Pandemic Lockdown

Pandemic lockdowns, implemented to curb infectious disease spread, have spurred extensive research into optimizing their effectiveness and mitigating their economic and social consequences. Current research focuses on developing data-driven models, including reinforcement learning and machine learning algorithms (e.g., SARIMA, ensemble methods), to predict disease spread, forecast resource needs (e.g., energy demand, healthcare capacity), and optimize lockdown stringency. These studies highlight the complex interplay between public health interventions, economic stability, and societal behavior, emphasizing the need for adaptive strategies tailored to diverse contexts and populations. The resulting insights are crucial for improving pandemic preparedness and response, informing policy decisions, and mitigating the negative impacts of future outbreaks.