Abstract
A great variety of complex physical, natural and artificial systems are governed by statistical distributions, which often follow a standard exponential function in the bulk, while their tail obeys the Pareto power law. The recently introduced kappa -statistics framework predicts distribution functions with this feature. A growing number of applications in different fields of investigation are beginning to prove the relevance and effectiveness of kappa -statistics in fitting empirical data. In this paper, we use kappa -statistics to formulate a statistical approach for epidemiological analysis. We validate the theoretical results by fitting the derived kappa -Weibull distributions with data from the plague pandemic of 1417 in Florence as well as data from the COVID-19 pandemic in China over the entire cycle that concludes in April 16, 2020. As further validation of the proposed approach we present a more systematic analysis of COVID-19 data from countries such as Germany, Italy, Spain and United Kingdom, obtaining very good agreement between theoretical predictions and empirical observations. For these countries we also study the entire first cycle of the pandemic which extends until the end of July 2020. The fact that both the data of the Florence plague and those of the Covid-19 pandemic are successfully described by the same theoretical model, even though the two events are caused by different diseases and they are separated by more than 600 years, is evidence that the kappa -Weibull model has universal features.
Highlights
A great variety of complex physical, natural and artificial systems are governed by statistical distributions, which often follow a standard exponential function in the bulk, while their tail obeys the Pareto power law
The purpose of this section is the validation of the κ-Weibull statistical model which is described in detail in the “Methods” section
The differences in the Pareto parameters between the different countries are likely to reflect differences in the response strategies, in the compliance of the citizens to restrictive measures that can curb the spread of the virus, as well as in the readiness and quality of the health care system including emergency treatment units. Both the plague data from the pandemic of 1417 in Florence as well as the Covid-19 data of the 2020 pandemic from China, Germany, Italy, Spain and United Kingdom have been analyzed by means of the proposed κ-Weibull model, to obtain information about the spreading dynamics of these deadly disease outbreaks
Summary
A great variety of complex physical, natural and artificial systems are governed by statistical distributions, which often follow a standard exponential function in the bulk, while their tail obeys the Pareto power law. As further validation of the proposed approach we present a more systematic analysis of COVID-19 data from countries such as Germany, Italy, Spain and United Kingdom, obtaining very good agreement between theoretical predictions and empirical observations. For these countries we study the entire first cycle of the pandemic which extends until the end of July 2020. Statistical approaches provide powerful tools for medical and epidemiological applications, since they allow predicting the behaviour of certain diseases[4,5] Such approaches are very useful for informing health policy and decision making, regarding control and mitigation measures in response to the societal impacts of epidemics and pandemics. Consiglio Nazionale delle Ricerche, c/o Dipartimento di Scienza Applicata e Tecnologia, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy. 7Region of Electrical and Electronic Systems Engineering, Scientific Reports | (2020) 10:19949
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