The k-statistics approach to epidemiology
Giorgio Kaniadakis, Mauro M. Baldi, Thomas S. Deisboeck, Giulia, Grisolia, Dionissios T. Hristopulos, Antonio M. Scarfone, Amelia Sparavigna,, Tatsuaki Wada, Umberto Lucia

TL;DR
This paper introduces a $7$-statistics based method to model epidemiological data, successfully fitting historical and recent pandemic data, demonstrating its universal applicability across different diseases and time periods.
Contribution
It applies $7$-statistics to epidemiology, providing a new universal model that fits diverse pandemic data effectively.
Findings
Successfully fitted historical and COVID-19 data with $7$-Weibull distributions.
Demonstrated the model's universal features across different diseases and centuries.
Achieved good agreement between theoretical predictions and empirical observations.
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 -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 -statistics in fitting empirical data. In this paper, we use -statistics to formulate a statistical approach for epidemiological analysis. We validate the theoretical results by fitting the derived -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…
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