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Related Experiment Video

Updated: Oct 13, 2025

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Published on: November 10, 2023

Kalman filter based short term prediction model for COVID-19 spread.

Koushlendra Kumar Singh¹, Suraj Kumar¹, Prachi Dixit²

¹National Institute of Technology, Jamshedpur, India.

Applied Intelligence (Dordrecht, Netherlands)

|November 12, 2021

Summary

This summary is machine-generated.

Machine learning models, including Random Forest and Kalman Filter, analyzed COVID-19 spread factors and forecast future trends. These models identified key demographic and environmental contributors to the pandemic's progression.

Keywords:

COVID19 Kalman filter Pearson correlation Random Forest

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Area of Science:

Epidemiology
Data Science
Computational Biology

Background:

The Corona Virus Disease 2019 (COVID-19) pandemic presents a significant global health challenge.
Understanding the dynamics of SARS-CoV-2 spread, including influencing factors, is crucial for effective public health interventions.

Purpose of the Study:

To analyze the spread of COVID-19 using machine learning techniques.
To identify key demographic and environmental factors contributing to SARS-CoV-2 transmission.
To forecast short-term and long-term spread patterns of the virus.

Main Methods:

Data integration from various sources on COVID-19 spread.
Application of Machine Learning models: Random Forest for factor identification and feature importance analysis.
Utilizing Pearson Correlation matrix for visualizing linear relationships between features.
Employing Kalman Filter for short-term and long-term spread forecasting.

Main Results:

Random Forest model demonstrated strong performance in evaluating COVID-19 spread data.
Key demographic and environmental factors influencing virus transmission were identified and their contributions analyzed.
Pearson Correlation heatmap provided insights into feature relationships.
Kalman Filter showed satisfactory short-term forecasting accuracy but limited long-term predictive power.

Conclusions:

Machine learning techniques, particularly Random Forest and Kalman Filter, are effective tools for analyzing and understanding COVID-19 spread dynamics.
The study successfully identified significant contributing factors to the pandemic's progression.
While Kalman Filter is useful for short-term forecasting, further research is needed for robust long-term predictions.