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Related Experiment Video

Updated: Aug 24, 2025

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling
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SimCOVID: Open-Source Simulation Programs for the COVID-19 Outbreak.

Ismael Abdulrahman1

  • 1Department of Information System Engineering, Erbil Technical Engineering College, Erbil Polytechnic University, Erbil, Iraq.

SN Computer Science
|October 24, 2022
PubMed
Summary
This summary is machine-generated.

Open-source simulation programs for COVID-19 (Coronavirus Disease 2019) tracking and estimation are presented. These tools utilize various mathematical models and adaptive systems for efficient, visualized global outbreak analysis and prediction.

Keywords:
COVID-19CoronavirusEpidemiologyMATLABProgramSimulinkVirus spread

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Area of Science:

  • Computational epidemiology
  • Infectious disease modeling
  • Software development for public health

Background:

  • Accurate simulation and tracking of infectious disease outbreaks, such as COVID-19, are crucial for public health response.
  • Existing modeling tools may lack flexibility or visualization capabilities for dynamic global data.

Purpose of the Study:

  • To present open-source computer simulation programs for simulating, tracking, and estimating COVID-19 outbreaks.
  • To provide a flexible and visualized platform for analyzing global epidemic data.

Main Methods:

  • Development of simulation programs in Simulink and MATLAB.
  • Implementation of compartmental models: SIR, SEIR, SEIRD, and SEIRV.
  • Utilizing sigmoid steps for dynamic data tracking and parameter estimation via MATLAB tools.
  • Employing an adaptive neuro-fuzzy inference system for model training and prediction.
  • Developing visualization tools for country-specific COVID-19 data.

Main Results:

  • The developed programs offer an efficient, fast, simple, and visualized method for simulating worldwide outbreaks.
  • Parameter estimation and dynamic tracking capabilities enhance the accuracy of outbreak analysis.
  • The adaptive neuro-fuzzy inference system aids in model prediction.
  • Visualization tools provide multi-dimensional views of country-specific COVID-19 data.

Conclusions:

  • The presented open-source software provides a valuable resource for simulating and analyzing COVID-19 outbreaks.
  • The tools can be utilized for research studies and as an educational instrument in epidemiology.
  • The flexible modeling approach supports the development of new epidemiological models.