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Updated: Sep 27, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Source code and secondary data of the stochastic process based COVID-19 simulation model.

S S Manathunga¹, I A Abeyagunawardena¹, S D Dharmaratne^2,3

¹National Hospital of Sri Lanka, Sri Lanka.

Software Impacts

|April 12, 2022

Summary

This summary is machine-generated.

This study developed a COVID-19 spread simulation environment. The tool models disease transmission, incorporating environmental and social factors to test mitigation strategies.

Keywords:

Basic reproduction number COVID-19 Epidemic modelling Novel coronavirus

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Area of Science:

Epidemiology
Computational modeling
Public health

Background:

The COVID-19 pandemic significantly impacted global health and economies.
Understanding disease dynamics is crucial for effective pandemic response.
Existing models may not fully capture the interplay of environmental and social factors.

Purpose of the Study:

To create a versatile simulation environment for COVID-19 spread.
To integrate environmental and social determinants into epidemic modeling.
To provide a tool for evaluating public health interventions.

Main Methods:

Development of a stochastic process-based epidemic simulation model.
Inclusion of a basic reproduction number estimation unit.
Integration of a graphics generator for visualization.
Inputting diverse environmental factors to predict infection spread patterns.

Main Results:

The simulation environment successfully models COVID-19 transmission dynamics.
The model accounts for various environmental and social influences on spread.
It allows for the testing of hypothetical mitigation strategies within a controlled setting.

Conclusions:

The developed simulation environment offers a valuable tool for policymakers and researchers.
It facilitates the assessment of different intervention strategies for pandemic control.
This approach supports evidence-based decision-making in public health emergencies.