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Transition model for coronavirus management
Antoine Djogbenou1, Christian Gourieroux2,3,4, Joann Jasiak1
1Department of Economics York University.
The Canadian Journal of Economics. Revue Canadienne D'Economique
|April 12, 2024
Summary
This study analyzes COVID-19 patient data from Ontario, revealing duration-dependent treatment patterns. The findings help forecast patient counts, aiding public health resource planning during pandemics.
Area of Science:
- Epidemiology
- Biostatistics
Background:
- The early COVID-19 pandemic presented challenges in understanding patient treatment trajectories.
- Accurate data analysis is crucial for managing healthcare resources during public health crises.
Purpose of the Study:
- To analyze patient treatment transitions and sojourn times for COVID-19.
- To develop a model for estimating and forecasting COVID-19 patient counts in Ontario.
- To address data complexities like right truncation and censoring.
Main Methods:
- Utilized individual patient records from Public Health Ontario (PHO) data.
- Employed transition modeling to trace daily patient care intensity changes.
- Examined sojourn times to identify duration dependence in treatments.
Main Results:
- Identified duration dependence in COVID-19 treatment patterns.
- Developed a transition model accounting for data truncation and censoring.
- Provided estimates and forecasts for COVID-19 patient counts in Ontario.
Conclusions:
- The developed model accurately estimates and forecasts COVID-19 patient counts.
- Understanding treatment duration dependence is key for pandemic resource management.
- This research provides valuable insights for public health preparedness.
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