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Related Experiment Video

Updated: Nov 21, 2025

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Estimation of COVID-19 epidemic curves using genetic programming algorithm.

Nikola Anđelić¹, Sandi Baressi Šegota¹, Ivan Lorencin¹

¹University of Rijeka, Faculty of Engineering, Rijeka, Croatia.

Health Informatics Journal

|January 18, 2021

Summary

This summary is machine-generated.

Genetic Programming (GP) models accurately estimate COVID-19 confirmed, deceased, and recovered cases. The developed mathematical models closely predict country-specific and global epidemiology curves using real-world data.

Keywords:

COVID-19 disease spread modeling evolutionary computing genetic programming machine learning

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Area of Science:

Computational Biology
Epidemiology
Machine Learning

Background:

The COVID-19 pandemic necessitated accurate forecasting of disease spread.
Mathematical modeling is crucial for understanding and predicting epidemiological trends.
Genetic Programming (GP) offers a powerful approach for deriving complex mathematical models from data.

Purpose of the Study:

To implement the Genetic Programming (GP) algorithm for modeling COVID-19 cases.
To develop predictive mathematical models for confirmed, deceased, and recovered cases.
To estimate and validate the epidemiology curve for selected countries and globally.

Main Methods:

Application of the Genetic Programming (GP) algorithm.
Utilizing a publicly available COVID-19 dataset.
Developing and evaluating mathematical models based on R-squared scores.

Main Results:

High accuracy achieved: R-squared scores of 0.999 for confirmed and deceased cases.
Excellent performance for recovered cases with an R-squared score of 0.998.
Generated models accurately estimated epidemiology curves, closely matching real-world data.

Conclusions:

Genetic Programming (GP) is a viable method for creating accurate COVID-19 predictive models.
The derived mathematical models effectively forecast epidemiological trends.
The study validates the utility of GP in public health data analysis and prediction.