Estimation of the Time Dependent Reproduction Number of Novel Coronavirus (COVID 19) for Turkey in the Late Stage of the Outbreak

BACKGROUND Since the beginning of the COVID-19 pandemic, researchers and health authorities have sought to identify the different parameters that govern their infection and death cycles, in order to be able to make better decisions. In particular, a series of reproduction number estimation models have been presented, with different practical results. OBJECTIVE This article aims to present an effective and efficient model for estimating the Reproduction Number and to discuss the impacts of sub-notification on these calculations. METHODS The concept of Moving Average Method with Initial value (MAMI) is used, as well as a model for Rt, the Reproduction Number, is derived from experimental data. The models are applied to real data and their performance is presented. RESULTS Analyses on Rt and sub-notification effects for Germany, Italy, Sweden, United Kingdom, South Korea, and the State of New York are presented to show the performance of the methods here introduced. CONCLUSIONS We show that, with relatively simple mathematical tools, it is possible to obtain reliable values for time-dependent, incubation period-independent Reproduction Numbers (Rt). We also demonstrate that the impact of sub-notification is relatively low, after the initial phase of the epidemic cycle has passed.

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Fractional SIR-Model for Estimating Transmission Dynamics of COVID-19 in India

J ◽

10.3390/j4020008 ◽

2021 ◽

Vol 4 (2) ◽

pp. 86-100

Author(s):

Nita H. Shah ◽

Ankush H. Suthar ◽

Ekta N. Jayswal ◽

Ankit Sikarwar

Keyword(s):

Basic Reproduction Number ◽

Reproduction Number ◽

Transmission Rate ◽

Sir Model ◽

Time Dependent ◽

Contact Rate ◽

Distribution Maps ◽

Fundamental Parameters ◽

Different Order ◽

The Basic Reproduction Number

In this article, a time-dependent susceptible-infected-recovered (SIR) model is constructed to investigate the transmission rate of COVID-19 in various regions of India. The model included the fundamental parameters on which the transmission rate of the infection is dependent, like the population density, contact rate, recovery rate, and intensity of the infection in the respective region. Looking at the great diversity in different geographic locations in India, we determined to calculate the basic reproduction number for all Indian districts based on the COVID-19 data till 7 July 2020. By preparing district-wise spatial distribution maps with the help of ArcGIS 10.2, the model was employed to show the effect of complete lockdown on the transmission rate of the COVID-19 infection in Indian districts. Moreover, with the model's transformation to the fractional ordered dynamical system, we found that the nature of the proposed SIR model is different for the different order of the systems. The sensitivity analysis of the basic reproduction number is done graphically which forecasts the change in the transmission rate of COVID-19 infection with change in different parameters. In the numerical simulation section, oscillations and variations in the model compartments are shown for two different situations, with and without lockdown.

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Transmission potential of COVID-19 in South Korea

10.1101/2020.02.27.20028829 ◽

2020 ◽

Cited By ~ 23

Author(s):

Eunha Shim ◽

Amna Tariq ◽

Wongyeong Choi ◽

Yiseul Lee ◽

Gerardo Chowell

Keyword(s):

South Korea ◽

Reproduction Number ◽

Social Distancing ◽

Transmission Potential ◽

Incidence Data ◽

Novel Coronavirus

AbstractSince the first identified individual of 2019 novel coronavirus (COVID-19) infection on Jan 20, 2020 in South Korea, the number of confirmed cases rapidly increased. As of Feb 26, 2020, 1,261 cases of COVID-19 including 12 deaths were confirmed in South Korea. Using the incidence data of COVID-19, we estimate the reproduction number at 1.5 (95% CI: 1.4-1.6), which indicates sustained transmission and support the implementation of social distancing measures to rapidly control the outbreak.

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Forecasting of COVID-19 Reproduction Number by ARIMA Methodology and Quantile Estimation based on Best Fit Distribution by L- moments for Top-10 Affected Countries

10.21203/rs.3.rs-471180/v1 ◽

2021 ◽

Author(s):

Muhammad Waqas ◽

Songhua Xu ◽

Linyun Zhou

Keyword(s):

