Trends in Biomathematics: Stability and Oscillations in Environmental, Social, and Biological Models: Selected Works from the BIOMAT Consortium Lectures, Rio de Janeiro, Brazil, 2021

This contributed volume convenes selected, peer-reviewed works presented at the BIOMAT 2021 International Symposium, which was virtually held on November 1-5, 2021, with its organization staff based in Rio de Janeiro, Brazil. In this volume the reader will find applications of mathematical modeling on health, ecology, and social interactions, addressing topics like probability distributions of mutations in different cancer cell types;oscillations in biological systems;modeling of marine ecosystems;mathematical modeling of organs and tissues at the cellular level;as well as studies on novel challenges related to COVID-19, including the mathematical analysis of a pandemic model targeting effective vaccination strategy and the modeling of the role of media coverage on mitigating the spread of infectious diseases. Held every year since 2001, the BIOMAT International Symposium gathers together, in a single conference, researchers from Mathematics, Physics, Biology, and affine fields to promote the interdisciplinary exchange of results, ideas and techniques, promoting truly international cooperation for problem discussion. BIOMAT volumes published from 2017 to 2020 are also available by Springer. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Oral Microbiota in Children Who Started Antiretroviral Therapy at Young Ages

Background: Infancy is an important developmental period when the human microbiome is shaped. Given links between young age at antiretroviral treatment (ART) initiation and smaller persisting viral reservoirs, we hypothesized that earlier ART initiation may leave distinct microbial signatures in the oral cavity detectable in children living with HIV (CLWH). Method(s): Oral swab samples were collected from 477 CLWH and 123 children without HIV at two sites in Johannesburg, South Africa. CLWH had started ART < 2 years of age with 60% starting < 6 months of age. Most were wellcontrolled on ART at a median of 10 years of age when the swab was collected. Controls were age-matched and recruited from the same communities. Sequencing of the V4 amplicon of the 16S rRNA gene was done using established protocols. DADA2, decontam, and phyloseq were used for sequence inference, contaminant removal, and subsequent analyses. All p-values were adjusted for multiple testing using Benjamini-Hochberg false discovery rate method. Statistical analyses were performed with R. Result(s): CLWH had lower alpha diversity than uninfected children (Shannon index p< 0.0001). Genus-level abundances of Granulicatella, Streptococcus and Gemella were greater and Neisseria and Haemophilus were less abundant among CLWH compared to uninfected children. Associations were strongest among boys. There was no evidence of attenuation of associations with earlier ART initiation. In fact, decreased bacterial diversity and differences in taxa abundances in CLWH versus controls were consistent regardless of whether ART was started before or after 6 months of age. Shifts in genus-level taxa abundances relative to uninfected controls were most marked in children on regimens containing lopinavir/ritonavir;with few shifts seen if on regimens containing efavirenz. Conclusion(s): A distinct profile of less diverse oral bacterial taxa was observed in school-age CLWH on ART versus uninfected age-matched children suggesting persisting interference of HIV and its treatments on microbiota in the mouth. Any effects of earlier ART initiation were not detectable at this age. Studies of treated adults with HIV have observed similar shifts in taxa abundances. Oral microbiota have been linked to salivary cytokine levels with associations between Granulicatella and IL-8 and Neisseria and IL-6. Declines in Neisseria abundances in oral samples have been associated with more severe outcomes in influenza and COVID-19.

Marine Resource Recovery Following the COVID-19 Event in Southern Thailand

This study investigated coral and reef fish recovery following the COVID-19 event between low and high environmental disturbance reefs at Racha Yai Island, Southern Thailand. Three and four 50-m permanent line transects were set at low and high environmental reefs to collect the percent of live coral cover, fish diversity and abundance, and fish trophic-functional groups based on diet and habitat use. Our results showed a significant rise in the percentage of live coral cover, the number of individual fish, the number of fish species, and species richness at both bays following the COVID-19 lockdown due to a crucial reduction in human activities on the reef. In addition, there were increases in the number of corallivore fishes belonging to Chaetodontidae and Pomacentridae families and a reduction of omnivorous fish at the fish-feeding tourist attraction reefs due during the COVID-19 lockdown due to reducing fish-feeding tourism. This indicated that restricted human activities and reduced anthropogenic stress on a coral reef may have substantial short-term impacts on reef fish diversity. Our insights could help designate guidelines to manage tourist impacts on coral reefs and aid in their prolonged persistence. © 2022 by the authors. Licensee C.E.J, Tehran, Iran.

