98 (95% CrI: 2.65–8.41) at day 22 (19 March 2020), peaking at 5.61 (95% credible interval (CrI): 3.83–7.88) at day 25 (22 March 2020). The median reproduction number over the study period was 2.71 and the latest value on 11 April 2020, was 1.42 (95% CrI: 1.26–1.58). These 45-day estimates suggested that cases of COVID-19 in Nigeria have been remarkably lower than expected and the preparedness to detect needs to be shifted to stop local transmission.

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