Reproduction Number ◽

Quantile Estimation ◽

The Usa ◽

L Moments ◽

Short And Long Term ◽

Long Term Forecasting ◽

Novel Coronavirus ◽

The Uk ◽

Health Experts

Abstract We utilized the average weekly estimated reproduction number data of COVID-19 from March (2020–2021). By applying ARIMA and L-moments methodology, short-and-long-term forecasting of R0 is made for Govt. officials and public health experts to take before-time policy measures to control the spread of novel coronavirus. This study helps medical staff to measure the expected demand of COVID-19 vaccine doses. We applied various ARIMA models on each country’s data and the best selected based on RMSE, AIC, and BIC for point and interval forecasting. Application L-Moments techniques selected GLO, GEV, and GNO distributions and quantile estimation with return period calculations. The forecasting shows that maximum countries mean R0 > 1, which is still a serious threat and can lead to heath disaster. The forecasting provided an alarming situation in the coming months for India, France, Turkey, and Spain; health experts should take strict measures because the cases rise due to the high R0 forecast. The USA, Russia, and the UK mean R0 will not suddenly increase; these countries consistent in COVID-19 R0 control. We find that even the significant population differences prevail among selected countries, the R0 is still high in maximum countries, so its a dire need to take strict control actions to minimize the R0 for public safety.

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Early epidemiological assessment of the transmission potential and virulence of coronavirus disease 2019 (COVID-19) in Wuhan City: China, January-February, 2020

10.1101/2020.02.12.20022434 ◽

2020 ◽

Cited By ~ 40

Author(s):

Kenji Mizumoto ◽

Katsushi Kagaya ◽

Gerardo Chowell

Keyword(s):

Basic Reproduction Number ◽

Reproduction Number ◽

Public Health Intervention ◽

Ecological Modelling ◽

The Novel ◽

Wuhan City ◽

Transmission Potential ◽

The World ◽

Novel Coronavirus ◽

Epidemiological Parameters

AbstractBackgroundSince the first cluster of cases was identified in Wuhan City, China, in December, 2019, coronavirus disease 2019 (COVID-19) rapidly spread around the world. Despite the scarcity of publicly available data, scientists around the world have made strides in estimating the magnitude of the epidemic, the basic reproduction number, and transmission patterns. Accumulating evidence suggests that a substantial fraction of the infected individuals with the novel coronavirus show little if any symptoms, which highlights the need to reassess the transmission potential of this emerging disease. In this study, we derive estimates of the transmissibility and virulence of COVID-19 in Wuhan City, China, by reconstructing the underlying transmission dynamics using multiple data sources.MethodsWe employ statistical methods and publicly available epidemiological datasets to jointly derive estimates of transmissibility and severity associated with the novel coronavirus. For this purpose, the daily series of laboratory–confirmed COVID-19 cases and deaths in Wuhan City together with epidemiological data of Japanese repatriated from Wuhan City on board government–chartered flights were integrated into our analysis.ResultsOur posterior estimates of basic reproduction number (R) in Wuhan City, China in 2019–2020 reached values at 3.49 (95%CrI: 3.39–3.62) with a mean serial interval of 6.0 days, and the enhanced public health intervention after January 23rd in 2020 was associated with a significantly reduced R at 0.84 (95%CrI: 0.81–0.88), with the total number of infections (i.e. cumulative infections) estimated at 1906634 (95%CrI: 1373500–2651124) in Wuhan City, elevating the overall proportion of infected individuals to 19.1% (95%CrI: 13.5–26.6%). We also estimated the most recent crude infection fatality ratio (IFR) and time–delay adjusted IFR at 0.04% (95% CrI: 0.03%–0.06%) and 0.12% (95%CrI: 0.08–0.17%), respectively, estimates that are several orders of magnitude smaller than the crude CFR estimated at 4.06%ConclusionsWe have estimated key epidemiological parameters of the transmissibility and virulence of COVID-19 in Wuhan, China during January-February, 2020 using an ecological modelling approach. The power of this approach lies in the ability to infer epidemiological parameters with quantified uncertainty from partial observations collected by surveillance systems.

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The Basic Reproduction Number and Prediction of the Epidemic Size of the Novel Coronavirus (COVID-19) in Shahroud, Iran

SSRN Electronic Journal ◽

10.2139/ssrn.3564420 ◽

2020 ◽

Author(s):

Ahmad Khosravi ◽

Reza Chaman ◽

Marzieh Rohani-Rasaf ◽

Fariba Zare ◽

Shiva Mehravaran ◽

...

Keyword(s):

Basic Reproduction Number ◽

Reproduction Number ◽

The Novel ◽

Epidemic Size ◽

Novel Coronavirus ◽

The Basic Reproduction Number

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Basic Reproduction Number of the 2019 Novel Coronavirus Disease in the Major Endemic Areas of China: A Latent Profile Analysis

Frontiers in Public Health ◽

10.3389/fpubh.2021.575315 ◽

2021 ◽

Vol 9 ◽

Author(s):

Honglv Xu ◽

Yi Zhang ◽

Min Yuan ◽

Liya Ma ◽

Meng Liu ◽

...