The effect of the COVID-19 epidemic on Moroccan sectoral indices: The entropy approach

The current study investigates the impact of the Coronavirus 2019 (COVID-19) pandemic on the volatility of Moroccan stock market sectoral indices. Shannon entropy with multiple estimators and Rényi entropy for different scales were calculated from February 1, 2019 to May 1, 2022, to measure volatility in the Banking, Oil and Gas, Construction and Building Materials, Beverage, Food Producers and Processors, Distributors, and Mining sector's indices. In this regard, this study uses three periods to quantify the uncertainty in Moroccan sectoral indices before, during, and after the first year of the COVID-19 pandemic in Morocco. The empirical results from Shannon and Rényi entropies indicated higher volatility during the COVID-19 pandemic for all sectoral indices except Oil and Gas. However, the consumer staples sectors have shown a form of resilience compared to other sectors. Indeed, the impact of COVID-19 on the consumer staples sectoral indices' volatilities was negligible compared to other sectors. In addition, investing in a portfolio composed of Mining or Construction and Building Materials stocks was risky due to the increased volatility before and during the epidemic. However, after the COVID-19 pandemic, the entropy level corresponding to all sectors has rearranged except the Beverage sector, which kept the lowest entropy during the three periods. Thus, it seems that the Beverage sector was a safe investment for the three periods. The findings are crucial for governments, businesses, private and public authorities, and investors to create recovery action plans for sensitive sectors and give investors trust to make smarter investment decisions. © The author(s) 2022. This publication is an open access article.

A PROMETHEE based outranking approach for the construction of Fangcang shelter hospital using spherical fuzzy sets.

This study mainly aims to develop two effective and practical multi-criteria group decision-making approaches by taking advantage of the ground-breaking theory of PROMETHEE family of outranking methods. The presented variants of Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method are acknowledged to address the complex decision-making problems carrying the ambiguous information, expressible in terms of yes, no, abstinence and refusal, owing to the preeminent condition and wider structure of spherical fuzzy sets. Both of the proposed approaches seek help from the Shannon's entropy formula to evaluate the object weights of the decision criteria. The proposed techniques operate by taking into account the deviation between each pair of potential alternatives in accordance to different types of preference functions to determine the preference indices. The proposed technique of spherical fuzzy PROMETHEE I method carefully compares the positive and negative outranking flows of the alternative to get partial rankings. In contrast, the spherical fuzzy PROMETHEE II method has the edge to eliminate the incomparable pair by employing the net outranking flow to derive the final ranking. The application of proposed approaches is explained via a case study in the field of medical concerning the selection of appropriate site to establish Fangcang shelter hospital in Wuhan to treat COVID-19 patients. The convincing comparisons of the proposed methodologies with q-rung orthopair fuzzy PROMETHEE and spherical fuzzy TOPSIS methods are also included to verify the aptitude of the proposed methodology.

Using Shannon Entropy to Improve the Identification of MP-SBM Models with Undesirable Output.

In the context of the COVID-19 global epidemic, it is particularly important to use limited medical resources to improve the systemic control of infectious diseases. There is a situation where a shortage of medical resources and an uneven distribution of resources in China exist. Therefore, it is important to have an accurate understanding of the current status of the healthcare system in China and to improve the efficiency of their infectious disease control methods. In this study, the MP-SBM-Shannon entropy model (modified panel slacks-based measure Shannon entropy model) was proposed and applied to measure the disposal efficiency of the medical institutions responding to public health emergencies (disposal efficiency) in China from 2012 to 2018. First, a P-SBM (panel slacks-based measure) model, with undesirable outputs based on panel data, is given in this paper. This model measures the efficiency of all DMUs based on the same technical frontier and can be used for the dynamic efficiency analysis of panel data. Then, the MP-SBM model is applied to solve the specific efficiency paradox of the P-SBM model caused by the objective data structure. Finally, based on the MP-SBM model, undesirable outputs are considered in the original efficiency matrix alignment combination for the deficiencies of the existing Shannon entropy-DEA model. The comparative analysis shows that the MP-SBM-Shannon model not only solves the problem of the efficiency paradox of the P-SBM model but also improves the MP-SBM model identification ability and provides a complete ranking with certain advantages. The results of the study show that the disposal efficiency of the medical institutions responding to public health emergencies in China shows an upward trend, but the average combined efficiency is less than 0.47. Therefore, there is still much room for improvement in the efficiency of infectious disease prevention and control in China. It is found that the staffing problem within the Center for Disease Control and the health supervision office are two stumbling blocks.

