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Related Experiment Video

Updated: Oct 6, 2025

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

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Published on: November 10, 2023

Complexity of COVID-19 Dynamics.

Bellie Sivakumar¹, Bhadran Deepthi¹

¹Department of Civil Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India.

Entropy (Basel, Switzerland)

|January 21, 2022

Summary

Chaos theory reveals COVID-19 (Coronavirus disease 2019) dynamics exhibit complexity. The false nearest neighbor method analyzed daily cases and deaths, showing deaths are often more complex than cases globally.

Keywords:

COVID-19 chaos theory coronavirus false nearest neighbor algorithm infectious diseases nonlinear dynamics phase space reconstruction

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Area of Science:

Complex Systems Science
Epidemiology
Infectious Disease Dynamics

Background:

The global spread of infectious diseases, exemplified by the COVID-19 pandemic, necessitates a deeper understanding of disease transmission dynamics.
Existing models for COVID-19 spread often have limitations due to the inherent complexity of real-world epidemiological data.
Chaos theory offers tools to analyze the underlying complexity and predictability of dynamic systems, including disease outbreaks.

Purpose of the Study:

To investigate the temporal dynamic complexity of COVID-19 (Coronavirus disease 2019) spread worldwide using chaos theory principles.
To quantify the dimensionality of COVID-19 case and death data to understand underlying patterns.
To compare the complexity of COVID-19 case dynamics versus death dynamics across multiple countries.

Main Methods:

Application of the false nearest neighbor (FNN) method to estimate the attractor dimension of COVID-19 time series.
Reconstruction of multi-dimensional phase space from single-variable time series of daily COVID-19 cases and deaths.
Analysis of data from 40 countries/regions to assess global patterns in disease dynamics complexity.

Main Results:

COVID-19 case dynamics demonstrated low- to medium-level complexity, with estimated dimensionality between 3 and 7.
COVID-19 death dynamics exhibited a wider range of complexity, from low to high, with dimensionality from 3 to 13.
For most analyzed countries (75%), the complexity of COVID-19 death dynamics was found to be greater than or equal to that of case dynamics.

Conclusions:

The dynamics of COVID-19 cases and deaths display measurable complexity, varying across different geographical regions.
The complexity of death dynamics often exceeds that of case dynamics, suggesting more intricate underlying factors.
Findings have significant implications for developing more accurate epidemiological models and improving infectious disease spread predictions.