Multidimensional scaling analysis of virus diseases

Visualization of cross-resistance between antimicrobial agents by asymmetric multidimensional scaling

What is known and objective

In our previous studies, we developed a cross‐resistance rate (CRR) correlation diagram (CRR diagram) that visually captures the magnitude of CRRs between antimicrobials using scatter plots. We used asymmetric multidimensional scaling (MDS) to transform cross‐resistance similarities between antimicrobials into a 2‐dimensional map and attempted to visually express them. We also explored the antibiograms of Pseudomonas aeruginosa before and after the transfer to newly built hospitals, and we determined by the CRR diagram that the CRRs among β‐lactam antimicrobials other than carbapenems decreased substantially with the facility transfer. The present study tests whether the analysis of CRRs by asymmetric MDS can be used as new visual information that is easy for healthcare professionals to understand.

Method

We tested the impact of changes in the nosocomial environment due to institutional transfers on CRRs among antimicrobials in asymmetric MDS, as well as contrasted the asymmetric MDS map and CRR diagram.

Results and Discussion

In the asymmetric MDS map, antimicrobial groups with the same mechanism of action were displayed close together, and antimicrobial groups with different mechanisms of action were displayed separately. The asymmetric MDS map drawn solely for antimicrobials belonging to the group with the same mechanism of action showed similarities to the CRR diagram. Also, the distance of each antimicrobial to other antimicrobials shown in the asymmetric MDS map was negatively correlated with the CRRs for them against that antimicrobial.

What is new and conclusion

The asymmetric MDS map expresses the dissimilarity as distances between agents, and there are no meanings or units on the ordinate and abscissa axes of the output map. In contrast, the CRR diagram expresses the antimicrobials' resistance status as values, such as resistance rate and CRR. By analysing the CRRs in the asymmetric MDS, it is feasible to visually recognize cross‐resistance similarities between antimicrobial groups as distances. The use of the asymmetric MDS combined with the CRR diagram allows us to visually understand the resistance and cross‐resistance status of each antimicrobial agent as a 2‐dimensional map, as well as to understand the trends and characteristics of the data by means of quantitative values.

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.

Patterns of virus growth across the diversity of life

Although viruses in their natural habitats add up to less than 10% of the biomass, they contribute more than 90% of the genome sequences [1]. These viral sequences or 'viromes' encode viruses that populate the Earth's oceans [2, 3] and terrestrial environments [4, 5], where their infections impact life across diverse ecological niches and scales [6, 7], including humans [8-10]. Most viruses have yet to be isolated and cultured [11-13], and surprisingly few efforts have explored what analysis of available data might reveal about their nature. Here, we compiled and analyzed seven decades of one-step growth and other data for viruses from six major families, including their infections of archaeal, bacterial and eukaryotic hosts [14-191]. We found that the use of host cell biomass for virus production was highest for archaea at 10%, followed by bacteria at 1% and eukarya at 0.01%, highlighting the degree to which viruses of archaea and bacteria exploit their host cells. For individual host cells, the yield of virus progeny spanned a relatively narrow range (10-1000 infectious particles per cell) compared with the million-fold difference in size between the smallest and largest cells. Furthermore, healthy and infected host cells were remarkably similar in the time they needed to multiply themselves or their virus progeny. Specifically, the doubling time of healthy cells and the delay time for virus release from infected cells were not only correlated (r = 0.71, p < 10-10, n = 101); they also spanned the same range from tens of minutes to about a week. These results have implications for better understanding the growth, spread and persistence of viruses in complex natural habitats that abound with diverse hosts, including humans and their associated microbes.

Understanding COVID-19 nonlinear multi-scale dynamic spreading in Italy

The outbreak of COVID-19 in Italy took place in Lombardia, a densely populated and highly industrialized northern region, and spread across the northern and central part of Italy according to quite different temporal and spatial patterns. In this work, a multi-scale territorial analysis of the pandemic is carried out using various models and data-driven approaches. Specifically, a logistic regression is employed to capture the evolution of the total positive cases in each region and throughout Italy, and an enhanced version of a SIR-type model is tuned to fit the different territorial epidemic dynamics via a differential evolution algorithm. Hierarchical clustering and multidimensional analysis are further exploited to reveal the similarities/dissimilarities of the remarkably different geographical epidemic developments. The combination of parametric identifications and multi-scale data-driven analyses paves the way toward a closer understanding of the nonlinear, spatially nonuniform epidemic spreading in Italy.

An Evolutionary Perspective of Virus Propagation

This paper presents an evolutionary algorithm that simulates simplified scenarios of the diffusion of an infectious disease within a given population. The proposed evolutionary epidemic diffusion (EED) computational model has a limited number of variables and parameters, but is still able to simulate a variety of configurations that have a good adherence to real-world cases. The use of two space distances and the calculation of spatial 2-dimensional entropy are also examined. Several simulations demonstrate the feasibility of the EED for testing distinct social, logistic and economy risks. The performance of the system dynamics is assessed by several variables and indices. The global information is efficiently condensed and visualized by means of multidimensional scaling.

Computational analysis of the SARS-CoV-2 and other viruses based on the Kolmogorov's complexity and Shannon's information theories

This paper tackles the information of 133 RNA viruses available in public databases under the light of several mathematical and computational tools. First, the formal concepts of distance metrics, Kolmogorov complexity and Shannon information are recalled. Second, the computational tools available presently for tackling and visualizing patterns embedded in datasets, such as the hierarchical clustering and the multidimensional scaling, are discussed. The synergies of the common application of the mathematical and computational resources are then used for exploring the RNA data, cross-evaluating the normalized compression distance, entropy and Jensen-Shannon divergence, versus representations in two and three dimensions. The results of these different perspectives give extra light in what concerns the relations between the distinct RNA viruses.