An uncertainty measure based on Pearson correlation as well as a multiscale generalized Shannon-based entropy with financial market applications

In this research, we intended to employ the Pearson correlation and a multiscale generalized Shannon-based entropy to trace the transition and type of inherent mutual information as well as correlation structures simultaneously. An optimal value for scale is found to prevent over smoothing, which leads to the removal of useful information. The lowest Singular Value Decomposition Multiscale Generalized Cumulative Residual Entropy (SVDMWGCRE), or SVD Entropy (SVDE), is obtained for periodic–chaotic series, generated by logistic map;hence, the different dynamic, correlation structures, and intrinsic mutual information have been characterized correctly. It is found out that the mutual information between emerging markets entails higher sensitivity, and moreover emerging markets have demonstrated the highest uncertainty among investigated markets. Additionally, the fractional order has synergistic effects on the enhancement of sensitivity with the multiscale feature. According to the logistic map and financial time series results, it can be inferred that the logistic map can be utilized as a financial time series. Further investigations can be performed in other fields through this financial simulation. The temporal evolutions of financial markets are also investigated. Although the results demonstrated higher noisy information for emerging markets, it was illustrated that emerging markets are getting more efficient over time. Additionally, the temporal investigations have demonstrated long-term lag and synchronous phases between developed and emerging markets. We also focused on the COVID-19 pandemic and compared the reactions of developing and emerging markets. It is ascertained that emerging markets have demonstrated higher uncertainty and overreaction to this pandemic. [ FROM AUTHOR] Copyright of International Journal of Nonlinear Sciences & Numerical Simulation is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Evidence of Shannon Entropy Diffusion Along Central Europe at the Covid-19 Pandemic

At the end of 2021, a 4th wave of Corona Virus 2019 (Covid-19 in short) pandemic has emerged at Germany against the expectations after a vaccination program that could have reached a 3/4 of German population (to date). It is actually interesting that the peak of infections at the third week of November is twice than the second wave as seen at data one year ago despite that at that times the vaccination scheme was still modest. This paper focuses at Germany and its ongoing wave that is perceived as a consequence of a type of entropy because the mobility of virus and infections. In addition the consequences of this entropy and the possible correlation at the neighbors countries such as Austria and Czech are analyzed. © 2022 IEEE.

LONGITUDINAL STUDY ASSESSING THE EFFECTS OF METHENAMINE HIPPURATE ON THE URINARY MICROBIOME OF POSTMENOPAUSALWOMEN WITH RECURRENT UTI

Introduction: Postmenopausal women with recurrent urinary tract infections (RUTI) are repeatedly exposed to antibiotics and therefore at risk for colonization by multi-drug resistant organisms. Methenamine hippurate (MH) is FDAapproved for the prevention of RUTI;however, the mechanism of action of MH or, more specifically, the role of MH in the alteration of the urobiome is not known. Since preliminary data has shown that MH may be effective against some bacteria (e.g., Escherichia coli), but not others (e.g., Enterococcus faecalis), we hypothesize that resident bladder microbiota will be altered by administration of MH. Objective: Our objective is to determine the longitudinal effect of MH on the urobiome of postmenopausal women with RUTI. Methods: A longitudinal study with a convenient sample of 10 postmenopausal women with a clinical history of RUTI was conducted (Figure 1). UDI6 questionnaires, voided urine, catheterized urine, and peri-urethral swabs were obtained at baseline and three months after daily MH use. Expanded quantitative urine culture (EQUC) was performed on these specimens. In addition, during the 3-month timeframe, four self-collection windows were completed (windows A-D): (A) prior to initiating MH (baseline urobiome), (B) one week after starting MH, (C) two weeks before the 3-month follow-up, and (D) one week before the 3-month follow-up. Voided urine and peri-urethral swabs were collected daily for one week during windows A-D to determine how the urobiome changed. Sequencing of samples from these collection windows is pending. Results: Ten participants enrolled;however, three participants were not able to complete the study due to allergic reaction, improper handling of samples, and COVID infection. Six participants have completed the study;microbiological studies for one participant are still in process. There were no episodes of acute cystitis for any participant during the length of the study. UDI6 results suggested a trend towards a decrease in frequency, leakage with urgency, and abdominal pain;however, none of these were statistically significant (Table 1). Of the six remaining participants, the average baseline urine pH was 5.8 ± 0.8. For the completed participants, an initial microbiological comparison of EQUC results at baseline and 3-month visits show differences in sample diversity. Specifically, the number of species detected (richness) in catheterized urine increased for all but one participant (Figures 2A and 2B) though there was little or no changes in overall diversity (Shannon Index, Figure 2B) or evenness (Pielou's Index, Figure 2C) for any sample type. Exposure to MH did not result in the loss of uropathogenic species present in catheterized urine at baseline;instead, additional uropathogenic and commensal microbiota were detected at the 3-month visit. Conclusions: UDI6 trended towards symptom improvement in frequency, urge incontinence, and pain, consistent with RUTI prevention and symptoms control. Microbiological results suggest that MH increases the richness of the bladder urobiome. This consistent trend suggests MH may reduce RUTI events by altering the urobiome community richness instead of eliminating uropathogenic microbiota from the bladder. Further studies are needed to understand the interaction between MH and a host that is susceptible to uropathogen overgrowth (Table Presented).

