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Optimal control-based vaccination and testing strategies for COVID-19
Alberto Olivares1, Ernesto Staffetti1
1Universidad Rey Juan Carlos Camino del Molino 5, 28942 Fuenlabrada, Madrid, Spain.
Optimizing COVID-19 vaccination and testing strategies using mathematical models can reduce infections and save vaccine supplies. Early interventions are crucial for healthcare system resilience, but insufficient testing can increase symptomatic cases.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Public Health
Background:
- Limited availability of effective COVID-19 rapid tests and vaccines necessitates strategic allocation.
- Understanding the impact of vaccination strategies on SARS-CoV-2 transmission under various resource constraints is critical.
Purpose of the Study:
- To compare the effects of different vaccination strategies on SARS-CoV-2 transmission.
- To analyze scenarios with limitations in vaccine supply and rapid test availability.
Main Methods:
- Utilized optimal control problems on a compartmental epidemic model.
- Incorporated daily vaccination and testing rates as control variables.
- Employed a direct transcription technique to solve problems with algebraic constraints.
Main Results:
- Early control measures significantly reduce symptomatic infections, enhancing healthcare system resilience.
- Vaccination strategies can be optimized to conserve vaccine supplies without increasing symptomatic cases.
- Insufficient testing rates can lead to an increase in symptomatic infections.
- Reduced vaccine efficacy significantly increases symptomatic infections.
Conclusions:
- Optimal control of compartmental epidemic models offers a robust method for scheduling vaccination and testing policies.
- This approach aids healthcare systems in effectively controlling SARS-CoV-2 spread.
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