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Emerging algebraic growth trends in SARS-CoV-2 pandemic data
Katarína Bod'ová1, Richard Kollár1
1Faculty of Mathematics, Physics and Informatics, Comenius University, Mlynská Dolina, 84248 Bratislava, Slovakia.
Physical Biology
|September 9, 2020
Summary
This study analyzed SARS-CoV-2 data from 119 countries, finding active cases follow algebraic growth. Advanced stages show algebraic decay in the reproduction number, aiding pandemic data prediction.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Infectious Disease Dynamics
Background:
- The SARS-CoV-2 pandemic presented unprecedented challenges in understanding disease spread.
- Accurate modeling of infectious disease trajectories is crucial for public health interventions.
Purpose of the Study:
- To analyze the growth patterns of active SARS-CoV-2 cases globally.
- To develop mathematical models describing epidemic progression.
- To investigate the behavior of the reproduction number in later epidemic stages.
Main Methods:
- Analysis of reported SARS-CoV-2 data (confirmed cases, deaths, recoveries) from 119 countries (January-May 2020).
- Application of algebraic growth and decay models to time series data.
- Formulation of findings within compartment-type mathematical epidemic models.
Main Results:
- Active COVID-19 cases exhibited strong agreement with algebraic growth patterns.
- Later epidemic stages showed a combined algebraic growth with exponential decay.
- The relative reproduction number (R 0) demonstrated universal scaling, interpreted as algebraic decay (T M/t) in advanced stages.
Conclusions:
- The developed models provide a framework for improving predictions of reported pandemic data.
- Findings allow for the estimation of key epidemic parameters.
- The study highlights the utility of mathematical modeling in understanding epidemic dynamics, while acknowledging limitations regarding the true scale of the pandemic.
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