Health Misinformation on Social Media: A Systematic Literature Review and Future Research Directions

Health misinformation on social media is an emerging public concern as the COVID-19 infodemic tragically evidences. Key challenges that empower health misinformation’s spread include rapidly advancing social technologies and high social media usage penetration. However, research on health misinformation on social media lacks cohesion and has received limited attention from information systems (IS) researchers. Given this issue’s importance and relevance to the IS discipline, we summarize the current state of research on this emerging topic and identify research gaps together with meaningful research questions. Following a two-step literature search, we identify and analyze 101 papers. Drawing on the Shannon-Weaver communication model, we propose an integrative stage-based framework of health misinformation on social media. Based on literature analysis, we identify research opportunities and prescribe directions for future research on health misinformation on social media.

Differential privacy preserving dynamic data release scheme based on Jensen-Shannon divergence

Health monitoring data or the data about infectious diseases such as COVID-19 may need to be constantly updated and dynamically released, but they may contain user's sensitive information. Thus, how to preserve the user's privacy before their release is critically important yet challenging. Differential Privacy (DP) is well-known to provide effective privacy protection, and thus the dynamic DP preserving data release was designed to publish a histogram to meet DP guarantee. Unfortunately, this scheme may result in high cumulative errors and lower the data availability. To address this problem, in this paper, we apply Jensen-Shannon (JS) divergence to design the OPTICS (Ordering Points To Identify The Clustering Structure) scheme. It uses JS divergence to measure the difference between the updated data set at the current release time and private data set at the previous release time. By comparing the difference with a threshold, only when the difference is greater than the threshold, can we apply OPTICS to publish DP protected data sets. Our experimental results show that the absolute errors and average relative errors are significantly lower than those existing works.

Does the "Delta Variant" affect the nonlinear dynamic characteristics of SARS-CoV-2 transmission?

In this paper, we analyzed the difference of nonlinear dynamic characteristics of SARS-CoV-2 transmission caused by 'Delta Variant'. We selected the daily new diagnostic data of SARS-CoV-2 from 15 countries. Four different kinds of complexity metrics such as Kolmogorov complexity, Higuchi's Hurst exponent, Shannon entropy, and multifractal degrees were selected to explore the features of information content, persistence, randomness, multifractal complexity. Afterwards, Student's t-tests were performed to assess the presence of differences of these nonlinear dynamic characteristics for periods before and after "Delta Variant" appearance. The results of two-tailed Student's t-test showed that for all the nonlinear dynamic characteristics, the null hypothesis of equality of mean values were strongly rejected for the two periods. In addition, by one-tailed Student's t-test, we concluded that time series in "Delta period" exhibit higher value of Kolmogorov complexity and Shannon entropy, indicating a higher level of information content and randomness. On the other hand, the Higuchi's Hurst exponent in "Delta period" was lower, which showed the weaker persistent in this period. Moreover, the multifractal specturm width after "Delta" emergence were reduced, representing a more stable multifractality. The sources for the formation of multifractal features are also investigated.

