Predicting Optimum Lockdown Pattern of Epidemic Spread Using AI Techniques

Abstract

Recent COVID-19 has revealed that we are still unable to find a sustainable approach to combat such infections in order to maintain a stable economy and minimize death rates. COVID-19 vaccine is available but when COVID-19 first arrived, restricting movement was the only option to control the spread, yet, one of its consequences is an economic crisis. This paper proposes predicting optimum lockdown patterns of epidemic spread using AI techniques (MESA ABM) which will help to balance economic fall and death rates while conducting vaccine research. We created a computergenerated environment to replicate a pandemic, such as the initial wave of COVID-19. The model was created using the SIR (Susceptible, Infected, Recovered) hypothesis for disease propagation and implemented using the Mesa, which is an agent-based modeling framework. Using this model, we develop an optimum lockdown pattern using agents (entities that act like humans). Our model produces optimal movement restrictions by randomly putting lockdowns ranging from one to a hundred days using given conditions. Additionally, we carry out several assessments and offer the justification for the action.