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Updated: Aug 6, 2025
09:26
Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples
Published on: June 30, 2023
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Using data mining techniques deep analysis and theoretical investigation of COVID-19 pandemic.
Atheer Y O Allmuttar1,2, Sarmad K D Alkhafaji1
1Department of Computer Sciences, College of Education for Pure Science, University of Thi-Qar, Iraq.
Measurement. Sensors
|March 22, 2023
Summary
K-Means Clustering analysis of COVID-19 in Iraq reveals that containment measures significantly impact disease spread. Increased prevention efforts can reduce patient numbers, while abandoning measures could lead to widespread infection.
Area of Science:
- Medical Informatics
- Epidemiology
- Data Mining
Background:
- Coronavirus Disease (COVID-19) presents treatment challenges due to its complex structure.
- Data mining offers novel approaches for disease examination and evaluation.
- The K-Means clustering algorithm has been adapted for analyzing COVID-19.
Purpose of the Study:
- To analyze the evolution and spread of COVID-19 using K-Means Clustering.
- To model the prevalence of COVID-19 in Iraq.
- To evaluate the impact of containment measures on pandemic progression.
Main Methods:
- Application of the K-Means Clustering algorithm for COVID-19 data analysis.
- Simulation of disease prevalence using a basic K-Means model.
- Observation of outbreak dynamics, including peak and containment strategies.
Main Results:
- Modeling suggests that 50% inhibition of spread could result in 500,000 cases in Iraq by year-end.
- Halving prevention measures could lead to over 1 million cases.
- Abandoning all measures might affect 55% of the population within a month, with numbers decreasing post-September.
Conclusions:
- K-Means Clustering provides valuable insights into COVID-19 dynamics.
- Pandemic-prevention efforts are crucial for controlling the spread of COVID-19.
- The study highlights the significant impact of public health interventions on disease outcomes.
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