scholarly journals Predicting COVID-19 Dynamics Using SEIR-PADC Model

2020 ◽  
Author(s):  
Ahmad Sedaghat ◽  
Amir Mosavi
Keyword(s):  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Clinical Data ◽  
Optimization Algorithm ◽  
Initial Conditions ◽  
Initial Guess ◽  
Infected Population ◽  
Best Fit ◽  
Mathematics Community

There are a number of derivates of SIR type models developed in mathematics community with 5 to 8 ordinary differential equations to include detailed mechanisms. These models have included exposed, deceased, super-spreader, symptomatic and asymptomatic infected and hospitalized populations; but are mathematically complex and cumbersome. These methods rarely used actual clinical data in details and usually fitted with one or maximum two major clinical data. In this paper, we introduce SEIR-PADC model to include exposed, deceased, super-spreader and critical populations and divide infected population to symptomatic and asymptomatic. SEIR-PADC model is a set of 8 ordinary differential equations with 12 unknown coefficients. Along with, we used an optimization algorithm in MATLAB to find best fit coefficients to 5 set of COVID-19 data in Kuwait. Our focus is to track trends of COVID-19 in coming days. Initial conditions for 8 populations and initial guess values for 12 unknown coefficients are found in a way to best fit COVID-19 data. We used 136 days of COVID-19 data in Kuwait and obtained solutions to cumulative populations rather than daily population. Predictions for 5 different population of COVID-19 in Kuwait using SEIR-PADC model are promising and are discussed here.

scholarly journals COVID-19 (Coronavirus Disease) Outbreak Prediction Using a Susceptible-Exposed-Symptomatic Infected-Recovered-Super Spreaders-Asymptomatic Infected-Deceased-Critical (SEIR-PADC) Dynamic Model

2020 ◽  
Author(s):  
Ahmad Sedaghat ◽  
Amir Mosavi
Keyword(s):  
Infectious Disease ◽  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Clinical Data ◽  
Optimization Algorithm ◽  
Gulf Cooperation Council ◽  
Disease Outbreak ◽  
Gcc Countries ◽  
Number Of Publications ◽  
Insight Into

AbstractExtension of SIR type models has been reported in a number of publications in mathematics community. But little is done on validation of these models to fit adequately with multiple clinical data of an infectious disease. In this paper, we introduce SEIR-PAD model to assess susceptible, exposed, infected, recovered, super-spreader, asymptomatic infected, and deceased populations. SEIR-PAD model consists of 7-set of ordinary differential equations with 8 unknown coefficients which are solved numerically in MATLAB using an optimization algorithm to fit 4-set of COVID-19 clinical data consist of cumulative populations of infected, deceased, recovered, and susceptible. Trends of COVID-19 in Trends in Gulf Cooperation Council (GCC) countries are successfully predicted using available data from outbreak until 23rd June 2020. Promising results of SEIR-PAD model provide insight into better management of COVID-19 pandemic in GCC countries.

scholarly journals Trends of COVID-19 (Coronavirus Disease) in GCC Countries using SEIR-PAD Dynamic Model

2020 ◽  
Author(s):  
Ahmad Sedaghat ◽  
Seyed Amir Abbas Oloomi ◽  
Mahdi Ashtian Malayer ◽  
Amir Mosavi
Keyword(s):  
Infectious Disease ◽  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Dynamic Model ◽  
Clinical Data ◽  
Optimization Algorithm ◽  
Health Organizations ◽  
Policy Makers ◽  
Gcc Countries ◽  
Number Of Publications

AbstractExtension of SIR type models has been reported in a number of publications in mathematics community. But little is done on validation of these models to fit adequately with multiple clinical data of an infectious disease. In this paper, we introduce SEIR-PAD model to assess susceptible, exposed, infected, recovered, super-spreader, asymptomatic infected, and deceased populations. SEIR-PAD model consists of 7-set of ordinary differential equations with 8 unknown coefficients which are solved numerically in MATLAB using an optimization algorithm. Four set of COVID-19 clinical data consist of cumulative populations of infected, deceased, recovered, and susceptible are used from start of the outbreak until 23rd June 2020 to fit with SEIR-PAD model results. Results for trends of COVID-19 in GCC countries indicate that the disease may be terminated after 200 to 300 days from start of the outbreak depends on current measures and policies. SEIR-PAD model provides a robust and strong tool to predict trends of COVID-19 for better management and/or foreseeing effects of certain enforcing laws by governments, health organizations or policy makers.

scholarly journals Trends of COVID-19 (Coronavirus Disease) in GCC Countries using SEIR-PAD Dynamic Model

2020 ◽  
Author(s):  
Ahmad Sedaghat ◽  
Seyed Amir Abbas Oloomi ◽  
Ashtian Malayer ◽  
Amir Mosavi
Keyword(s):  
Infectious Disease ◽  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Dynamic Model ◽  
Clinical Data ◽  
Optimization Algorithm ◽  
Health Organizations ◽  
Policy Makers ◽  
Gcc Countries ◽  
Number Of Publications

