Mechanistic Understanding of Pandemic Dynamics: A Multiscale Algorithmic Framework

We present a robust, data-efficient framework for early outbreak assessment using multiscale analysis and Computational Singular Perturbation (CSP). This framework overcomes the shortcomings of the standard compartmental epidemiological models, which often struggle with parameter identifiability during the early stages of a pandemic, limiting their predictive utility considerably when data is sparse. Rather than relying on curve-fitting population profiles, which are sensitive to uncertainty, our approach isolates the dominant "explosive time scale that characterizes the outbreak's intensity and duration. Using a calibrated SEIRD model, CSP allows for the identification of the paths that drive the process during the outbreak phase and the critical transition from accelerating to decelerating growth, which serves as a reliable precursor to the epidemic peak. This framework is assessed against the 4th, 5th, and 6th waves of the COVID-19 pandemic in Greece during 2021, covering periods dominated by the Delta and Omicron variants. Using only early-stage data from short calibration windows, CSP diagnostic tools revealed distinct dynamical drivers for each wave; e.g., a transition from the 4th wave that was driven by transmission intensity (Delta variant dominance) to the 6th wave that was driven by rapid exposure-to-infection turnover and reduced opposition from recovery mechanisms (Omicron variant dominance). Furthermore, it is demonstrated that the timing of the outbreak's weakening can be accurately predicted, demonstrating robustness with results obtained from longer observation windows. These findings position multiscale analysis as a powerful, pathogen-agnostic early-warning system, capable of disentangling complex epidemic mechanisms and assessing intervention efficacy in real-time.