Real-time estimation of the reproduction number of the novel coronavirus disease (COVID-19) in China in 2020 based on incidence data

AbstractAn outbreak of COVID-19 developed aboard the Princess Cruises Ship during January-February 2020. Using mathematical modeling and time-series incidence data describing the trajectory of the outbreak among passengers and crew members, we characterize how the transmission potential varied over the course of the outbreak. Our estimate of the mean reproduction number in the confined setting reached values as high as ∼11, which is higher than mean estimates reported from community-level transmission dynamics in China and Singapore (approximate range: 1.1-7). Our findings suggest that Rt decreased substantially compared to values during the early phase after the Japanese government implemented an enhanced quarantine control. Most recent estimates of Rt reached values largely below the epidemic threshold, indicating that a secondary outbreak of the novel coronavirus was unlikely to occur aboard the Diamond Princess Ship.

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Early Real-Time Estimation of the Basic Reproduction Number of Emerging Infectious Diseases

Physical Review X ◽

10.1103/physrevx.2.031005 ◽

2012 ◽

Vol 2 (3) ◽

Cited By ~ 5

Author(s):

Bahman Davoudi ◽

Joel C. Miller ◽

Rafael Meza ◽

Lauren Ancel Meyers ◽

David J. D. Earn ◽

...

Keyword(s):

Infectious Diseases ◽

Real Time ◽

Basic Reproduction Number ◽

Reproduction Number ◽

Emerging Infectious Diseases ◽

Time Estimation ◽

Real Time Estimation ◽

The Basic Reproduction Number

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REAL TIME ESTIMATION OF REPRODUCTION NUMBERS BASED ON CASE NOTIFICATIONS-Effective reproduction number of primary pneumonic plague

Tropical Medicine and Health ◽

10.2149/tmh.33.127 ◽

2005 ◽

Vol 33 (3) ◽

pp. 127-132 ◽

Cited By ~ 1

Author(s):

HIROSHI NISHIURA ◽

MASAYUKI KAKEHASHI

Keyword(s):

Real Time ◽

Reproduction Number ◽

Time Estimation ◽

Pneumonic Plague ◽

Reproduction Numbers ◽

Real Time Estimation

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Dynamics of Transmission and Control of COVID-19: A Real-time Estimation Using the Kalman Filter

10.1101/2020.04.19.20071886 ◽

2020 ◽

Cited By ~ 1

Author(s):

Francisco Arroyo Marioli ◽

Francisco Bullano ◽

Carlos Rondón-Moreno

Keyword(s):

Kalman Filter ◽

South Korea ◽

Real Time ◽

Reproduction Number ◽

Time Estimation ◽

Control Measures ◽

Significant Heterogeneity ◽

Real Time Estimation ◽

And Control ◽

Over Time

AbstractThe COVID-19 pandemic has become the center of attention for both researchers and authorities. In this paper, we propose and test a methodology to estimate the daily effective reproduction number (ℛt) through the lens of the Kalman Filter and Bayesian estimation. Moreover, we apply our method to data from the current COVID-19 pandemic in China, Italy, Japan, and South Korea. We correlate our findings with the implementation of control measures in each of these countries. Our results show that China, Italy, and South Korea have been able to reduce ℛt over time. We find significant heterogeneity in the way ℛt decreases across countries. For instance, China reduced ℛt from its peak to below one in 19 days, while South Korea achieved the same reduction in 12 days. In contrast, it has taken Italy almost a month to reach similar levels. We hypothesize this is related to how strict, enforceable, and comprehensive are the implemented policies.

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EpiLPS: a fast and flexible Bayesian tool for near real-time estimation of the time-varying reproduction number

10.1101/2021.12.02.21267189 ◽

2021 ◽

Author(s):

Oswaldo Gressani ◽

Jacco Wallinga ◽

Christian Althaus ◽

Niel Hens ◽

Christel Faes

Keyword(s):

Real Time ◽

Negative Binomial ◽

Reproduction Number ◽

Time Estimation ◽

Renewal Equation ◽

Closed Form Expression ◽

Time Varying ◽

Epidemic Curve ◽

Influenza A H1n1 ◽

Real Time Estimation

AbstractIn infectious disease epidemiology, the instantaneous reproduction number R(t) is a timevarying metric defined as the average number of secondary infections generated by individuals who are infectious at time t. It is therefore a crucial epidemiological parameter that assists public health decision makers in the management of an epidemic. We present a new Bayesian tool for robust estimation of the time-varying reproduction number. The proposed methodology smooths the epidemic curve and allows to obtain (approximate) point estimates and credible envelopes of R(t) by employing the renewal equation, using Bayesian P-splines coupled with Laplace approximations of the conditional posterior of the spline vector. Two alternative approaches for inference are presented: (1) an approach based on a maximum a posteriori argument for the model hyperparameters, delivering estimates of R(t) in only a few seconds; and (2) an approach based on a MCMC scheme with underlying Langevin dynamics for efficient sampling of the posterior target distribution. Case counts per unit of time are assumed to follow a Negative Binomial distribution to account for potential excess variability in the data that would not be captured by a classic Poisson model. Furthermore, after smoothing the epidemic curve, a “plug-in” estimate of the reproduction number can be obtained from the renewal equation yielding a closed form expression of R(t) as a function of the spline parameters. The approach is extremely fast and free of arbitrary smoothing assumptions. EpiLPS is applied on data of SARS-CoV-1 in Hong-Kong (2003), influenza A H1N1 (2009) in the USA and current SARS-CoV-2 pandemic (2020-2021) for Belgium, Portugal, Denmark and France.Author summaryThe instantaneous reproduction number R(t) is a key metric that provides important insights into an epidemic outbreak. We present a flexible Bayesian approach called EpiLPS (Epidemiological modeling with Laplacian-P-splines) for smooth estimation of the epidemic curve and R(t). Computational speed and absence of arbitrary assumptions on smoothing makes EpiLPS an interesting tool for near real-time estimation of the reproduction number. An R software package is available (https://github.com/oswaldogressani).

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A machine for real-time estimation of the tribological characteristics of materials

Industrial laboratory Diagnostics of materials ◽

10.26896/1028-6861-2020-86-4-61-65 ◽

2020 ◽

Vol 86 (4) ◽

pp. 61-65

Author(s):

M. V. Abramchuk ◽

R. V. Pechenko ◽

K. A. Nuzhdin ◽

V. M. Musalimov

Keyword(s):

Real Time ◽

Time Estimation ◽

Tribological Characteristics ◽

Continuous Processing ◽

Friction Machine ◽

Dynamic Friction Coefficient ◽

Displacement Sensors ◽

Frictional Interaction ◽

Real Time Estimation ◽

Automated Quality Control

A reciprocating friction machine Tribal-T intended for automated quality control of the rubbing surfaces of tribopairs is described. The distinctive feature of the machine consists in implementation of the forced relative motion due to the frictional interaction of the rubbing surfaces fixed on the drive and conjugate platforms. Continuous processing of the signals from displacement sensors is carried out under conditions of continuous recording of mutual displacements of loaded tribopairs using classical approaches of the theory of automatic control to identify the tribological characteristics. The machine provides consistent visual real time monitoring of the parameters. The MATLAB based computer technologies are actively used in data processing. The calculated tribological characteristics of materials, i.e., the dynamic friction coefficient, damping coefficient and measure of the surface roughness, are presented. The tests revealed that a Tribal-T reciprocating friction machine is effective for real-time study of the aforementioned tribological characteristics of materials and can be used for monitoring of the condition of tribo-nodes of machines and mechanisms.

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