Includes a staic and dynamic visualization of sample data of Covid Infections in London. Open: - static visualization of covid infections - dynamicCovidVisualization

City-wise confirmed Covid-19 cases within India and specifically within KeralaFor discussions, please visit and follow the Facebook profile: https://www.facebook.com/viswaprabhaTo see the underlying live data, please visit this Google Sheet

2019 Novel Coronavirus COVID-19 (2019-nCoV) Visual Dashboard and Map:
https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

• Confirmed Cases by Country/Region/Sovereignty
• Confirmed Cases by Province/State/Dependency
• Deaths
• Recovered

Downloadable data:
https://github.com/CSSEGISandData/COVID-19

Additional Information about the Visual Dashboard:
https://systems.jhu.edu/research/public-health/ncov

The data displayed on the NSW vaccination map on nsw.gov.au is an important tool to help encourage the community to see the value of getting vaccinated to keep themselves and their loved ones safe. The Department of Customer Service has presented the data in a way that is easy to read and understand, but the data sources belong to the federal and state health agencies.\r \r This map is updated every Tuesdays and Fridays.

COVID-19Plots and analysis relating to the coronavirus pandemic. Includes five sets of plots and associated R code to generate them.1) HeatmapsUpdated every few days - heatmaps of COVID-19 case and death trajectories for Local Authorities (or equivalent) in England, Wales, Scotland, Ireland and Germany.2) All cause mortalityUpdated on Tuesday (for England & Wales), Wednesday (for Scotland) and Friday (for Northern Ireland) - analysis and plots of weekly all-cause deaths in 2020 compared to previous years by country, age, sex and region. Also a set of international comparisons using data from mortality.org3) ExposuresNo longer updated - mapping of potential COVID-19 mortality exposure at local levels (LSOAs) in England based on the age-sex structure of the population and levels of poor health.There is also a Shiny app which creates slightly lower resolution versions of the same plots online, which you can find here: https://victimofmaths.shinyapps.io/covidmapper/, on GitHub https://github.com/VictimOfMaths/COVIDmapper and uploaded to this record4) Index of Multiple Deprivation No longer updated - preliminary analysis of the inequality impacts of COVID-19 based on Local Authority level cases and levels of deprivation. 5) Socioeconomic inequalities. No longer updated (unless ONS release more data) - Analysis of published ONS figures of COVID-19 and other cause mortality in 2020 compared to previous years by deprivation decile.Latest versions of plots and associated analysis can be found on Twitter: https://twitter.com/victimofmathsThis work is described in more detail on the UK Data Service Impact and Innovation Lab blog: http://lab.ukdataservice.ac.uk/2020/05/21/visualising-high-risk-areas-for-covid-19-mortality/Adapted from data from the Office for National Statistics licensed under the Open Government Licence v.1.0.http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/

Supplementary Information and Raw Data for "https://www.plus.ac.at/biowissenschaften/der-fachbereich/arbeitsgruppen/duschl/members/martin-himly/list-of-publications/">Hofstätter N., Hofer S., Duschl A., and Himly M.
Children’s privilege in COVID-19: The protective role of the juvenile lung morphometry and ventilatory pattern on airborne SARS-CoV-2 transmission and severe pulmonary disease (2021). Biomedicines 9(10):1414. DOI: https://doi.org/10.3390/biomedicines9101414

1. pdf of deposition heatmaps (incl probability values) for 4 different VLP count medium diameters and 3 different age groups upon nose breathing

2. pdf of deposition heatmaps (incl probability values) for 4 different VLP count medium diameters and 3 different age groups upon mouth breathing

3. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 3 y upon nose breathing

4. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 3 y upon mouth breathing

5. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 8 y upon nose breathing

6. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 8 y upon mouth breathing

7. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 21 y upon nose breathing

8. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 21 y upon mouth breathing

Supplementary Information and Raw Data for Hofstaetter et al., 2021

1. pdf of deposition heatmaps (incl probability values) for 4 different VLP count medium diameters and 3 different age groups

2. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 3 y

3. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 8 y

4. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 21 y

The folder contains the different heatmaps originating from the variant prioritization analysis in both .jpeg and .html file. The latter are interactive. Moreover, the individuals genotype table originating the heatmaps is provided in .txt format.

The folder contains the heatmap plots obtained from the gene burden analysis considering gene disruptive variants. The plots are provided in a static format (.jpeg) and in a more interactive one (.html). Additionally, tables generating the heatmaps are provided in a .txt file format.

Exosomes mediate intercellular communication by transporting substrates with a variety of functions related to tissue homeostasis and disease. Recently, a subset of exosomes was labeled as defensosomes that are assembled during bacterial infection. Defensosomes mediate host defense by binding and inhibiting pore-forming toxins secreted by bacterial pathogens through incorporating protein receptors on their surface. Therefore, this study examined the role of defensosomes during infection by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which is the etiological agent of Coronavirus Disease 2019 (COVID-19). It was found that ACE2+ exosomes were induced by SARS-CoV-2 infection, which are defined as defensosomes. This study showed that ACE2+ defensosomes directly bind and block viral entry.

