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Regressive Class Modelling for Predicting Trajectories of COVID-19 Fatalities Using Statistical and Machine Learning

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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.

Keywords:
Deep learning techniquesJoint modellingModel accuracyRepeated measuresSARS-CoV-2 virus

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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.