The implementation of the COVID-19 game on all the 50 states requires a massive amount of memory and time; when performed on CPU, it took 30 hours to perform the game theory and graph algorithm-based combined confinement. Thus, we used Tesla P100-PCIE-16GB GPU available in Google Colab Pro to perform the restricted mobility and make it run fast. We used the high-RAM setting of the google colab to provide the higher memory required by the experiment. The high-RAM setting provides a maximum GPU RAM of 26GB. It took almost two hours to run the experiment for all the 50 states data. We used Neo4j browser to create graph database and apply graph algorithm.
We have considered the population information of all the 50 states and the general mobility of the state. The parameter values are chosen from information of NY times daily infection rates and the average mobility between states. We believe that even a low contact between states may help in transmitting infection, as we can see in real-world, a variant originated in one state may take a few days to spread through whole country. So, it is important to factor the effect of mobility rates between states.
Based on this information, we vary different important factors of the attacker and defender strategies. For instance, if a state has high mobility, it can lead to a higher infection rate, and states which are running out of resources to accommodate medical attention for infected patients has low recovery rate and high mortality rate. Based on the urban and sub-urban areas in that state, we consider the effectiveness or rate of the masking or social distancing strategies. We assume that more populated areas make these strategies less effective and increase the attacker strategies' effectiveness.