Comparison of geological clusters between influenza and COVID-19 in Thailand with unsupervised clustering analysis.

The coronavirus disease (COVID-19) pandemic has considerably impacted public health, including the transmission patterns of other respiratory pathogens, such as the 2009 pandemic influenza (H1N1). COVID-19 and influenza are both respiratory infections that started with a lack of vaccination-based immunity in the population. However, vaccinations have been administered over time, resulting in a transition of the status of both diseases from a pandemic to an endemic.

Analysis of COVID-19 vaccine adverse event using language model and unsupervised machine learning.

Background: After the COVID-19 pandemic, the world has made efforts to recover from the chaotic situation. Vaccination is a way to help control infectious diseases, and many people have been vaccinated against COVID-19 by this point. However, an extremely small number of those who received the vaccine have experienced diverse side effects.

NGS data vectorization, clustering, and finding key codons in SARS-CoV-2 variations.

The rapid global spread and dissemination of SARS-CoV-2 has provided the virus with numerous opportunities to develop several variants. Thus, it is critical to determine the degree of the variations and in which part of the virus those variations occurred. Therefore, in this study, methods that could be used to vectorize the sequence data, perform clustering analysis, and visualize the results were proposed using machine learning methods.

Infectious disease outbreak prediction using media articles with machine learning models.

When a newly emerging infectious disease breaks out in a country, it brings critical damage to both human health conditions and the national economy. For this reason, apprehending which disease will newly emerge, and preparing countermeasures for that disease, are required. Many different types of infectious diseases are emerging and threatening global human health conditions.

Forecasting imported COVID-19 cases in South Korea using mobile roaming data.

As the number of global coronavirus disease (COVID-19) cases increases, the number of imported cases is gradually rising. Furthermore, there is no reduction in domestic outbreaks. To assess the risks from imported COVID-19 cases in South Korea, we suggest using the daily risk score.

Forecasting seasonal influenza-like illness in South Korea after 2 and 30 weeks using Google Trends and influenza data from Argentina.

We aimed to identify variables for forecasting seasonal and short-term targets for influenza-like illness (ILI) in South Korea, and other input variables through weekly time-series of the variables. We also aimed to suggest prediction models for ILI activity using a seasonal autoregressive integrated moving average, including exogenous variables (SARIMAX) models. We collected ILI, FluNet surveillance data, Google Trends (GT), weather, and air-pollution data from 2010 to 2019, applying cross-correlation analysis to identify the time lag between the two respective time-series.

Forecasting type-specific seasonal influenza after 26 weeks in the United States using influenza activities in other countries.

To identify countries that have seasonal patterns similar to the time series of influenza surveillance data in the United States and other countries, and to forecast the 2018-2019 seasonal influenza outbreak in the U.S., we collected the surveillance data of 164 countries using the FluNet database, search queries from Google Trends, and temperature from 2010 to 2018.

Effects of temperature, humidity, and diurnal temperature range on influenza incidence in a temperate region.

Background: The effect of temperature and humidity on the incidence of influenza may differ by climate region. In addition, the effect of diurnal temperature range on influenza incidence is unclear, according to previous study findings.

Objectives: The aim of this study was to analyze the effects of temperature, humidity, and diurnal temperature range on the incidence of influenza in Seoul, Republic of Korea, which is located in a temperate region.

Weekly ILI patient ratio change prediction using news articles with support vector machine.

Background: Influenza continues to pose a serious threat to human health worldwide. For this reason, detecting influenza infection patterns is critical. However, as the epidemic spread of influenza occurs sporadically and rapidly, it is not easy to estimate the future variance of influenza virus infection.

A Visualization Tool for Calculating the Genetic Substitution Patterns Between Two Different Groups.

We developed simulation tool for influenza virus variation (SimFluVar), an analytics software for calculating genomic variation among members of the influenza virus group. This study is related to computational evolutionary biology and evolutionary bioinformatics. SimFluVar is an analytical tool that can be used to calculate codon substitution patterns of viral genes.