Analyzing the Risk Factors of COVID-19 in India Using Intuitionistic Fuzzy VIKOR Method Based on Entropy Weighting

The virus that arises from Wuhan, popularly called as “coronavirus” has been spread all over the world in a short period. India has also taken preventive measures to control this threatening virus. In addition to precautions, it is necessary to analyze the risk factors of COVID-19 in overpopulated countries to reduce the impact of the virus. As India is the second-populated country, analyzing the risk factor of COVID-19 helps in categorizing the likely and non-likely people affect by the virus. The work manages the fuzziness through intuitionistic fuzzy sets combine with the VIKOR decision-making process to find the most influencing risk factors of COVID-19. The objective weights of the criteria are evaluated by entropy as it measures the randomness in discrete distribution. Moreover, sensitivity analysis is conducted to verify the robustness of the results of the proposed method.

A Skew Logistic Distribution for Modelling COVID-19 Waves and Its Evaluation Using the Empirical Survival Jensen-Shannon Divergence.

A novel yet simple extension of the symmetric logistic distribution is proposed by introducing a skewness parameter. It is shown how the three parameters of the ensuing skew logistic distribution may be estimated using maximum likelihood. The skew logistic distribution is then extended to the skew bi-logistic distribution to allow the modelling of multiple waves in epidemic time series data. The proposed skew-logistic model is validated on COVID-19 data from the UK, and is evaluated for goodness-of-fit against the logistic and normal distributions using the recently formulated empirical survival Jensen-Shannon divergence (ESJS) and the Kolmogorov-Smirnov two-sample test statistic (KS2). We employ 95% bootstrap confidence intervals to assess the improvement in goodness-of-fit of the skew logistic distribution over the other distributions. The obtained confidence intervals for the ESJS are narrower than those for the KS2 on using this dataset, implying that the ESJS is more powerful than the KS2.

An Interval-Valued Intuitionistic Fuzzy Model Based on Extended VIKOR and MARCOS for Sustainable Supplier Selection in Organ Transplantation Networks for Healthcare Devices

The selection of proper healthcare device suppliers in sustainable organ transplantation networks has become an essential topic of increasing life expectancy. Assessment of sustainable healthcare device suppliers can be regarded as a complex multi-criteria decision-making (MCDM) problem that consists of multiple alternative solutions with sustainable criteria. For this reason, this paper proposes a new integrated MCDM model based on combining an extended vlsekriterijuska optimizacija i komoromisno resenje (E-VIKOR) and measurement alternatives and ranking according to the compromise solution (MARCOS) approaches under interval-valued intuitionistic fuzzy sets (IVIFSs). The aggregating technique of the E-VIKOR method is a strong point of this method compared to the original approach. The IVIFS is taken to cope with the uncertain situation of real-world applications. In this regard, an IVIF-similarity measure is introduced to compute weights of the decision-makers (DMs). The IVIF-Shannon entropy method is utilized to calculate the criteria weights, and a new hybrid proposed model is developed by presenting the IVIF-E-VIKOR method and IVIF-MARCOS, to calculate the ranking of sustainable supplier alternatives in organ transplantation networks to supply the surgery devices. Afterward, an illustrative example is introduced to evaluate the performance of the proposed model, and a comparative analysis is presented to confirm and validate the proposed approach. Moreover, sensitivity analysis for essential parameters of the proposed model is performed to assess their effects on outcomes.

Spreading of Corona Virus in Central Europe: The Cases of Italy and Spain and the Very Beginning of Pandemic

In this paper a mathematical model that focuses at the very beginning of pandemic at Europe is presented. In essence it is assumed that once the virus arrived to Italy then the geographical propagation was done through probabilistic rules among then to Spain. Because of this the model of propagation of Feynman in conjunction to Wiener schemes have been used to model the displacement of virus from Wuhan to Milan as well from Milan to Spain, as seen at the end of 2019 triggering the beginning of European pandemic at January of 2020. As seen at official data Italy and Spain have presented same statistics at the first months of local pandemic. From the usage of the proposed formalism, it is found that the country data are following Gaussian-like distributions due to the space-time propagation of virus. © 2021 IEEE.

