THE CONTAGION NUMBER: HOW FAST CAN A DISEASE SPREAD?

The overall purpose of this paper is to define a new metric on the spreadability of a disease. Herein, we define a variant of the well-known graph-theoretic burning number (BN) metric that we coin the contagion number (CN). We aver that the CN is a better metric to model disease spread than the BN as the CN concentrates on first time infections. This is important because the Centers for Disease Control and Prevention report that COVID-19 reinfections are rare. This paper delineates a novel methodology to solve for the CN of any tree, in polynomial time, which addresses how fast a disease could spread (i.e., a worst-cast analysis). We then employ Monte Carlo simulation to determine the average contagion number (ACN) (i.e., a most-likely analysis) of how fast a disease would spread. The latter is analyzed on scale-free graphs, which are specifically designed to model human social networks (sociograms). We test our method on some randomly generated scale-free graphs and our findings indicate the CN to be a robust, tractable (the BN is NP-hard even for a tree), and effective disease spread metric for decision makers. The contributions herein advance disease spread understanding and reveal the importance of the underlying network structure. Understanding disease spreadability informs public policy and the associated managerial allocation decisions. © 2023 by the authors.

Memory Augmented Distributed Monte Carlo Tree Search Algorithm-Based Content Popularity Aware Content Recommendation Using Content Centric Networks

The rapid advancements of social media networks have created the problem of overloaded information. As a result, the service providers push multiple redundant contents and advertisements to the users without adequate analysis of the user interests. The content recommendation without user interests reduces the probability of users reading them and the wastage rate of network load increases. This problem can be alleviated by providing accurate content recommendations with consideration of users' precise interests and content similarity. Content centric networking has been developed as the trending framework to satisfy these requirements and improve access to relevant information and reception by the desired user. The uses of message entity by giving a proper name, the users' real-time interests are identified and then the accurate and popular contents with high contextual similarity are recommended. An efficient content recommendation scheme is presented in this paper using Memory Augmented Distributed Monte Carlo Tree Search (MAD-MCTS) algorithm for ensuring minimum energy consumption in the CCN. The big data context of the users' social media data is considered in this study so that the complexity can be visualized and controlled to minimize the network complexities. Experiments are conducted on a benchmark as well as an offline collected Twitter dataset on Covid-19 and the results implied that the accuracy and convergence of the proposed MAD-MCTS outperform the other content recommendation algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Inferring Phylogenetic Relationships using the Smith-Waterman Algorithm and Hierarchical Clustering

All biological species undergo change over time due to the evolutionary process. These changes can occur rapidly and unpredictably. Due to their high potential to spread quickly, it is critical to be able to monitor changes and detect viral variants. Phylogenetic trees serve as good methods to study evolutionary relationships. Complex big data in biomedicine is plentiful in regards to viral data. In this paper, we analyze phylogenetic trees with reference to viruses and conduct dynamic programming using the Smith-Waterman algorithm, followed by hierarchical clustering. This methodology constitutes an intelligent approach for data mining, paving the way for examining variations in SARS-Cov-2, which in turn can help to discover knowledge potentially useful in biomedicine. © 2022 IEEE.

An efficient lightweight encryption model with re-encryption scheme to create robust blockchain architecture for COVID-19 data

Handling electronic health records from the Internet of Medical Things is one of the most challenging research areas as it consists of sensitive information, which targets attackers. Also, dealing with modern healthcare systems is highly complex and expensive, requiring much secured storage space. However, blockchain technology can mitigate these problems through improved health record management. The proposed work develops a scalable, lightweight framework based on blockchain technology to improve COVID-19 data security, scalability and patient privacy. Initially, the COVID-19 related data records are hashed using the enhanced Merkle tree data structure. The hashed values are encrypted by lattice based cryptography with a Homomorphic proxy re-encryption scheme in which the input data are secured. After completing the encryption process, the blockchain uses inter planetary file system to store secured information. Finally, the Proof of Work concept is utilized to validate the security of the input COVID based data records. The proposed work's experimental setup is performed using the Python tool. The performance metrics like encryption time, re-encryption time, decryption time, overall processing time, and latency prove the efficacy of the proposed schemes. © 2022 John Wiley & Sons Ltd.

