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This study introduces a new mathematical model for daily planning of COVID-19 interventions. It optimizes vaccine and non-pharmaceutical strategies to control epidemic spread and hospitalizations.

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Area of Science:

  • Epidemiology
  • Operations Research
  • Mathematical Modeling

Background:

  • COVID-19 necessitates complex non-pharmaceutical strategies alongside vaccination.
  • Limited vaccine availability and the need for second doses complicate intervention planning.

Purpose of the Study:

  • To develop a novel Mixed Integer Nonlinear Programming (MINLP) formulation for fine-grained, daily optimal intervention planning against epidemics like COVID-19.
  • To create an optimal mixed strategy integrating pharmaceutical (vaccines) and non-pharmaceutical interventions.

Main Methods:

  • A modified SIR model incorporating heterogeneous populations, social distancing, and varied vaccine efficacies/delays.
  • An MINLP formulation to optimize intervention timing, considering vaccine batch availability and second-dose requirements.
  • Analysis of a case study for Italy using realistic parameters.

Main Results:

  • The proposed formulation enables effective daily planning of mixed intervention strategies.
  • The model successfully balances epidemic control, hospitalization thresholds, and vaccination logistics.
  • Demonstrated effectiveness in a case study for Italy.

Conclusions:

  • The novel MINLP approach provides a powerful tool for real-time epidemic management.
  • This method supports informed decision-making for public health policies during outbreaks.
  • It offers a framework for optimizing resource allocation and intervention timing.