Determination and modelling of the formative and statistical time delay in neon

COVID-19 has been impacting on the whole world critically and constantly Since December 2019. We have independently developed a novel statistical time delay dynamic model on the basis of the distribution models from CCDC. Based only on the numbers of confirmed cases in different regions in China, the model can clearly reveal that the containment of the epidemic highly depends on early and effective isolation. We apply the model on the epidemic in Japan and conclude that there could be a rapid outbreak in Japan if no effective quarantine measures are carried out immediately.

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Empirical and semiempirical models of the formative time delay in nitrogen

Canadian Journal of Physics ◽

10.1139/p08-028 ◽

2008 ◽

Vol 86 (7) ◽

pp. 947-951

Author(s):

V Lj Marković ◽

S N Stamenković ◽

S R Gocić

Keyword(s):

Time Delay ◽

Time Dependence ◽

Voltage Drop ◽

Electrical Breakdown ◽

Breakdown Time ◽

Formative Time ◽

Delay Times ◽

Statistical Time ◽

Semiempirical Models ◽

Good Agreement

The formative time dependence on working voltages tf(U) in nitrogen is determined: (1) from the Laue diagrams, by taking the values where the linear approximation of the electrical breakdown time delay (td) intersects the time axis, (2) from histograms, by taking the minimum values of the delay times for the formative time, and (3) from a difference tf = [Formula: see text] – [Formula: see text] ≈ [Formula: see text] – σ (td), where standard deviation σ,(td) is approximately equal to the mean of the statistical time delay [Formula: see text]. The breakdown time delay measurements are supported by oscilloscopic measurements of the voltage drop and the current rise time during inception of the discharge. Several simple models were applied to describe the experimental formative time dependence on working voltages tf,(U) and a good agreement with experimental data was found.PACS Nos.: 51.50.+v, 52.80.–s

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From binomial distribution of electron occurrence to Gauss and Gauss-exponential distribution of the statistical time delay: Analytical transition and simulations

EPL (Europhysics Letters) ◽

10.1209/0295-5075/100/45002 ◽

2012 ◽

Vol 100 (4) ◽

pp. 45002 ◽

Cited By ~ 5

Author(s):

V. Lj. Marković ◽

A. P. Jovanović ◽

S. N. Stamenković ◽

B. Č. Popović

Keyword(s):

Time Delay ◽

Exponential Distribution ◽

Binomial Distribution ◽

Statistical Time

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Contribution of statistical time delay and formative time to total electrical breakdown time delay in argon for different afterglow periods

Journal of Vacuum Science & Technology A Vacuum Surfaces and Films ◽

10.1116/1.2966435 ◽

2008 ◽

Vol 26 (5) ◽

pp. 1326-1330 ◽

Cited By ~ 2

Author(s):

Momcilo M. Pejovic ◽

Milic M. Pejovic

Keyword(s):

Time Delay ◽

Electrical Breakdown ◽

Breakdown Time ◽

Formative Time ◽

Statistical Time

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The reproductive number R0 of COVID-19 based on estimate of a statistical time delay dynamical system

10.1101/2020.02.17.20023747 ◽

2020 ◽

Cited By ~ 14

Author(s):

Nian Shao ◽

Jin Cheng ◽

Wenbin Chen

Keyword(s):

Dynamical System ◽

Growth Rate ◽

Time Delay ◽

Dynamic System ◽

Reproductive Number ◽

Generation Interval ◽

Delay Dynamical System ◽

Simulation Results ◽

Statistical Time

AbstractIn this paper, we estimate the reproductive number R0 of COVID-19 based on Wallinga and Lipsitch framework [11] and a novel statistical time delay dynamic system. We use the observed data reported in CCDC’s paper to estimate distribution of the generation interval of the infection and apply the simulation results from the time delay dynamic system as well as released data from CCDC to fit the growth rate. The conclusion is: Based our Fudan-CCDC model, the growth rate r of COVID-19 is almost in [0.30, 0.32] which is larger than the growth rate 0.1 estimated by CCDC [9], and the reproductive number R0 of COVID-19 is estimated by 3.25 ≤ R0 ≤ 3.4 if we simply use R = 1 + r ∗ Tc with Tc = 7.5, which is bigger than that of SARS. Some evolutions and predictions are listed.

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