Longest k-tuple Common Sub-Strings

We focus on a new problem that is formulated to find a longest k-tuple of common sub-strings (abbr. k-CSSs) of two or more strings. We present a suffix tree based algorithm for this problem, which can find a longest k-CSS of m strings in O(kmn-{k}) time and O(kmn) space where n is the length sum of the m strings. This algorithm can be used to approximate the longest k-CSS problem to a performance ratio frac{1}{epsilon} in O(kmn-{lceilepsilon krceil}) time for epsilonin(0,1]. Since the algorithm has the space complexity in linear order of n, it will show advantage in comparing particularly long strings. This algorithm proves that the problem that asks to find a longest gapped pattern of non-constant number of strings is polynomial time solvable if the gap number is restricted constant, although the problem without any restriction on the gap number was proved NP-Hard. Using a C++ tool that is reliant on the algorithm, we performed experiments of finding longest 2-CSSs, 3-CSSs and 5-CSSs of 2 14 COVID-19 S-proteins. Under the help of longest 2-CSSs and 3-CSSs of COVID-19 S-proteins, we identified the mutation sites in the S-proteins of two COVID-19 variants Delta and Omicron. The algorithm based tool is available for downloading at https://github.com/lytt0/k-CSS. © 2022 IEEE.

Longest k-tuple Common Sub-Strings

We focus on a new problem that is formulated to find a longest k-tuple of common sub-strings (abbr. k-CSSs) of two or more strings. We present a suffix tree based algorithm for this problem, which can find a longest k-CSS of m strings in O(kmn-{k}) time and O(kmn) space where n is the length sum of the m strings. This algorithm can be used to approximate the longest k-CSS problem to a performance ratio frac{1}{epsilon} in O(kmn-{lceilepsilon krceil}) time for epsilonin(0,1]. Since the algorithm has the space complexity in linear order of n, it will show advantage in comparing particularly long strings. This algorithm proves that the problem that asks to find a longest gapped pattern of non-constant number of strings is polynomial time solvable if the gap number is restricted constant, although the problem without any restriction on the gap number was proved NP-Hard. Using a C++ tool that is reliant on the algorithm, we performed experiments of finding longest 2-CSSs, 3-CSSs and 5-CSSs of 2 14 COVID-19 S-proteins. Under the help of longest 2-CSSs and 3-CSSs of COVID-19 S-proteins, we identified the mutation sites in the S-proteins of two COVID-19 variants Delta and Omicron. The algorithm based tool is available for downloading at https://github.com/lytt0/k-CSS. © 2022 IEEE.

Pre-processing SARS-CoV-2 Sequence Data for Application of Machine Learning Techniques for Visualization and Clustering of Virus Characteristics

SARS-CoV-2, first known as unknown pneumonia on December 31, 2019, has been around the world for more than two years. As the virus has spread for a long time, various types of mutant viruses have occurred, and the sequence data of the virus has been accumulated considerably. Therefore, studies are being conducted on the types of mutations that are divided by analyzing sequence data and what features are found in which variants. Traditionally, this kinds of sequence analysis has been dominated by analysis and visualization using phylogenetic trees. Analysis with these phylogenetic trees can be useful if there is not much data. However, analysis and visualization are not easy when there are hundreds of thousands or millions of data. Thus, in this study, we propose a method to pre-process virus sequence data so that several machine learning techniques can be applied to better analyze and visualize data. In this study, SARS-CoV-2 sequence data is pre-processed by suggesting method and machine learning models such as Auto Encoder and DBSCAN are applied to extract important features and clustering the data. According to the experimental results, important features were extracted by reducing the dimension of the data, and it was confirmed that a numerous amount of viruses were well visualized on 3-dimensional graphs depending on the characteristics of the data, and that they were well clustered according to the virus variation. © 2022 Asia-Pacific of Signal and Information Processing Association (APSIPA).

Towards Privacy-Preserving Computation on Gene: Construct Covid-19 Phylogenetic Tree using Homomorphic Encryption

Constructing a phylogenetic tree is an essential method of analyzing the evolution of the covid-19 virus. In the case of multiple entities holding different coronavirus genetic data, it is simple to aggregate all data into one entity and then calculate the phylogenetic tree. However, such a method is challenging to carry out. Genetic data is susceptible and has high economic value, and it is usually impossible to copy between different entities directly. Also, the direct sharing of genetic data can lead to data leaks or even legal problems. In this paper, we propose a homomorphic-encryption-based solution to tackle this problem, where two participants, A and B, both hold a part of covid-19 genetic data and compute the gene distance matrix calculation of the overall dataset without revealing the genetic data held by both parties. After the computation, participant A can decrypt the final distance matrix from the encrypted result and then use the plain-text result to construct the covid-19 phylogenetic tree. Experiment results show that the proposed method can process the genetic data accurately in a short time, and the phylogenetic tree generated by the proposed solution has no loss of accuracy compared to plain-text calculation. In terms of engineering optimization, we propose an optimized encryption method, which can further shorten the encryption time of the entire dataset without reducing the security level. © 2022 IEEE.

