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Updated: Aug 25, 2025
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Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19
Published on: February 16, 2022
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An intelligent forecast for COVID-19 based on single and multiple features.
Yilei Wang1, Yiting Zhang1, Xiujuan Zhang1
1School of Computer Science Qufu Normal University Rizhao China.
International Journal of Intelligent Systems
|October 17, 2022
Summary
This study visualizes COVID-19 data and uses logistic growth and Susceptible Exposed Infected Removed models to forecast epidemic trends. The models accurately predict the spread of COVID-19, aiding global monitoring efforts.
Area of Science:
- Epidemiology
- Data Visualization
- Mathematical Modeling
Background:
- Urgent need for global COVID-19 development monitoring.
- Visualization is crucial for tracking COVID-19 trends.
- Existing methods lack integrated epidemic data forecasting.
Purpose of the Study:
- To visually analyze real-time COVID-19 data.
- To establish a connection between epidemic data visualization and forecasting.
- To predict COVID-19 development trends using multiple models.
Main Methods:
- Visual analysis of real-time COVID-19 data.
- Development of a logistic growth model for single-feature prediction.
- Implementation of a Susceptible Exposed Infected Removed (SEIR) model for multi-feature prediction.
- Fitting model predictions to actual COVID-19 epidemic data.
Main Results:
- Visualizations effectively monitor COVID-19 trends.
- Logistic growth and SEIR models show consistency with real epidemic data.
- The combined modeling approach demonstrates strong performance in predicting COVID-19 trends.
Conclusions:
- The study successfully integrates visualization and multi-model forecasting for COVID-19.
- The proposed models accurately predict epidemic development trends.
- This approach enhances global monitoring and prediction of infectious diseases like COVID-19.
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