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Updated: Dec 25, 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

SARS epidemical forecast research in mathematical model.

Ding Guanghong¹, Liu Chang¹, Gong Jianqiu¹

¹Department of Mechanics and Engineering Science, Shanghai Research Center of Acupuncture and Meridians, Fudan University, 200433 Shanghai, China.

Chinese Science Bulletin = Kexue Tongbao

|March 28, 2020

Summary

This study uses a simplified SIJR model to analyze SARS transmission dynamics. The model accurately forecasts the epidemic

Keywords:

SARS SEIJR model SIJR model basic reproductive number inflexion quarantined rate transmission rate

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Area of Science:

Epidemiology
Mathematical modeling

Background:

The Severe Acute Respiratory Syndrome (SARS) epidemic posed a significant global health threat.
Understanding transmission dynamics and control effectiveness is crucial for managing infectious disease outbreaks.

Purpose of the Study:

To analyze key SARS epidemic parameters using a simplified mathematical model.
To forecast SARS transmission and evaluate control strategies.
To compare epidemic control effectiveness across different regions.

Main Methods:

Adaptation and application of the simplified SIJR (Susceptible-Infected-Infectious-Removed) model.
Parameter estimation using outbreak data from Hong Kong, Singapore, and Canada.
Model-based forecasting through adjustment of parameters like quarantine rates.

Main Results:

The SIJR model accurately estimated transmission rates and basic reproductive numbers for SARS.
Forecasts demonstrated distinct transmission characteristics between controlled and uncontrolled epidemics.
Model results showed good agreement with actual SARS data from Hong Kong, Singapore, and Canada.

Conclusions:

The SIJR model provides a valuable tool for analyzing SARS transmission and control effectiveness.
The model offers quantitative insights for future research on epidemic diseases.
It can generate indices to assess the effectiveness of control measures in different geographical areas.