Abstract
With population explosion and globalization, the spread of infectious diseases has been a major concern. In 2019, a newly identified type of Coronavirus caused an outbreak of respiratory illness, popularly known as COVID-19, and became a pandemic. Although enormous efforts have been made to understand the spread of COVID-19, our knowledge of the COVID-19 dynamics still remains limited. The present study employs the concepts of chaos theory to examine the temporal dynamic complexity of COVID-19 around the world. The false nearest neighbor (FNN) method is applied to determine the dimensionality and, hence, the complexity of the COVID-19 dynamics. The methodology involves: (1) reconstruction of a single-variable COVID-19 time series in a multi-dimensional phase space to represent the underlying dynamics; and (2) identification of “false” neighbors in the reconstructed phase space and estimation of the dimension of the COVID-19 series. For implementation, COVID-19 data from 40 countries/regions around the world are studied. Two types of COVID-19 data are analyzed: (1) daily COVID-19 cases; and (2) daily COVID-19 deaths. The results for the 40 countries/regions indicate that: (1) the dynamics of COVID-19 cases exhibit low- to medium-level complexity, with dimensionality in the range 3 to 7; and (2) the dynamics of COVID-19 deaths exhibit complexity anywhere from low to high, with dimensionality ranging from 3 to 13. The results also suggest that the complexity of the dynamics of COVID-19 deaths is greater than or at least equal to that of the dynamics of COVID-19 cases for most (three-fourths) of the countries/regions. These results have important implications for modeling and predicting the spread of COVID-19 (and other infectious diseases), especially in the identification of the appropriate complexity of models.
Highlights
Infectious diseases are diseases caused by living organisms, such as viruses and bacteria
It may be noted that a comparison of these phase space diagrams for the daily COVID-19 deaths with those for the daily COVID-19 cases seems to indicate that the complexity of the former is greater than that of the latter
Similar to the observations made for the daily COVID-19 cases above (Section 3.2), these low- to medium-level dimension values seem to suggest that the dynamics of the daily COVID-19 deaths in these 12 countries exhibit low- to medium-level complexity
Summary
Infectious diseases are diseases caused by living organisms, such as viruses and bacteria. They can be passed from person to person. Infectious diseases are among the most dangerous health issues faced by humanity, since they can spread from person to person, over a large spatial extent, and over a long period of time. In this regard, population explosion and globalization have and continue to play a key role in the spread of infectious diseases. Infectious diseases cause millions of deaths annually and cost a huge amount of money for prevention and treatment. An adequate understanding of the dynamics of infectious diseases is key to saving our lives and economy
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