Correlations Between the Incidence of National Notifiable Infectious Diseases and Public Open Data, Including Meteorological Factors and Medical Facility Resources.

Objectives: This study was performed to investigate the relationship between the incidence of national notifiable infectious diseases (NNIDs) and meteorological factors, air pollution levels, and hospital resources in Korea.

Methods: We collected and stored 660,000 pieces of publicly available data associated with infectious diseases from public data portals and the Diseases Web Statistics System of Korea. We analyzed correlations between the monthly incidence of these diseases and monthly average temperatures and monthly average relative humidity, as well as vaccination rates, number of hospitals, and number of hospital beds by district in Seoul.

SimFlu: a simulation tool for predicting the variation pattern of influenza A virus.

Since the first pandemic outbreak of avian influenza A virus (H5N1 subtype) in 1997, the National Center for Biotechnology Information (NCBI) has provided a large number of influenza virus sequences with well-organized annotations. Using the time-series sequences of influenza A viruses, we developed a simulation tool for influenza virus, named SimFlu, to predict possible future variants of influenza viruses. SimFlu can create variants from a seed nucleotide sequence of influenza A virus using the codon variation parameters included in the SimFlu package.

Protein backbone torsion angle-based structure comparison and secondary structure database web server.

Structural information has been a major concern for biological and pharmaceutical studies for its intimate relationship to the function of a protein. Three-dimensional representation of the positions of protein atoms is utilized among many structural information repositories that have been published. The reliability of the torsional system, which represents the native processes of structural change in the structural analysis, was partially proven with previous structural alignment studies.

Discriminant analysis of prion sequences for prediction of susceptibility.

Prion diseases, including ovine scrapie, bovine spongiform encephalopathy (BSE), human kuru and Creutzfeldt-Jakob disease (CJD), originate from a conformational change of the normal cellular prion protein (PrP(C)) into abnormal protease-resistant prion protein (PrP(Sc)). There is concern regarding these prion diseases because of the possibility of their zoonotic infections across species. Mutations and polymorphisms of prion sequences may influence prion-disease susceptibility through the modified expression and conformation of proteins.

Computational model for analyzing the evolutionary patterns of the neuraminidase gene of influenza A/H1N1.

In this study, we performed computer simulations to evaluate the changes of selection potentials of codons in influenza A/H1N1 from 1999 to 2009. We artificially generated the sequences by using the transition matrices of positively selected codons over time, and their similarities against the database of influenzavirus A genus were determined by BLAST search. This is the first approach to predict the evolutionary direction of influenza A virus (H1N1) by simulating the codon substitutions over time.

Evolutionary analysis of human-origin influenza A virus (H3N2) genes associated with the codon usage patterns since 1993.

This study investigated genetic variations in eight major genes (hemagglutinin, HA; neuraminidase, NA; matrix protein, MP; non-structural protein, NS; nucleoprotein, NP; polymerase, PA; PA basic protein 1, PB1; and PA basic protein 2, PB2) of the influenza A virus subtype H3N2 (A/H3N2) to determine the evolutionary pattern in codon bias. A total of 6,881 sequences isolated between 1993 and 2010 were used. The relative synonymous codon usage (RSCU) and G+C% content at the three codon positions were analyzed by calculating the codon substitution patterns were analyzed by calculating the percentage of synonymously substituted codons (SSCs) and that of codons substituted to the same codon within each synonymous codon group (EMC) between 1993 and subsequent years.

Comparative study of codon substitution patterns in foot-and-mouth disease virus (serotype O).

We compared genetic variations in the VP1 gene of foot-and-mouth disease viruses (FMDVs) isolated since 2000 from various region of the world. We analyzed relative synonymous codon usage (RSCU) and phylogenetic relationship between geographical regions, and calculated the genetic substitution patterns between Korean isolate and those from other countries. We calculated the ratios of synonymously substituted codons (SSC) to all observed substitutions and developed a new analytical parameter, EMC (the ratio of exact matching codons within each synonymous substitution group) to investigate more detailed substitution patterns within each synonymous codon group.

Comparative study of the nucleotide bias between the novel H1N1 and H5N1 subtypes of influenza A viruses using bioinformatics techniques.

