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Regressive Class Modelling for Predicting Trajectories of COVID-19 Fatalities Using Statistical and Machine Learning
Rafiqul I Chowdhury1, M Tariqul Hasan2, Gary Sneddon3
1Charlottetown, PEI C1A 4P3 Canada School of Mathematical and Computational Sciences, University of Prince Edward Island.
Bulletin of the Malaysian Mathematical Sciences Society
|April 18, 2022
Summary
This study introduces a new joint modeling approach to understand COVID-19 spread dynamics. It analyzes risk factors impacting new cases and deaths over time, aiding public health policy.
Area of Science:
- Epidemiology
- Biostatistics
- Public Health
Background:
- The COVID-19 pandemic presents significant global health and societal challenges.
- Effective long-term immunity and complete eradication strategies for SARS-CoV-2 remain elusive.
- Understanding the dynamics of new cases and fatalities is crucial for pandemic control.
Purpose of the Study:
- To investigate the impact of various risk factors on COVID-19 new cases and deaths over time.
- To propose a novel marginal-conditional based joint modeling approach for predicting disease trajectories.
- To provide insights for health policy planners to implement effective control measures.
Main Methods:
- Development of a marginal-conditional based joint modeling approach for repeated measures.
- Analysis of the dependence between consecutive new cases and deaths.
- Comparison of the proposed model's predictive accuracy against extended machine learning algorithms.
Main Results:
- The proposed joint modeling approach effectively predicts COVID-19 trajectories.
- The model identifies key risk factors influencing new cases and fatalities.
- Demonstrated predictive accuracy using COVID-19 data from Texas Health and Human Services.
Conclusions:
- The marginal-conditional joint modeling approach offers significant insights into COVID-19 pandemic dynamics.
- This methodology aids in understanding the relationship between risk factors and disease progression.
- The findings support evidence-based decision-making for public health interventions during pandemics.
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