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Author Spotlight: Computing the Effects of a Local Radiofrequency Hyperthermia Intervention on Tumor Biomechanics

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Corona COVID-19 spread - a nonlinear modeling and simulation.

Ahmad M Harb¹, Souhib M Harb²

¹German Jordanian University, Jordan.

Computers & Electrical Engineering : an International Journal

|October 20, 2020

Summary

This study models COVID-19 spread using the Susceptible Infected Recovery (SIR) model. Reducing human contact and increasing medication significantly slow virus transmission and decrease deaths.

Keywords:

COVID 19 Corona virus Dynamical simulations Nonlinear dynamics SIR

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Published on: September 27, 2014

Area of Science:

Epidemiology
Mathematical Biology
Public Health

Background:

The emergence of Novel Coronavirus (COVID-19) necessitated understanding its transmission dynamics.
Predictive modeling is crucial for developing effective public health interventions.

Purpose of the Study:

To develop and present a non-linear mathematical model for simulating and predicting COVID-19 spread.
To analyze the impact of key parameters on disease transmission and mortality.

Main Methods:

Utilized the Susceptible Infected Recovery (SIR) mathematical model.
Incorporated key parameters: human contact factor (b), transmission factor (a), medication factor (m), and initial infected count (I0).

Main Results:

Simulation results demonstrate the significant influence of contact, transmission, and medication factors on COVID-19 spread.
A high medication factor and low contact factor were shown to effectively slow down virus transmission.
Increased medication factor correlated with a decrease in COVID-19 related deaths.

Conclusions:

Controlling the human contact factor through measures like social distancing is an effective strategy to mitigate COVID-19 spread.
While medication factor is crucial, its improvement is dependent on national infrastructure.
The model highlights the interplay between behavioral interventions and healthcare capacity in managing pandemics.