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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.

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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.

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