UNIVERSAL EPIDEMIC SCALING: INFLUENZA AND COVID-19
- 1Alexianer Krankenhaus Hedwigshoehe
Abstract
Conventional compartmental models may become difficult to parameterize over multi-wave epidemic horizons when susceptibility and transmission conditions change between successive epidemic regimes. This study introduces a reduced macroscopic framework that changes the scale of epidemic description from individual-level transmission structure to effective population-level dynamics. Epidemic waves are formulated as transport processes governed by deterministic boundaries, mass balance, and a small set of effective macroscopic parameters. The susceptible population is represented by a dynamic Effective Susceptible Pool that can be re-initialized at transitions between biologically distinct epidemic regimes, while transmission resistance and external control measures are incorporated at the macroscopic level. The resulting equations admit a dimensionless similarity representation and closed-form analytical solutions, enabling analytical estimation of epidemic trajectories and peak healthcare demand without computationally intensive numerical simulation. The framework is evaluated using comparative time-series data for SARS-CoV-2 and seasonal influenza A within the geographically and demographically consistent setting of Rhode Island. Despite their different biological and immunological regimes, the analyzed trajectories exhibit a common reduced asymptotic scaling form. The results support the use of a macroscopic, scale-reduced representation for cross-calibration of heterogeneous surveillance signals and analytical assessment of healthcare-system demand. Further validation across pathogens, populations, and open-system settings is required.
HighlightsO_LIEpidemic waves are modeled analytically as macroscopic transport processes in non-linear media. C_LIO_LIThe vulnerable population is redefined as a dynamic, re-initialized effective susceptible pool. C_LIO_LIMass immunization acts as a synchronized control operator suppressing the epidemic peak. C_LIO_LIA strict mass-balance deconvolution filter removes post-symptomatic shedding noise. C_LIO_LIPeak hospital capacity thresholds are predicted using an analytically tractable closed-form method. C_LI
Related
- The Unprecedented Influenza Epidemic of 2024/2025: Overshadowing the COVID‐19 Winter Wave and Exploring Viral Interference
- Influenza Epidemic Thresholds for Turkey Using the Moving Epidemic Methods: A Ten-Season Analysis
- Utility of Rt epidemic trend categories for influenza surveillance: alignment with observed emergency department trends across two influenza seasons in the United States
- Real-time situational assessment of respiratory virus epidemics in Australia over winter 2025 and an out-of-season influenza A H3N2 epidemic
- Genome‐Wide Association Studies of Hospitalized Influenza Identify Four Risk Loci and Reveal Shared and Unique Genetic Factors Compared With COVID‐19
BibTeX
@misc{below2026universal,
title = {{UNIVERSAL EPIDEMIC SCALING: INFLUENZA AND COVID-19}},
author = {D. Below and F. Mairanowski},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.16.26363217},
url = {https://www.medrxiv.org/content/10.64898/2026.09.16.26363217v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - UNIVERSAL EPIDEMIC SCALING: INFLUENZA AND COVID-19
AU - D. Below
AU - F. Mairanowski
PY - 2026
DA - 2026/09/16
PB - medRxiv
DO - 10.64898/2026.09.16.26363217
UR - https://www.medrxiv.org/content/10.64898/2026.09.16.26363217v1
AB - Conventional compartmental models may become difficult to parameterize over multi-wave epidemic horizons when susceptibility and transmission conditions change between successive epidemic regimes. This study introduces a reduced macroscopic framework that changes the scale of epidemic description from individual-level transmission structure to effective population-level dynamics. Epidemic waves are formulated as transport processes governed by deterministic boundaries, mass balance, and a small set of effective macroscopic parameters. The susceptible population is represented by a dynamic Effective Susceptible Pool that can be re-initialized at transitions between biologically distinct epidemic regimes, while transmission resistance and external control measures are incorporated at the macroscopic level. The resulting equations admit a dimensionless similarity representation and closed-form analytical solutions, enabling analytical estimation of epidemic trajectories and peak healthcare demand without computationally intensive numerical simulation. The framework is evaluated using comparative time-series data for SARS-CoV-2 and seasonal influenza A within the geographically and demographically consistent setting of Rhode Island. Despite their different biological and immunological regimes, the analyzed trajectories exhibit a common reduced asymptotic scaling form. The results support the use of a macroscopic, scale-reduced representation for cross-calibration of heterogeneous surveillance signals and analytical assessment of healthcare-system demand. Further validation across pathogens, populations, and open-system settings is required. HighlightsO_LIEpidemic waves are modeled analytically as macroscopic transport processes in non-linear media. C_LIO_LIThe vulnerable population is redefined as a dynamic, re-initialized effective susceptible pool. C_LIO_LIMass immunization acts as a synchronized control operator suppressing the epidemic peak. C_LIO_LIA strict mass-balance deconvolution filter removes post-symptomatic shedding noise. C_LIO_LIPeak hospital capacity thresholds are predicted using an analytically tractable closed-form method. C_LI
ER -