Abstract
This paper presents a new epidemiological modeling approach that adapts the working set (WS) concept from computer memory management to the dynamics of infectious diseases. Traditional compartmental models provide valuable insights but are limited in their ability to capture dynamic isolation and heterogeneous contact patterns. In contrast, the WS model conceptualizes a time-varying subset of agents actively participating in social interactions, allowing for dynamic adjustments to the rate of infection and the explicit identification of superspreaders. By incorporating isolation states for both susceptible and infected individuals, the model more realistically captures quarantine and targeted interventions. Including an incubation period reduces epidemic peaks by nearly 40% and delays them by more than three weeks, providing critical time for public health response. Within the WS model, moderate isolation reduces peak infection rates by more than three times compared to uncontrolled scenarios, while high isolation almost completely prevents large-scale spread. These results highlight the model's ability to estimate the intensity and timing of interventions with greater accuracy than traditional models. By integrating the time window parameter and computer resource management principles, the adapted WS model represents a robust and adaptable tool for analyzing epidemic dynamics. The results highlight its potential for advancing epidemic modeling and supporting real-time public health decision-making.