Distribution of Incubation Period of COVID-19 in the Canadian Context: Modeling and Computational Study

Computational Study ◽

Context Modeling ◽

Canadian Context ◽

Incubation Periods ◽

Delay Differential ◽

Novel Model ◽

Abstract We propose a novel model based on a set of coupled delay differential equations with fourteen delays in order to accurately estimate the incubation period of COVID-19, employing publicly available data of confirmed corona cases. In this goal, we separate the total cases into fourteen groups for the corresponding fourteen incubation periods. The estimated mean incubation period we obtain is 6.74 days (95% Confidence Interval(CI): 6.35 to 7.13), and the 90th percentile is 11.64 days (95% CI: 11.22 to 12.17), corresponding to a good agreement with statistical supported studies. This model provides an almost zero-cost approach to estimate the incubation period.

Distribution of Incubation Period of COVID-19 in the Canadian Context: Modeling and Computational Study

10.1101/2020.11.20.20235648 ◽

2020 ◽

Author(s):

Subhendu Paul ◽

Emmanuel Lorin

Keyword(s):

Confidence Interval ◽

Incubation Period ◽

Computational Study ◽

Original Model ◽

Context Modeling ◽

Canadian Context ◽

Incubation Periods ◽

Delay Differential ◽

We propose an original model based on a set of coupled delay differential equations with fourteen delays in order to accurately estimate the incubation period of COVID-19, employing publicly available data of confirmed corona cases. In this goal, we separate the total cases into fourteen groups for the corresponding fourteen incubation periods. The estimated mean incubation period we obtain is 6.74 days (95% Confidence Interval(CI): 6.35 to 7.13), and the 90th percentile is 11.64 days (95% CI: 11.22 to 12.17), corresponding to a good agreement with statistical supported studies. This model provides an almost zero-cost approach to estimate the incubation period.

Distribution of incubation periods of COVID-19 in the Canadian context

Scientific Reports ◽

10.1038/s41598-021-91834-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Subhendu Paul ◽

Emmanuel Lorin

Keyword(s):

Computational Complexity ◽

Confidence Interval ◽

Differential Equations ◽

Incubation Period ◽

Canadian Context ◽

Incubation Periods ◽

Delay Differential ◽

Novel Model ◽

AbstractWe propose a novel model based on a set of coupled delay differential equations with fourteen delays in order to accurately estimate the incubation period of COVID-19, employing publicly available data of confirmed corona cases. In this goal, we separate the total cases into fourteen groups for the corresponding fourteen incubation periods. The estimated mean incubation period we obtain is 6.74 days (95% Confidence Interval(CI): 6.35 to 7.13), and the 90th percentile is 11.64 days (95% CI: 11.22 to 12.17), corresponding to a good agreement with statistical supported studies. This model provides an almost zero-cost computational complexity to estimate the incubation period.

Estimation of COVID-19 recovery and decease periods in Canada using delay model

Scientific Reports ◽

10.1038/s41598-021-02982-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Subhendu Paul ◽

Emmanuel Lorin

Keyword(s):

Large Scale ◽

Recovery Period ◽

Bivariate Distribution ◽

Bivariate Analysis ◽

Delay Model ◽

Death Toll ◽

Incubation Periods ◽

Delay Differential ◽

Novel Model ◽

Similar Domain

AbstractWe derive a novel model escorted by large scale compartments, based on a set of coupled delay differential equations with extensive delays, in order to estimate the incubation, recovery and decease periods of COVID-19, and more generally any infectious disease. This is possible thanks to some optimization algorithms applied to publicly available database of confirmed corona cases, recovered cases and death toll. In this purpose, we separate (1) the total cases into 14 groups corresponding to 14 incubation periods, (2) the recovered cases into 406 groups corresponding to a combination of incubation and recovery periods, and (3) the death toll into 406 groups corresponding to a combination of incubation and decease periods. In this paper, we focus on recovery and decease periods and their correlation with the incubation period. The estimated mean recovery period we obtain is 22.14 days (95% Confidence Interval (CI) 22.00–22.27), and the 90th percentile is 28.91 days (95% CI 28.71–29.13), which is in agreement with statistical supported studies. The bimodal gamma distribution reveals that there are two groups of recovered individuals with a short recovery period, mean 21.02 days (95% CI 20.92–21.12), and a long recovery period, mean 38.88 days (95% CI 38.61–39.15). Our study shows that the characteristic of the decease period and the recovery period are alike. From the bivariate analysis, we observe a high probability domain for recovered individuals with respect to incubation and recovery periods. A similar domain is obtained for deaths analyzing bivariate distribution of incubation and decease periods.

Elementary time-delay dynamics of COVID-19 disease

10.1101/2020.03.27.20045328 ◽

2020 ◽

Cited By ~ 1

Author(s):

José Menéndez

Keyword(s):

Time Delay ◽

Differential Equations ◽

South Korea ◽

Ordinary Differential Equations ◽

Survival Function ◽

Elementary Model ◽

Infected People ◽

Delay Differential ◽

An elementary model of COVID-19 dynamics—based on time-delay differential equations with a step-like survival function—is shown to be in good agreement with data from China and South Korea. The time-delal approach overcomes the major limitation of standard Susceptible-Exposed-Infected-Recovered (SEIR) models based on ordinary differential equations, namely their inability to predict the observed curve of infected individuals as a function of time. The model is also applied to countries where the epidemic is in earlier stages, such as Italy and Spain, to obtain estimates of the total number of cases and peak number of infected people that might be observed.

