Title: A BARMM Case Study: COVID-19 Agent-Based Model with Goal Optimization for Vaccine Distribution
Preventive and control measures such as community quarantine, wearing face masks and social distancing have been widely used to limit the spread of COVID-19. Quarantine is used to lower the number of infectives, helping health facilities cope, but trade-offs with the economy can be observed. Different health and economic policies have various implications in the community. Thus, the idea of emergence through an agent-based model (ABM) is developed to observe the impact of various health policies on the spread of the disease.
Now that SARS-CoV-2 (COVID-19) vaccines are developed, it is very important to plan its distribution strategy. Combined with the ABM, a resource optimization model was proposed in this study to simulate the possible decisions of policymakers and to help them identify appropriate strategies for their constituents. Using the proposed model, it aims that it could simulate possible decisions of policymakers and could help them identify appropriate strategies for their constituents.
In this case study, simulations for different vaccination scenarios for the Bangsamoro region were analyzed.
Sample Implementation of Solution: https://barmm-abm-covid19-vaccination.herokuapp.com
> python main.py
For more details of the model, please see the following preprints:
COVID-19 Agent-Based Model with Multi-objective Optimization for Vaccine Distribution
Modeling the dynamics of COVID-19 using Q-SEIR model with age-stratified infection probability
COVID19 Epidemiological Data - Exploratory Analysis
Vaccine Distribution using Different Prioritization Factors
Slurm Scripts for High-Performance Computing (HPC) cluster
Script for Summarized Sensitivity Analysis
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