Use of Classification Techniques for the Analysis of Data Related to COVID-19 in México

SARS-CoV-2 has bought many challenges to the world, socially, economically, and healthy habits. Even to those that have not experienced the sickness itself, and even though it has changed the lifestyle of the people across the world nation wise the effects of COVID-19 need to be analyzed and understood, analyzing a large amount of data is a process by itself, in this document details the analysis of the data collected from México by the Secretary of Health, the data was analyzed by implementing statistics, and classification methods known as K-Means, C&R Tree and TwoStep Cluster, using processed and unprocessed data. With the main emphasis on K-means. The study has the purpose of detecting what makes the highest impact on a person, to get sick, and succumb to the effects of the disease. In the study, it was found that in México the age of risk is at its highest at the age of 57, and the ones at the highest risk of mortality are those with hypertension and obesity, with those that present both at the age of 57 having a 19.37% of death. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Classification of German Jungian Extraversion and Introversion Texts with Assessment of Changes during the COVID-19 Pandemic

The corona pandemic and countermeasures such as social distancing and lockdowns have confronted individuals with new challenges for their mental health and well-being. It can be assumed that the Jungian psychology types of extraverts and introverts react differently to these challenges. We propose a Bi-LSTM model with an attention mechanism for classifying introversion and extraversion from German tweets, which is trained on hand-labeled data created by 335 participants. With this work, we provide this novel dataset for free use and validation. The proposed model achieves solid performance with F1 = .72. Furthermore, we created a feature engineered logistic model tree (LMT) trained on hand-labeled tweets, to which the data is also made available with this work. With this second model, German tweets before and during the pandemic have been investigated. Extraverts display more positive emotions, whilst introverts show more insight and higher rates of anxiety. Even though such a model can not replace proper psychological diagnostics, it can help shed light on linguistic markers and to help understand introversion and extraversion better for a variety of applications and investigations. © European Language Resources Association (ELRA)

Application of Machine Learning and Deep Learning in Forecasting Covid Daily Death Counts

Time series modeling and forecasting has fundamental importance to a wide range of applications. While classical time series models dominated the forecasting field for years, their applications have been limited to single time series data at low frequency such as monthly, quarterly or annually. The goal of this paper is to build multi-series time series models to forecast future daily death counts for each county in the state of Pennsylvania. The data used in this paper include JHU daily death counts and confirmed cases and CDC vaccination rates from 1/22/2020 to 1/7/2022 at the county level for Pennsylvania. Both machine learning (Extreme Gradient Boosted Tree XGBoost) and deep learning (Keras Slim Residual Neural Network Regressor, Keras) algorithms were explored and time series modeling related steps such as feature engineering, data partition and project setup are discussed in detail. In addition, four metrics were calculated to evaluate the algorithms' performance. The comparison with a baseline time series model indicated that machine learning and deep learning algorithms did improve forecasting accuracy significantly and Keras has slightly better performance than XGBoost. Finally, the Keras model was utilized to forecast daily death counts for 60 days after 1/7/2022, i.e., 1/8/2022 to 3/8/2022. Based on the model forecasts, daily death counts should gradually ease off by mid-February which has been validated by the subsequent observations. () © 2022 IEEE.

Geofencing-based Congestion Control in Workplaces Environment using Sequential Pattern Mining

To control congestion in the workplace environment especially in crises like the COVID-19 pandemic, this requires careful control of highly crowded workplace locations. Therefore, innovative technologies, such as geofencing and sequential pattern mining can be used to estimate people movement pattern and combat the spread of COVID-19. In this paper, the workplace area is divided into a set of geofences by using geofencing technology. Then, the movement profiles of each user are estimated to control the possible congestion in the workplace's enviroment. To accomplish this, the user's historical geofence transitions are used to anticipate the next time the user will leave the current geofence. The Sequential Pattern Discovery using Equivalence classes (CM-SPADE), Succinct BWT-based Sequence prediction model (SuBSeq) and Compact Prediction Tree + (CPT+) algorithms are adopted to predict the user's next geofence. In the CM-SPADE algorithm, a vertical database is obtained from the available database and the frequent sequence is found based on relative support, confidence, and lift measures. Meanwhile, in the training phase of the SuBSeq algorithm, Ferragina and Manzini (FM)-index and Burrows-Wheeler Transform string are generated. Then, in the ready-to-predict phase, the next geofence is anticipated. The CPT+ algorithm is based on generating Prediction Tree (PT), Lookup Table (LT), and Inverted Index (IIdx) for the training data. Then, Frequent Subsequence Compression (FSC) and Simple Branches Compression (SBC) are used to reduce the size of the PT. In addition, the Prediction with improved Noise Reduction (PNR) method is utilized to reduce the execution time. The results show remarkable superiority for SuBSeq algorithm over CM-SPADE and CPT+ with the accuracy greater than 90% withh an average of 8 input geofences to predict the next geofence. © 2022 IEEE.