Keyword(s):

Basic Reproduction Number ◽

Latent Profile Analysis ◽

Latent Class ◽

Reproduction Number ◽

Profile Analysis ◽

Hunan Province ◽

Initial Value ◽

Endemic Areas ◽

Novel Coronavirus ◽

Latent Profile

Objective: The aim of this study is to analyze the latent class of basic reproduction number (R0) trends of the 2019 novel coronavirus disease (COVID-19) in the major endemic areas of China.Methods: The provinces that reported more than 500 cases of COVID-19 till February 18, 2020 were selected as the major endemic areas. The Verhulst model was used to fit the growth rate of cumulative confirmed cases. The R0 of COVID-19 was calculated using the parameters of severe acute respiratory syndrome (SARS) and COVID-19. The latent class of R0 was analyzed using the latent profile analysis (LPA) model.Results: The median R0 calculated from the SARS and COVID-19 parameters were 1.84–3.18 and 1.74–2.91, respectively. The R0 calculated from the SARS parameters was greater than that calculated from the COVID-19 parameters (Z = −4.782 to −4.623, p < 0.01). Both R0 can be divided into three latent classes. The initial value of R0 in class 1 (Shandong Province, Sichuan Province, and Chongqing Municipality) was relatively low and decreased slowly. The initial value of R0 in class 2 (Anhui Province, Hunan Province, Jiangxi Province, Henan Province, Zhejiang Province, Guangdong Province, and Jiangsu Province) was relatively high and decreased rapidly. Moreover, the initial R0 value of class 3 (Hubei Province) was in the range between that of classes 1 and 2, but the higher R0 level lasted longer and decreased slowly.Conclusion: The results indicated that the overall R0 trend is decreased with the strengthening of comprehensive prevention and control measures of China for COVID-19, however, there are regional differences.

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Epidemiologic characteristics of early cases with 2019 novel coronavirus (2019-nCoV) disease in Korea

Epidemiology and Health ◽

10.4178/epih.e2020007 ◽

2020 ◽

Vol 42 ◽

pp. e2020007 ◽

Cited By ~ 92

Author(s):

Moran Ki

Keyword(s):

First Generation ◽

Second Generation ◽

Additional Data ◽

Reproduction Number ◽

First Case ◽

Novel Coronavirus ◽

Epidemiologic Characteristics ◽

Index Cases ◽

Epidemiological Information ◽

Close Contacts

In about 20 days since the diagnosis of the first case of the 2019 novel coronavirus (2019-nCoV) in Korea on January 20, 2020, 28 cases have been confirmed. Fifteen patients (53.6%) of them were male and median age of was 42 years (range, 20-73). Of the confirmed cases, 16, 9, and 3 were index (57.2%), first-generation (32.1%), and second-generation (10.7%) cases, respectively. All first-generation and second-generation patients were family members or intimate acquaintances of the index cases with close contacts. Fifteen among 16 index patients had entered Korea from January 19 to 24, 2020 while 1 patient had entered Korea on January 31, 2020. The average incubation period was 3.9 days (median, 3.0), and the reproduction number was estimated as 0.48. Three of the confirmed patients were asymptomatic when they were diagnosed. Epidemiological indicators will be revised with the availability of additional data in the future. Sharing epidemiological information among researchers worldwide is essential for efficient preparation and response in tackling this new infectious disease.

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Time Course of COVID-19 Cases in Austria

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17093270 ◽

2020 ◽

Vol 17 (9) ◽

pp. 3270 ◽

Cited By ~ 5

Author(s):

Hanns Moshammer ◽

Michael Poteser ◽

Kathrin Lemmerer ◽

Peter Wallner ◽

Hans-Peter Hutter

Keyword(s):

Public Health ◽

Infectious Disease ◽

Time Course ◽

Reproduction Number ◽

Average Duration ◽

Northern Italy ◽

Reproduction Rate ◽

Health Measures ◽

Major Outbreak ◽

Novel Coronavirus

COVID-19 is an infectious disease caused by a novel coronavirus, which first appeared in China in late 2019, and reached pandemic distribution in early 2020. The first major outbreak in Europe occurred in Northern Italy where it spread to neighboring countries, notably to Austria, where skiing resorts served as a main transmission hub. Soon, the Austrian government introduced strict measures to curb the spread of the virus. Using publicly available data, we assessed the efficiency of the governmental measures. We assumed an average incubation period of one week and an average duration of infectivity of 10 days. One week after the introduction of strict measures, the increase in daily new cases was reversed, and the reproduction number dropped. The crude estimates tended to overestimate the reproduction rate in the early phase. Publicly available data provide a first estimate about the effectiveness of public health measures. However, more data are needed for an unbiased assessment.

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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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