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Modelling Covid-19 infections in Zambia using data mining techniques.

Josephat Kalezhi¹, Mathews Chibuluma², Christopher Chembe³

¹Department of Computer Engineering, Copperbelt University, Kitwe, Zambia.

Results in Engineering

|March 23, 2022

Summary

This summary is machine-generated.

Data mining models, including J48 decision trees and Naïve Bayes, were used to analyze the Covid-19 pandemic's spread in Zambia. This research provides insights into epidemic dynamics using machine learning techniques.

Keywords:

COVID-19 Coronavirus J48 algorithm Multilayer perceptron Naïve Bayes WEKA

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Area of Science:

Epidemiology
Data Science
Public Health

Background:

The Covid-19 pandemic presents a significant global health challenge.
Understanding disease transmission patterns is crucial for effective public health interventions.
Data mining techniques have a proven track record in analyzing complex datasets.

Purpose of the Study:

To apply data mining methods for analyzing the spread of Covid-19 in Zambia.
To evaluate the effectiveness of various machine learning classifiers in understanding epidemic dynamics.
To compare the performance of different models against simpler approaches and existing literature.

Main Methods:

Utilized the Waikato Environment for Knowledge Analysis (WEKA) machine learning library.
Applied several data mining classifiers, including J48 decision tree, Multilayer Perceptron, and Naïve Bayes.
Compared the predictive accuracy of these advanced classifiers with simpler models.

Main Results:

The study successfully applied J48 decision tree, Multilayer Perceptron, and Naïve Bayes classifiers to Covid-19 data from Zambia.
Comparative analysis was performed against simpler classifiers and previously reported findings.
The results offer insights into the epidemic's spread within the specified region.

Conclusions:

Data mining models, particularly those in WEKA, are effective tools for analyzing epidemic spread.
The findings contribute to a better understanding of Covid-19 transmission dynamics in Zambia.
This research supports the use of machine learning in public health surveillance and response.