Rare and extreme events: the case of COVID-19 pandemic

Complex systems have characteristics that give rise to the emergence of rare and extreme events. This paper addresses an example of such type of crisis, namely the spread of the new Coronavirus disease 2019 (COVID-19). The study deals with the statistical comparison and visualization of country-based real-data for the period December 31, 2019, up to April 12, 2020, and does not intend to address the medical treatment of the disease. Two distinct approaches are considered, the description of the number of infected people across time by means of heuristic models fitting the real-world data, and the comparison of countries based on hierarchical clustering and multidimensional scaling. The computational and mathematical modeling lead to the emergence of patterns, highlighting similarities and differences between the countries, pointing toward the main characteristics of the complex dynamics.

[1]  Mohsen Jafari,et al.  Stability analysis of a fractional order model for the HIV/AIDS epidemic in a patchy environment , 2019, J. Comput. Appl. Math..

[2]  Qigui Yang,et al.  Complex dynamics in a stochastic internal HIV model , 2011 .

[3]  J. A. Tenreiro Machado,et al.  Complex dynamics of financial indices , 2013 .

[4]  Long Jiang Zhang,et al.  Coronavirus Disease 2019 (COVID-19): A Perspective from China , 2020, Radiology.

[5]  B. Gnedenko,et al.  Limit Distributions for Sums of Independent Random Variables , 1955 .

[6]  John A. Hartigan,et al.  Clustering Algorithms , 1975 .

[7]  Jay A. Farrell "Prediction Is Very Difficult, Especially About the Future" (Niels Bohr) [President's Message] , 2014 .

[8]  Jessica T Davis,et al.  The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak , 2020, Science.

[9]  L. Brammer,et al.  2009 Pandemic influenza A (H1N1) deaths among children--United States, 2009-2010. , 2011, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[10]  Isack E. Kibona,et al.  SIR Model of Spread of Zika Virus Infections: ZIKV Linked to Microcephaly Simulations , 2017 .

[11]  J. T. Tenreiro Machado Relativistic time effects in financial dynamics , 2014 .

[12]  D. Murdoch,et al.  COVID-19: another infectious disease emerging at the animal-human interface. , 2020, The New Zealand medical journal.

[13]  F. e. Calcul des Probabilités , 1889, Nature.

[14]  J. A. Tenreiro Machado,et al.  A review of power laws in real life phenomena , 2012 .

[15]  K. Górska,et al.  Exact and explicit probability densities for one-sided Lévy stable distributions. , 2010, Physical review letters.

[16]  R. Sokal,et al.  THE COMPARISON OF DENDROGRAMS BY OBJECTIVE METHODS , 1962 .

[17]  David A. Coil,et al.  2019 Novel Coronavirus (COVID-19) Outbreak: A Review of the Current Literature and Built Environment (BE) Considerations to Reduce Transmission , 2020 .

[18]  Tai Hing Lam,et al.  Mass masking in the COVID-19 epidemic: people need guidance , 2020, The Lancet.

[19]  Fred L. Bookstein,et al.  Landmark methods for forms without landmarks: morphometrics of group differences in outline shape , 1997, Medical Image Anal..

[20]  J. T. Tenreiro Machado Complex dynamics of financial indices , 2013 .

[21]  M. B. Stegmann,et al.  A Brief Introduction to Statistical Shape Analysis , 2002 .

[22]  K. Chung,et al.  Limit Distributions for Sums of Independent Random Variables , 1955 .

[23]  José António Tenreiro Machado,et al.  Multidimensional Scaling Visualization Using Parametric Similarity Indices , 2015, Entropy.

[24]  Elena Deza,et al.  Encyclopedia of Distances , 2014 .

[25]  N. Draper,et al.  Applied Regression Analysis , 1966 .

[26]  José António Tenreiro Machado,et al.  Multidimensional Scaling Visualization Using Parametric Entropy , 2015, Int. J. Bifurc. Chaos.

[27]  Henrik Jeldtoft Jensen,et al.  Self-Organized Criticality , 1998 .

