The unpredictable behavior of SARS-CoV-2 infection

In the year 2019, the first case of severe acute respiratory syndrome coronavirus 2 was discovered in China which led to the coronavirus pandemic of 2019 (COVID-19). COVID-19 caused a significant loss in human life between 2020 and 2021 despite the strict measures to stop the spread of the SARS-2 virus. It is therefore crucial to know the nature of the virus to devise effective policies.

A new study, recently published in the IEEE Access is aimed at understanding how COVID-19 is spread and assess evidence to show that it exhibits a chaotic pattern across the globe as well as in the US.

Chaotic Systems

While many aspects of the pandemic have been examined (e.g. mechanisms and transmission rate) however, it is difficult to assess their evolution. Examining the course of transmission from China across the world, scientists believe the dynamics must be complex that is characterized by chaotic behavior. is the main factor.

In chaotic behavior, elements of the system are aligned and compete to survive. Chaos systems are unpredictable owing to their sensitivity to initial conditions, although they can be characterized by a handful of variables and equations.

A New Study

Researchers looked at data from a total of 214 countries and territories across the world. The data were collected from Johns Hopkins University (JHU) Center for Systems Science and Engineering (CSSE).

The compartmental model of the research examined the dynamics of three classes of infected and susceptible populations. The model could be utilized to calculate fractional-order alternatives which could improve its accuracy and flexibility.

To study the intricate dynamics of this system, different computational tools were used, including Lyapunov exponents, Lyapunov spectrums and bifurcation charts. Calculating the divergence rate of trajectories in the phase space is a way to evaluate chaotic behavior. positive LE can indicate chaos while negative LE is typically an indicator of stability.

The main goal was to determine if the COVID-19 epidemic exhibits a chaotic pattern of behavior. Time-series data on daily infections reported to the COVID-19 outbreak was used to analyze the data. We first looked into the spread of infection in the different US states. Then, we looked at the behavior of the virus in other countries.

In the study, a test of 0-1 was used , which is not computationally intensive and does not require the reconstruction of the phase space of the system being studied. When the test results are close to 1 chaos is present however chaos is absent when they are closer to 0.

K-Median Values derived from the 0-1 Test for Confirmed Daily COVID-19 Cases in the US.

Principal Findings

In the context of the United States, the K-median values of the 0-1 test were used to determine if the time-series infections data showed chaotic deterministic patterns (K-median values of > 0.9 were classified as chaotic). The results showed that the spread of COVID-19 infection in 19 out of 50 states was chaotic (39.2 percent). Scientists then studied the global transmission after discovering the chaotic spread of SARS-2 in the USA.

The image alt=”Examples of daily COVID-19 cases for states that exhibit chaotic behavior.” height=”483″ src=”” srcset=” 820w, 750w, 550w, 450w” title=”Examples of confirmed daily COVID-19 cases for states showing chaotic behavior.” width=”820″/>

Examples that confirm daily COVID-19 cases in states with chaotic behavior.

Overall, 118 countries (out of 213 countries/territories) exhibited chaotic behavior of the spread of COVID-19 infections, i.e., roughly 55%.

All countries and territories have been categorized under three broad headings which include developed economies, emerging economies and emerging economies, using various econometric measurements. The Department of Economic and Social Affairs of the United Nations Secretariat (UN/DESA) is responsible for this classification.

Examples confirming daily COVID-19 cases in states that show non-chaotic behavior

68.3 percent (110 out of 161) of developing countries or territories reported chaotic time series of daily confirmed COVID-19 cases. The same proportion was 13.9 percent (5 out 36) and 18.8% (3 out of 16) for the developed and transition countries, respectively. In Europe, 11 out of 32 (34.4%) countries showed a chaotic spread. The numbers were alarming, with 86 percent for sub-Sharan Africa and 70 percent for Latin America and the Caribbean. Asia was home to 46.4% (13 of 28) countries that had chaotic transmission.

In the present study only COVID-19 cases that were confirmed daily were subjected to nonlinear dynamic analysis. One of the limitations of the study was that the information on the number of hospitalizations, the daily count of deaths, and a number of recoveries were not taken into consideration.

Other variables like income education and employment, the percentage of people living below the poverty line, the total population, government regulations, etc. can also assist us better understand COVID-19 transmission dynamics.

Scientists stated that future research should examine the most well-known Lyapunov exponents of every time-series data set for daily infections to confirm current results.


Researchers looked at time-series data, which represented the spread of confirmed COVID-19 infections between 1/22/2020 and 1/13/2020 and looked for signs of disorder.

A majority of countries (irrespective of continent) were observed to exhibit an unpredictable behavior, making it difficult to determine the course of the pandemic in the longer term.

Other information on government and sociopolitical regulations as and public health policies are required to overcome some of the shortcomings in this study.

Journal reference:
  • N. Sapkota et al. (2021) The Chaotic Behavior of the spread of Infection During the COVID-19 Pandemic in the United States and Globally. IEEE. 9. pp. 80692-80702, 2021, doi: 10.1109/ACCESS.2021.3085240,

Content Source:

Gemma Wilson

Gemma is a journalism graduate with keen interest in covering business news – specifically startups. She has as a keen eye for technologies and has predicted quite a few successful startups over the last couple of years.

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