Pandemic of Covid-19 as Global Entropy: When Shannon Theory fits To-Date Data of New Infections

It is well-known that Coronavirus has been propagated due to human activities mainly based at intercontinental flights. Thus, in the first months of 2020, most new countries have already presented peaks in the number of infections, so that airports and borders were closed. With the social restrictions imposed along the beginning of second semester of 2020, the curve of cases of infections has exhibited to be flat in comparison to the beginning of 2020. Therefore, the human activities of end-of-year 2020 have caused againg peaks as the second wave of the pandemic in most countries. So far, by the end of 2021, most countries particularly located at Europe, are exhibiting the fourth wave. In this paper, the entropy of Shannon is considered as inherent mechanism and responsible of waves and large peaks of the number of infections. Modelling of data, the results of this paper suggest the inherent presence of a global entropy due to the transfer of randomness between neighboring countries. © 2021 IEEE.

Fifty years of Shannon information theory in assessing the accuracy and agreement of diagnostic tests.

Since 1948, Shannon theoretic methods for modeling information have found a wide range of applications in several areas where information plays a key role, which goes well beyond the original scopes for which they have been conceived, namely data compression and error correction over a noisy channel. Among other uses, these methods have been applied in the broad field of medical diagnostics since the 1970s, to quantify diagnostic information, to evaluate diagnostic test performance, but also to be used as technical tools in image processing and registration. This review illustrates the main contributions in assessing the accuracy of diagnostic tests and the agreement between raters, focusing on diagnostic test performance measurements and paired agreement evaluation. This work also presents a recent unified, coherent, and hopefully, final information-theoretical approach to deal with the flows of information involved among the patient, the diagnostic test performed to appraise the state of disease, and the raters who are checking the test results. The approach is assessed by considering two case studies: the first one is related to evaluating extra-prostatic cancers; the second concerns the quality of rapid tests for COVID-19 detection.

Feature-extraction and analysis based on spatial distribution of amino acids for SARS-CoV-2 Protein sequences.

BACKGROUND AND OBJECTIVE: The world is currently facing a global emergency due to COVID-19, which requires immediate strategies to strengthen healthcare facilities and prevent further deaths. To achieve effective remedies and solutions, research on different aspects, including the genomic and proteomic level characterizations of SARS-CoV-2, are critical. In this work, the spatial representation/composition and distribution frequency of 20 amino acids across the primary protein sequences of SARS-CoV-2 were examined according to different parameters. METHOD: To identify the spatial distribution of amino acids over the primary protein sequences of SARS-CoV-2, the Hurst exponent and Shannon entropy were applied as parameters to fetch the autocorrelation and amount of information over the spatial representations. The frequency distribution of each amino acid over the protein sequences was also evaluated. In the case of a one-dimensional sequence, the Hurst exponent (HE) was utilized due to its linear relationship with the fractal dimension (D), i.e. D+HE=2, to characterize fractality. Moreover, binary Shannon entropy was considered to measure the uncertainty in a binary sequence then further applied to calculate amino acid conservation in the primary protein sequences. RESULTS AND CONCLUSION: Fourteen (14) SARS-CoV protein sequences were evaluated and compared with 105 SARS-CoV-2 proteins. The simulation results demonstrate the differences in the collected information about the amino acid spatial distribution in the SARS-CoV-2 and SARS-CoV proteins, enabling researchers to distinguish between the two types of CoV. The spatial arrangement of amino acids also reveals similarities and dissimilarities among the important structural proteins, E, M, N and S, which is pivotal to establish an evolutionary tree with other CoV strains.

Advances in the computational analysis of SARS-COV2 genome.

Given a data-set of Ribonucleic acid (RNA) sequences we can infer the phylogenetics of the samples and tackle the information for scientific purposes. Based on current data and knowledge, the SARS-CoV-2 seemingly mutates much more slowly than the influenza virus that causes seasonal flu. However, very recent evolution poses some doubts about such conjecture and shadows the out-coming light of people vaccination. This paper adopts mathematical and computational tools for handling the challenge of analyzing the data-set of different clades of the severe acute respiratory syndrome virus-2 (SARS-CoV-2). On one hand, based on the mathematical paraphernalia of tools, the concept of distance associated with the Kolmogorov complexity and Shannon information theories, as well as with the Hamming scheme, are considered. On the other, advanced data processing computational techniques, such as, data compression, clustering and visualization, are borrowed for tackling the problem. The results of the synergistic approach reveal the complex time dynamics of the evolutionary process and may help to clarify future directions of the SARS-CoV-2 evolution.