Design of a Novel Second-Order Prediction Differential Model Solved by Using Adams and Explicit Runge–Kutta Numerical Methods

Mathematical Problems in Engineering ◽

10.1155/2020/9704968 ◽

2020 ◽

Vol 2020 ◽

pp. 1-7 ◽

Cited By ~ 2

Author(s):

Zulqurnain Sabir ◽

Juan L. G. Guirao ◽

Tareq Saeed ◽

Fevzi Erdoğan

Keyword(s):

Numerical Methods ◽

Numerical Solutions ◽

Second Order ◽

The Novel ◽

Differential Model ◽

Runge Kutta ◽

Delay Differential ◽

The Mind ◽

Novel Model

In this study, a novel second-order prediction differential model is designed, and numerical solutions of this novel model are presented using the integrated strength of the Adams and explicit Runge–Kutta schemes. The idea of the present study comes to the mind to see the importance of delay differential equations. For verification of the novel designed model, four different examples of the designed model are numerically solved by applying the Adams and explicit Runge–Kutta schemes. These obtained numerical results have been compared with the exact solutions of each example that indicate the performance and exactness of the designed model. Moreover, the results of the designed model have been presented numerically and graphically.

Association of host, agent and environment characteristics and the duration of incubation and symptomatic periods of norovirus gastroenteritis

Epidemiology and Infection ◽

10.1017/s0950268814003288 ◽

2014 ◽

Vol 143 (11) ◽

pp. 2308-2314 ◽

Cited By ~ 8

Author(s):

T. DEVASIA ◽

B. LOPMAN ◽

J. LEON ◽

A. HANDEL

Keyword(s):

Confidence Interval ◽

Incubation Period ◽

Environmental Characteristics ◽

Norovirus Gastroenteritis ◽

Incubation Periods ◽

Host Pathogen ◽

The Mean ◽

Reported Data

SUMMARYWe analysed the reported duration of incubation and symptomatic periods of norovirus for a dataset of 1022 outbreaks, 64 of which reported data on the average incubation period and 87 on the average symptomatic period. We found the mean and median incubation periods for norovirus to be 32·8 [95% confidence interval (CI) 30·9–34·6] hours and 33·5 (95% CI 32·0–34·0) hours, respectively. For the symptomatic period we found the mean and median to be 44·2 (95% CI 38·9–50·7) hours and 43·0 (95% CI 36·0–48·0) hours, respectively. We further investigated how these average periods were associated with several reported host, agent and environmental characteristics. We did not find any strong, biologically meaningful associations between the duration of incubation or symptomatic periods and the reported host, pathogen and environmental characteristics. Overall, we found that the distributions of incubation and symptomatic periods for norovirus infections are fairly constant and showed little differences with regard to the host, pathogen and environmental characteristics we analysed.

Delay Differential Equations - With Applications in Population Dynamics

10.1016/s0076-5392(08)x6164-8 ◽

1993 ◽

Keyword(s):

Population Dynamics ◽

Differential Equations ◽

Delay Differential

Neutral uncertain delay differential equations

Green Communications and Networks ◽

10.2495/gcn131342 ◽

2014 ◽

Author(s):

Xintong Ge

Keyword(s):

Differential Equations ◽

Delay Differential

Explaining the magnetism of gold

10.26434/chemrxiv.5393944.v1 ◽

2017 ◽

Author(s):

Robson de Farias

Keyword(s):

Gas Phase ◽

Computational Study ◽

Formation Enthalpy ◽

Gold Atom ◽

Orbital Energy ◽

Knudsen Effusion ◽

Energy Diagram ◽

Unpaired Electrons ◽

The Difference ◽

In the present work, a computational study is performed in order to clarify the possible magnetic nature of gold. For such purpose, gas phase Au2 (zero charge) is modelled, in order to calculate its gas phase formation enthalpy. The calculated values were compared with the experimental value obtained by means of Knudsen effusion mass spectrometric studies [5]. Based on the obtained formation enthalpy values for Au2, the compound with two unpaired electrons is the most probable one. The calculated ionization energy of modelled Au2 with two unpaired electrons is 8.94 eV and with zero unpaired electrons, 11.42 eV. The difference (11.42-8.94 = 2.48 eV = 239.29 kJmol-1), is in very good agreement with the experimental value of 226.2 ± 0.5 kJmol-1 to the Au-Au bond7. So, as expected, in the specie with none unpaired electrons, the two 6s1 (one of each gold atom) are paired, forming a chemical bond with bond order 1. On the other hand, in Au2 with two unpaired electrons, the s-d hybridization prevails, because the relativistic contributions. A molecular orbital energy diagram for gas phase Au2 is proposed, explaining its paramagnetism (and, by extension, the paramagnetism of gold clusters and nanoparticles).