Novel influenza A (H1N1) is a newly emerged flu virus which was first detected in April, 2009. Unlike the avian influenza (H5N1), this virus has been known to be able to spread from human to human directly. Although it is uncertain that how severe this novel H1N1 virus will be in terms of human illness, illness may be more widespread because most people will not have immunity to it.

Comparative study of synonymous codon usage variations between the nucleocapsid and spike genes of coronavirus, and C-type lectin domain genes of human and mouse.

Coronaviruses (CoVs) are single-stranded RNA viruses which contain the largest RNA genomes, and severe acute respiratory syndrome coronavirus (SARS-CoV), a newly found group 2 CoV, emerged as infectious disease with high mortality rate. In this study, we compared the synonymous codon usage patterns between the nucleocapsid and spike genes of CoVs, and C-type lectin domain (CTLD) genes of human and mouse on the codon basis. Findings indicate that the nucleocapsid genes of CoVs were affected from the synonymous codon usage bias than spike genes, and the CTLDs of human and mouse partially overlapped with the nucleocapsid genes of CoVs.

Comparative bioinformatics analysis of prion proteins isolated from reptile, rodent, ruminant, and human species.

Prion proteins (PrPs) are infectious pathogens that cause a group of invariably fatal, neurodegenerative diseases, including Creutzfeldt-Jakob disease, by means of an entirely novel mechanism. They are produced by various species, including reptile, rodent, ruminant and mammals, during normal metabolic processes, but they can be slowly changed into pathogenic isoforms upon contact with other infectious PrP isoforms. This transmission can occur across species barriers.

Comparative study of the hemagglutinin and neuraminidase genes of influenza A virus H3N2, H9N2, and H5N1 subtypes using bioinformatics techniques.

To investigate the genomic patterns of influenza A virus subtypes, such as H3N2, H9N2, and H5N1, we collected 1842 sequences of the hemagglutinin and neuraminidase genes from the NCBI database and parsed them into 7 categories: accession number, host species, sampling year, country, subtype, gene name, and sequence. The sequences that were isolated from the human, avian, and swine populations were extracted and stored in a MySQL database for intensive analysis. The GC content and relative synonymous codon usage (RSCU) values were calculated using JAVA codes.

[The current trend of avian influenza viruses in bioinformatics research].

Objectives: Since the first human infection from avian influenza was reported in Hong Kong in 1997, many Asian countries have confirmed outbreaks of highly pathogenic H5N1 avian influenza viruses. In addition to Asian countries, the EU authorities also held an urgent meeting in February 2006 at which it was agreed that Europe could also become the next target for H5N1 avian influenza in the near future. In this paper, we provide the general and applicable information on the avian influenza in the bioinformatics field to assist future studies in preventive medicine.

Epidemiological comparisons of codon usage patterns among HIV-1 isolates from Asia, Europe, Africa and the Americas.

To investigate the genomic properties of HIV-1, we collected 3,081 sequences from the HIV Sequence Database. The sequences were categorized according to sampling region, country, year, subtype, gene name, and sequence and were saved in a database constructed for this study. The relative synonymous codon usage (RSCU) values of matrix, capsid, and gp120 and gp41 genes were calculated using correspondence analysis.

Genomic analysis of influenza A viruses, including avian flu (H5N1) strains.

This study was designed to conduct genomic analysis in two steps, such as the overall relative synonymous codon usage (RSCU) analysis of the five virus species in the orthomyxoviridae family, and more intensive pattern analysis of the four subtypes of influenza A virus (H1N1, H2N2, H3N2, and H5N1) which were isolated from human population. All the subtypes were categorized by their isolated regions, including Asia, Europe, and Africa, and most of the synonymous codon usage patterns were analyzed by correspondence analysis (CA). As a result, influenza A virus showed the lowest synonymous codon usage bias among the virus species of the orthomyxoviridae family, and influenza B and influenza C virus were followed, while suggesting that influenza A virus might have an advantage in transmitting across the species barrier due to their low codon usage bias.