Stochastic optimization for vaccine and testing kit allocation for the COVID-19 pandemic

Area of Science:

• Epidemiology
• Mathematical Modeling
• Decision Science

Background:

• Epidemics necessitate efficient resource allocation for vaccines and tests.
• Uncertainty in disease spread and resource availability complicates distribution strategies.

Purpose of the Study:

• To develop a formal mathematical framework for multi-agent sequential decision-making in epidemic response.
• To create a robust and scalable model for allocating vaccines and tests under uncertainty.

Main Methods:

• Formulated a multi-agent problem involving collaboration between vaccination and learning agents.
• Designed a parameterized direct lookahead approximation for robust decision-making.
• Developed a test allocation policy to capture the value of information.

Main Results:

• The proposed model demonstrates robustness and scalability across various scenarios.
• The test allocation policy outperforms other learning policies in resource-scarce environments.
• Simulations using US states and Nevada nursing homes validate model performance.

Conclusions:

• The mathematical framework provides an effective approach for epidemic resource management.
• The model's ability to handle uncertainty and scarcity is crucial for effective public health interventions.
• The direct lookahead approximation offers a scalable solution for real-world epidemic response.