Extension of SIR type models has been reported in a number of publications in mathematics community. But little is done on validation of these models to fit adequately with multiple clinical data of an infectious disease. In this paper, we introduce SEIR-PAD model to assess susceptible, exposed, infected, recovered, super-spreader, asymptomatic infected, and deceased populations. SEIR-PAD model consists of 7-set of ordinary differential equations with 8 unknown coefficients which are solved numerically in MATLAB using an optimization algorithm. Four set of COVID-19 clinical data consist of cumulative populations of infected, deceased, recovered, and susceptible are used from start of the outbreak until 23rd June 2020 to fit with SEIR-PAD model results. Results for trends of COVID-19 in GCC countries indicate that the disease may be terminated after 200 to 300 days from start of the outbreak depends on current measures and policies. SEIR-PAD model provides a robust and strong tool to predict trends of COVID-19 for better management and/or foreseeing effects of certain enforcing laws by governments, health organizations or policy makers.

scholarly journals COVID-19 (Coronavirus Disease) Outbreak Prediction Using a Susceptible-Exposed-Symptomatic Infected-Recovered-Super Spreaders-Asymptomatic Infected-Deceased-Critical (SEIR-PADC) Dynamic Model

2020 ◽  
Author(s):  
Ahmad Sedaghat ◽  
Amir Mosavi
Keyword(s):  
Infectious Disease ◽  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Clinical Data ◽  
Optimization Algorithm ◽  
Gulf Cooperation Council ◽  
Disease Outbreak ◽  
Gcc Countries ◽  
Number Of Publications ◽  
Insight Into

Extension of SIR type models has been reported in a number of publications in mathematics community. But little is done on validation of these models to fit adequately with multiple clinical data of an infectious disease. In this paper, we introduce SEIR-PAD model to assess susceptible, exposed, infected, recovered, super-spreader, asymptomatic infected, and deceased populations. SEIR-PAD model consists of 7-set of ordinary differential equations with 8 unknown coefficients which are solved numerically in MATLAB using an optimization algorithm to fit 4-set of COVID-19 clinical data consist of cumulative populations of infected, deceased, recovered, and susceptible. Trends of COVID-19 in Trends in Gulf Cooperation Council (GCC) countries are successfully predicted using available data from outbreak until 23rd June 2020. Promising results of SEIRPAD model provide insight into better management of COVID-19 pandemic in GCC countries.

Plumes Above Line Fires in a Cross-Wind

1994 ◽  
Vol 4 (4) ◽  
pp. 201 ◽  
Author(s):  
GN Mercer ◽  
RO Weber
Keyword(s):  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Initial Conditions ◽  
Velocity Profiles ◽  
Ambient Wind ◽  
Leaf Scorch ◽  
Wind Velocities ◽  
Wind Profiles ◽  
Fire Characteristics ◽  
Temperature Time

A model for the plume above a line fire in a cross wind is constructed. This problem is shown to reduce to numerically solving a system of 6 coupled ordinary differential equations for given initial conditions that depend upon the fire characteristics. The model is valid above the flaming zone and takes inputs such as the width, velocity and temperature of the plume at a given height above the flaming zone, Different horizontal ambient wind velocities are allowed for and a comparison is made between some of these representative wind profiles. The plume trajectory, width, velocity and temperature are calculated for these different representative velocity profiles. This model has application to the calculation of temperature-time exposures of vegetation above line fires and hence can be used in models that predict effects such as leaf scorch and canopy stored seed death. On a larger scale it has application to the problem of tracking burning brands which can cause spotting ahead of the fire.

A Particle Swarm Optimization Algorithm and its Application in Hydrodynamic Equations

2012 ◽  
Vol 510 ◽  
pp. 472-477
Author(s):  
Jian Hui Zhou ◽  
Shu Zhong Zhao ◽  
Li Xi Yue ◽  
Yan Nan Lu ◽  
Xin Yi Si
Keyword(s):  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Differential Equations ◽  
Fluid Mechanics ◽  
Ordinary Differential Equations ◽  
Optimization Algorithm ◽  
Multiple Solutions ◽  
Particle Swarm Optimization Algorithm ◽  
Swarm Optimization ◽  
Estimation Problems

In fluid mechanics, how to solve multiple solutions in ordinary differential equations is always a concerned and difficult problem. A particle swarm optimization algorithm combining with the direct search method (DSPO) is proposed for solving the parameter estimation problems of the multiple solutions in fluid mechanics. This algorithm has improved greatly in precision and the success rate. In this paper, multiple solutions can be found through changing accuracy and search coverage and multi-iterations of computer. Parameter estimation problems of the multiple solutions of ordinary differential equations are calculated, and the result has great accuracy and this method is practical.

Application of Ambarzumian’s Method to Radiant Interchange in a Rectangular Cavity

1974 ◽  
Vol 96 (2) ◽  
pp. 191-196 ◽  
Author(s):  
A. L. Crosbie ◽  
T. R. Sawheny
Keyword(s):  
Differential Equation ◽  
Heat Flux ◽  
Integral Equation ◽  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Nonlinear Integral Equation ◽  
Initial Conditions ◽  
Rectangular Cavity ◽  
Wide Range ◽  
First Time

Ambarzumian’s method had been used for the first time to solve a radiant interchange problem. A rectangular cavity is defined by two semi-infinite parallel gray surfaces which are subject to an exponentially varying heat flux, i.e., q = q0 exp(−mx). Instead of solving the integral equation for the radiosity for each value of m, solutions for all values of m are obtained simultaneously. Using Ambarzumian’s method, the integral equation for the radiosity is first transformed into an integro-differential equation and then into a system of ordinary differential equations. Initial conditions required to solve the differential equations are the H functions which represent the radiosity at the edge of the cavity for various values of m. This H function is shown to satisfy a nonlinear integral equation which is easily solved by iteration. Numerical results for the H function and radiosity distribution within the cavity are presented for a wide range of m values.

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