[28]  José António Tenreiro Machado,et al.  Empirical Laws and Foreseeing the Future of Technological Progress , 2016, Entropy.

[29]  C. Lawson,et al.  Solving least squares problems , 1976, Classics in applied mathematics.

[30]  O. Bjørnstad,et al.  Dynamics of measles epidemics: Estimating scaling of transmission rates using a time series sir model , 2002 .

[31]  John P Nolan Efficient Methods for Stable Distributions , 2003 .

[32]  Weizhong Yang,et al.  COVID-19 control in China during mass population movements at New Year , 2020, The Lancet.

[33]  D. Sornette Dragon-Kings, Black Swans and the Prediction of Crises , 2009 .

[34]  R. Adler,et al.  A practical guide to heavy tails: statistical techniques and applications , 1998 .

[35]  Nasir Saeed,et al.  A Survey on Multidimensional Scaling , 2018, ACM Comput. Surv..

[36]  Charu C. Aggarwal,et al.  On the Surprising Behavior of Distance Metrics in High Dimensional Spaces , 2001, ICDT.

[37]  Pei Yu,et al.  Complex Dynamics in a Unified SIR and HIV Disease Model: A Bifurcation Theory Approach , 2019, J. Nonlinear Sci..

[38]  C. Cheung,et al.  Review of the Clinical Characteristics of Coronavirus Disease 2019 (COVID-19) , 2020, Journal of General Internal Medicine.

[39]  Daihai He,et al.  Early estimation of the case fatality rate of COVID-19 in mainland China: a data-driven analysis. , 2020, Annals of translational medicine.

[40]  J. L. Nolan Stable Distributions. Models for Heavy Tailed Data , 2001 .

[41]  E. Kinani,et al.  On the solution of fractional order SIS epidemic model , 2018, Chaos, Solitons & Fractals.

[42]  Frank J. Fabozzi,et al.  Financial Models with Levy Processes and Volatility Clustering , 2011 .

[43]  P. Levy,et al.  Calcul des Probabilites , 1926, The Mathematical Gazette.

[44]  S. Swaminathan,et al.  Data sharing for novel coronavirus (COVID-19) , 2020, Bulletin of the World Health Organization.

[45]  Peter Nijkamp,et al.  Accessibility of Cities in the Digital Economy , 2004, cond-mat/0412004.

[46]  W. O. Kermack,et al.  A contribution to the mathematical theory of epidemics , 1927 .

[47]  Tang,et al.  Self-Organized Criticality: An Explanation of 1/f Noise , 2011 .

[48]  M. E. J. Newman,et al.  Power laws, Pareto distributions and Zipf's law , 2005 .

[49]  M. P. Cummings PHYLIP (Phylogeny Inference Package) , 2004 .

[50]  D. Sornette,et al.  Robust statistical tests of Dragon-Kings beyond power law distributions , 2012 .

[51]  S. Hix,et al.  Prediction is very difficult, especially if it’s about the future , 2014 .

[52]  António M. Lopes,et al.  Analysis of global terrorism dynamics by means of entropy and state space portrait , 2016 .

[53]  Charles L. Lawson,et al.  Solving least squares problems , 1976, Classics in applied mathematics.

[54]  J. Rocklöv,et al.  The reproductive number of COVID-19 is higher compared to SARS coronavirus , 2020, Journal of travel medicine.

[55]  Henrik Jeldtoft Jensen,et al.  Self-Organized Criticality: Emergent Complex Behavior in Physical and Biological Systems , 1998 .

[56]  Yaqing Fang,et al.  Transmission dynamics of the COVID‐19 outbreak and effectiveness of government interventions: A data‐driven analysis , 2020, Journal of medical virology.

[57]  J. A. Tenreiro Machado,et al.  Relativistic time effects in financial dynamics , 2014 .

[58]  S. Zhang,et al.  Estimation of the reproductive number of novel coronavirus (COVID-19) and the probable outbreak size on the Diamond Princess cruise ship: A data-driven analysis , 2020, International Journal of Infectious Diseases.