A Robust Blockchain Architecture for Electronic Health Data using Efficient Lightweight Encryption Model with Re-Encryption Scheme

The handling of electronic health records (EHRs) from the Internet of Medical Things (IoMT) is one of the most challenging research areas as it consists of sensitive information which is a target for attackers. Also, it is highly complex and expensive to deal with modern healthcare systems as it requires a lot of secured storage space. However, these problems can be mitigated with the improvement in health record management using blockchain technology. To improve data security, patient privacy, and scalability, the proposed work develops a scalable lightweight framework based on blockchain technology. Initially, the COVID-19 related data records are hashed by using an enhanced Merkle tree (EMT) data structure. The hashed values are encrypted by lattice-based cryptography with a Homomorphic Proxy Re-Encryption scheme (LBC-HPRS) in which the input data are secured. After the completion of the encryption process, the blockchain uses IPFS to store secured information. Finally, the Proof of Work (PoW) concept is utilized to verify and validate the security of the input COVID-based data records. The experimental setup of the proposed work is performed by using a python tool and the performance metrics like encryption time, re-encryption time, decryption time, overall processing time and latency prove the efficacy of the proposed schemes. © 2022 IEEE.

Tracking the Evolution of Clusters in Social Media Streams

Tracking the evolution of clusters in social media streams is becoming increasingly important for many applications, such as early detection and monitoring of natural disasters or pandemics. In contrast to clustering on a static set of data, streaming data clustering does not have a global view of the complete data. The local (or partial) view in a high-speed stream makes clustering a challenging task. In this paper, we propose a novel density peak based algorithm, <monospace>TStream</monospace>, for tracking the evolution of clusters and outliers in social media streams, via the evolutionary actions of cluster adjustment, emergence, disappearance, split, and merge. <monospace>TStream</monospace> is based on a temporal decay model and text stream summarisation. The decay model captures the decreasing importance of textual documents over time. The stream summarisation compactly represents them with the help of cells (aka micro-clusters) in the memory. We also propose a novel efficient index called shared dependency tree (aka SD-Tree) based on the ideas of density peak and shared dependency. It maintains the dynamic dependency relationships in <monospace>TStream</monospace> and thereby improves the overall efficiency. We conduct extensive experiments on five real datasets. <monospace>TStream</monospace> outperforms the existing state-of-the-art solutions based on <monospace>MStream</monospace>, <monospace>MStreamF</monospace>, <monospace>EDMStream</monospace>, <monospace>OSGM</monospace>, and <monospace>EStream</monospace>, in terms of cluster mapping measure (CMM) by up to 17.8%, 18.6%, 6.9%, 16.4%, and 20.1%, respectively. It is also significantly more efficient than <monospace>MStream</monospace>, <monospace>MStreamF</monospace>, <monospace>OSGM</monospace>, and <monospace>EStream</monospace>, in terms of response time and throughput. IEEE

Propagation research of SARS-CoV-2 based on evolutionary tree and spectral clustering

RNA viruses have the characteristics of a high mutation rate. New Coronavirus (SARS-CoV-2), as a RNA virus, has been mutated to some extent since the outbreak of New Coronavirus pneumonia (COVID-19). It is of great significance to study the evolution and variation of novel coronavirus genes to analyze the source of virus infection and understand the evolution of viruses. This research is based on the Novel Coronavirus 2019 database at the National Genomics Data Center. We combined macro and micro. We used the phylogenetic tree to analyze the gene fragments of the virus, constructed an evolutionary tree with a depth of 301, searched the root node of the tree to find the source of the virus in the data set and used spectral clustering to analyze the degree of novel Coronavirus variation in each country and the clustering results were visualized to make them easier to observe. The experimental results show that the strain sample at the top of the evolutionary tree originated in New Zealand based on the existing data. In the evolutionary tree, the evolutionary process of the virus can be divided into three branches. After clustering the virus source data and constructing the visual map of the variation degree of SARS-COV-2, we found that the viruses in South Africa, New Zealand and other countries had a higher degree of variation, and the viruses in Australia, the United States and other countries have a relatively lower degree of virus variation. © 2022 IEEE.

