COVID-19 lethality in Brazilian States using information theory quantifiers

Abstract

In this paper, we presented an overview diagnosis consider the time series of daily deaths by COVID-19 in the Brazilian States using Bandt & Pompe method (BPM) to estimate the Information Theory quantifiers, more specifically the Permutation entropy (H s ) and the Fisher information measure (F s ). Based on the Information Theory quantifiers, we build up the Shannon-Fisher causality plane (SFCP) to promote insights into the COVID-19 temporal evolution inherent in the phenomenology associated with the number of daily deaths well as their respective locations along the SFCP. Moreover, we apply H s and F s to elaborate on the rank of the Brazilian States’ real situation, considering the number of daily death due to COVID-19 based on the complexity hierarchy. The Brazilian States that are located in the middle region of the two-dimensional plane (H s x F s ), such as Amapá (AP), Roraima (RO), Acre (AC), and Tocantins (TO) are characterized by a less entropic and low disorder, which implies in high predictability of the COVID-19 lethality. While, the Brazilian States that are located in the lower-right region, such as Ceará (CE), Bahia (BA), Pernambuco (PE), and Rio de Janeiro (RJ), are characterized by high entropy and high disorder, which leads to low predictability of the COVID-19 lethality. Given this, our results provide empirical evidence that the permutation entropy is a powerful approach to predicting infectious diseases. Dynamic monitoring of permutation entropy can help policymakers to take more or less restrictive measures to combat COVID-19.

Dynamics diagnosis of the COVID-19 deaths using the Pearson diagram

“…This paper proposes a new paradigm for analyzing the number of COVID-19 deaths based on a multivariate version of the Pearson diagram. The resulting maps complement other previous map works using quantifiers of information theory (permutation entropy and Fisher information, called Shannon-Fisher causality plane ) proposed by Fernandes and Araújo (2020) [11] and, recently, in [12] , [13] , [14] , as our proposal analyzes other statistical aspects of the stochastic process derived from the number of deaths from COVID-19. The analysis of the obtained maps is performed in two steps: First, we determine the risk profile for each country using the k-means cluster analysis method; second, we analyze the dynamics of the clusters formed in the previous step using the time series of the four different periods of the pandemic of COVID-19.…”

Booms in commodities price: assessing disorder and similarity overeconomic cycles

“…Precisely, the permutation entropy quantifies the probability distribution of ordinal patterns considering the temporal causality within the dataset. In this way, we connect the permutation entropy with the symbolic sequences of the time series underlying ( (Sensoy, 2019), (Fernandes et al, 2021a), (Fernandes et al, 2021c), (Dima et al, 2021)).…”

The resilience of cryptocurrency market efficiency to COVID-19 shock

“…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.…”