Mechanistic Understanding of Pandemic Dynamics: A Multiscale Algorithmic Framework

Abstract excerpt

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 considerably their predictive utility when data is sparse. Rather than r...

Topics

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• COVID-19 epidemiological studies
• Mathematical and Theoretical Epidemiology and Ecology Models
• Mathematical Biology Tumor Growth

Identifiers and source

Literature Corpus work

8fac7ac4-f5eb-54e7-bb1d-b72f61a3733b

DOI

10.20944/preprints202604.0048.v1