Following the Trail of Fake News Spreaders in Social Media: A Deep Learning Model

Even though the Internet and social media are usually safe and enjoyable, communication through social media also bears risks. For more than ten years, there have been concerns regarding the manipulation of public opinion through the social Web. In particular, misinformation spreading has proven effective in influencing people, their beliefs and behaviors, from swaying opinions on elections to having direct consequences on health during the COVID-19 pandemic. Most techniques in the literature focus on identifying the individual pieces of misinformation or fake news based on a set of stylistic, content-derived features, user profiles or sharing statistics. Recently, those methods have been extended to identify spreaders. However, they are not enough to effectively detect either fake content or the users spreading it. In this context, this paper presents an initial proof of concept of a deep learning model for identifying fake news spreaders in social media, focusing not only on the characteristics of the shared content but also on user interactions and the resulting content propagation tree structures. Although preliminary, an experimental evaluation over COVID-related data showed promising results, significantly outperforming other alternatives in the literature. © 2022 Owner/Author.

Trim Distance between Positions in Nucleotide Sequences for Structural Proteins of SARS-CoV-2

A trim distance between two positions in the set of nucleotide sequences is a tree-based distance between the trimmed phylogenetic trees at two positions, each of which is obtained by applying the label-based closest-neighbor trimming method to the relabeled phylogenetic tree at the position that the index as a label of leaves is relabeled to the nucleotide occurring at the position. In this paper, as a tree-based distance, we adopt a label histogram distance and a depth histogram distance. Then, we introduce new trim distances that a label trim distance and a depth trim distance, respectively. Finally, by using the nucleotide sequences and the reconstructed phylogenetic tree from them provided from NCBI, we investigate the trim distances between the positions in the nucleotide sequences for structural proteins of spike, envelope, membrane and nucleocapsid proteins of SARS-CoV-2. © 2021 IEEE.

A Decision Making Approach for Street Lockdown to Cope with Covid-19 Cases by Using Shortest Path Selection Mechanism for Unplanned Colonies

Infectious disease syndrome like covid-19 falls under the Public health domain and needs to be addressed with timely decisions and rapid actions. For such diseases, the dispersal becomes exponential with frequent social gatherings, therefore the immediate strategy, to control the surging waves of covid-19, was to impose immediate lockdown of COVID-19 infected zones. In this paper, the concept of street networks has been incorporated with shortest path algorithm e.g. minimum spanning tree (MST) to define an approach to investigate the correlation between reported COVID-19 cases and relevant streets in order to adopt better lockdown strategy for unplanned colonies. Geo-spatial representation has been used for subsequent composition of patterns to identify the particular streets for locked down. Results show that MST provides better solution by evaluating explicit areas of concern for lockdown plans. © 2022 IEEE.

Application of Bayesian Network Reasoning Algorithm in Emotion Classification

In the early stage of covid-19 disease transmission, it is easy to lead to public panic and dissatisfaction without timely information feedback. In order to solve this problem, this paper constructs an emotion classification and prediction algorithm based on Bayesian network reasoning by analyzing the variable elimination algorithm, connection tree reasoning algorithm and Gibbs sampling algorithm in Bayesian network reasoning algorithm. The algorithm can quickly identify the emotions of Internet users from the communication with low computational resources, and provide reference for the relevant departments to formulate the correct public opinion guidance strategy. © 2021 IEEE.

A Logical Database Design Methodology for MongoDB NoSQL Databases

This paper presents a logical database design methodology for a MongoDB NoSQL database. Given a query, the design methodology is able to assist database designers to determine the best set of configurations of data, also known elsewhere as scheme trees, in the database such that the retrieval time of the query can be minimal or reduced. The design methodology first models an application of interest with a conceptual model. Based on our previous researches, the design methodology then generates from the conceptual model as few scheme trees as possible, which will eventually be implemented as MongoDB's collections in the database. To illustrate the design methodology, the COVID-19 data set was downloaded as an example application. The design methodology first conceptualized the data set with an Entity-Relationship model. Multiples queries were then devised to access various parts of the date set, whose executions required retrievals of the attribute values of all or some of the entity types and/or the relationship in the ER model. The design methodology then generated the best sets of scheme trees for the queries. © 2021 IEEE.