Predictability of COVID-19 worldwide lethality using permutation-information theory quantifiers

Abstract

This paper examines the predictability of COVID-19 worldwide lethality considering 43 countries. Based on the values inherent to Permutation entropy ( ) and Fisher information measure ( ), we apply the Shannon-Fisher causality plane (SFCP), which allows us to quantify the disorder an evaluate randomness present in the time series of daily death cases related to COVID-19 in each country. We also use Hs and Fs to rank the COVID-19 lethality in these countries based on the complexity hierarchy. Our results suggest that the most proactive countries implemented measures such as facemasks, social distancing, quarantine, massive population testing, and hygienic (sanitary) orientations to limit the impacts of COVID-19, which implied lower entropy (higher predictability) to the COVID-19 lethality. In contrast, the most reactive countries implementing these measures depicted higher entropy (lower predictability) to the COVID-19 lethality. Given this, our findings shed light that these preventive measures are efficient to combat the COVID-19 lethality.

The resilience of cryptocurrency market efficiency to COVID-19 shock

“…This quantity can be understood in three different ways: (i) as an adequate measure for estimating a parameter, (ii) as a qualitative measure associated with the amount of information extracted from a set of data, and (iii) as the measure that reveals the state of disorder of a system or phenomenon. For more details, see (18). The discrete normalized form of the Fisher's information measure (0 ≤ 𝐹 ≤ 1), is:…”

The resilience of cryptocurrency market efficiency to COVID-19 shock

“…This quantity can be understood in three different ways: (i) as an adequate measure for estimating a parameter, (ii) as a qualitative measure associated with the amount of information extracted from a set of data, and (iii) as the measure that reveals the state of disorder of a system or phenomenon. For more details, see [21] . The discrete normalized form of the Fisher’s information measure , is: where and are consecutive probabilities from discrete distribution and is a normalization constant ( if or , and otherwise).…”

“…This paper explores the price disorder and market efficiency considering the daily closing price of five relevant cryptocurrencies (Bitcoin, BNB, Cardano, Ethereum, and XRP), considering three distinct periods, before and during COVID-19. Based on our expertise [19] , [20] , [21] , [22] , [23] , [24] , [25] , we employ the information theory quantifiers (permutation entropy and Fisher information measure) and the sliding window technique. Such a combination of methods allows us to examine the predictability of cryptocurrency prices and market efficiency as a function of time and thereby identify the potential impact of the pandemic on market efficiency in this controversial digital asset class.…”

Multifractal dynamics of COVID-19 vaccines in Europe

“…However, in the last decade, epidemic emergencies such as H1N1 influenza [2], Ebola [3], Zika [4] and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) [5,6,7,8,9], the virus that causes coronavirus disease 2019 (COVID- 19), [10,11,12], have catalyzed the disruption of this paradigm by the scientific community and the vaccine industries.…”