Adaptive Working Set Model for Epidemiological Analysis

The necessity of modeling the dynamics of infectious disease spread is predicated on the imperative to accurately predict epidemics and assess the efficacy of control measures, such as isolation and quarantine. Conventional compartmental models have been widely used for predicting the course of epidemics, but they have limitations due to their inability to account for dynamic isolation. In this paper, we propose a novel approach based on the concept of a Working Set, which we utilize as a subset of agents actively involved in social contact and potential transmission. Our adapted Working Set model incorporates isolation states for susceptible and infected agents, enabling dynamic adjustment of the transmission rate according to the current size of the working set. The incorporation of a time window parameter enables the identification of current contacts and the identification of super-spreaders, a important component for the optimization of epidemiological measures. Experimental results show that compared to the compartmental models, our model provides a more detailed and realistic tool for analyzing the spread of infection under dynamic control measures. The presented approach integrates resource management principles from computer systems with epidemiological models, providing a flexible and realistic tool for evaluating and optimizing infectious disease control measures.