Identifying second wave and new variants of covid-19 from shannon entropy in global pandemic data

Nieto-Chaupis, H.

In most countries that have been affected by the arrival of Corona Virus Disease 2019 (or Covid-19 in short), the surveillance of daily state of management of pandemic is reflected on the histogram of number of confirmed cases versus time (days or weeks). While at the first phases of pandemic is seen an exponential morphology, the public health operators target to flat the peak, fact that might to reflect the success of the done efforts such as quarantine, curfew and social distancing. In this paper is investigated the morphology of data of new cases in terms of Shannon's entropy. The resulting entropy distributions matches well to the Italian case where presumably the peaks of histogram can be to some extent interpreted as the effect of the presence of two different strains circulating in he country. Therefore, the Shannon's entropy approach can be projected to real data in order to examine the characteristics of pandemic under the assumption that human activity still in pandemic times can trigger subsequent waves. © 2021 IEEE.