Priority and age specific vaccination algorithm for the pandemic diseases: a comprehensive parametric prediction...

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health Policy

Background:

• Pandemic diseases pose significant threats, necessitating non-pharmacological interventions until vaccines are available.
• Vaccine production and distribution challenges require optimized, priority-based vaccination policies.

Purpose of the Study:

• To develop a comprehensive parametric model for analyzing and predicting pandemic casualties.
• To integrate pharmacological and non-pharmacological policies into a predictive framework.

Main Methods:

• A priority and age-specific vaccination policy was developed.
• Non-pharmacological policies (curfews, lockdowns, restrictions) were modified and integrated.
• A susceptible, suspicious, infected, hospitalized, intensive care, intubated, recovered, and death (SHIR) sub-model framework was used.
• A recursive least squares algorithm with inequality constraints optimized model parameters.

Main Results:

• A distinct third peak in casualties was observed around 40 days.
• Priority and age-specific vaccination policies led to faster convergence to zero for intensive care, intubated, and death cases.
• Lifting weekend/holiday curfews was estimated to cause more casualties than easing restrictions on individuals with chronic diseases or those over 65.

Conclusions:

• Sophisticated parametric models integrating policy interventions can accurately predict future pandemic casualties.
• The study highlights the effectiveness of tailored vaccination strategies and the impact of specific non-pharmacological policy adjustments.