This dataset includes raw sequencing data, as well as supplementary data tied to the publication. The supplementary data contains data about characteristics of exosomes from COVID-19 patient bronchoalveolar lavage fluid, characterizing role of exosomes against SARS-CoV-2 in vitro, cryo-EM tomogram and 3D rendering of an exosome and SARS-CoV-2 virion, low expression of the short ACE2 isoform dACE2 detected in a subset of COVID patients, correlation of dACE2 expression in COVID patients with various interferon stimulated genes, full-length ACE2 and not dACE2 is loaded onto exosomes, heatmap of IPA analysis including comparisons between “high” and “low” groups for percent ACE2+ exosomes and ACE2 mean fluorescence intensity (MFI) value, and signaling pathway analysis of differentially expressed genes in the “high” percent ACE2+ exosome group and differentially expressed genes in the “high” ACE2 MFI exosome group.

The supplementary data also contains video about tomograms from Cryo-ET of SARS-CoV-2 and ACE2+ exosomes, low expression of the short ACE2 isoform dACE2 detected in a subset of COVID patients, and tomographic reconstructions of exosomes and virions. The data even includes raw western blot images and tables that contain data about demographics and clinical characteristics of the COVID-19 patient cohort, regression using negative binomial model and length of stay in the intensive care unit (ICU) as the outcome, linear regression on covariates including ACE2 MFI using length of stay in the ICU as the outcome and ACE2 MFI using ventilation days as an outcome, and regression using negative binomial model and ventilation days as the outcome. The data indicate that defensosomes may contribute to the antiviral response against SARS-CoV-2.

ObjectiveClinical triage in coronavirus disease 2019 (COVID-19) places a heavy burden on senior clinicians during a pandemic situation. However, risk stratification based on serum biomarker bioprofiling could be implemented by a larger, nonspecialist workforce.MethodMeasures of Complement Activation and inflammation in patientS with CoronAvirus DisEase 2019 (CASCADE) patients (n = 72), (clinicaltrials.gov: NCT04453527), classified as mild, moderate, or severe (by support needed to maintain SpO2 > 93%), and healthy controls (HC, n = 20), were bioprofiled using 76 immunological biomarkers and compared using ANOVA. Spearman correlation analysis on biomarker pairs was visualised via heatmaps. Linear Discriminant Analysis (LDA) models were generated to identify patients likely to deteriorate. An X-Gradient-boost (XGB) model trained on CASCADE data to triage patients as mild, moderate, and severe was retrospectively employed to classify COROnavirus Nomacopan Emergency Treatment for covid 19 infected patients with early signs of respiratory distress (CORONET) patients (n = 7) treated with nomacopan.ResultsThe LDA models distinctly discriminated between deteriorators, nondeteriorators, and HC, with IL-27, IP-10, MDC, ferritin, C5, and sC5b-9 among the key predictor variables during deterioration. C3a and C5 were elevated in all severity classes vs. HC (p < 0.05). sC5b-9 was elevated in the “moderate” and “severe” categories vs. HC (p < 0.001). Heatmap analysis shows a pairwise increase of negatively correlated pairs with IL-27. The XGB model indicated sC5b-9, IL-8, MCP1, and prothrombin F1 and F2 were key discriminators in nomacopan-treated patients (CORONET study).ConclusionDistinct immunological fingerprints from serum biomarkers exist within different severity classes of COVID-19, and harnessing them using machine learning enabled the development of clinically useful triage and prognostic tools. Complement-mediated lung injury plays a key role in COVID-19 pneumonia, and preliminary results hint at the usefulness of a C5 inhibitor in COVID-19 recovery.

COVID-19 pandemic is a global crisis that threatens our way of life. As of April 29, 2020, COVID-19 has claimed more than 200,000 lives, with a global mortality rate of ~7% and recovery rate of ~30%. Understanding the interaction of cellular targets to the SARS-CoV2 infection is crucial for therapeutic development. Therefore, the aim of this study was to perform a comparative analysis of transcriptomic signatures of infection of COVID-19 compared to different respiratory viruses (Ebola, H1N1, MERS-CoV, and SARS-CoV), to determine unique anti-COVID1-19 gene signature. We identified for the first time molecular pathways for Heparin-binding, RAGE, miRNA, and PLA2 inhibitors, to be associated with SARS-CoV2 infection. The NRCAM and SAA2 that are involved in severe inflammatory response, and FGF1 and FOXO1genes, which are associated with immune regulation, were found to be associated with a cellular gene response to COVID-19 infection. Moreover, several cytokines, most significantly the IL-8, IL-6, demonstrated key associations with COVID-19 infection. Interestingly, the only response gene that was shared between the five viral infections was SERPINB1. The PPI study sheds light on genes with high interaction activity that COVID-19 shares with other viral infections. The findings showed that the genetic pathways associated with Rheumatoid arthritis, AGE-RAGE signaling system, Malaria, Hepatitis B, and Influenza A were of high significance. We found that the virogenomic transcriptome of infection, gene modulation of host antiviral responses, and GO terms of both COVID-19 and Ebola are more similar compared to SARS, H1N1, and MERS. This work compares the virogenomic signatures of highly pathogenic viruses and provides valid targets for potential therapy against COVID-19.

Supplementary tables and figures

Figure 1 : Significant DEGs across the five transcriptomic profiles , corresponding genes, chromosome locations, gene expression and significance scores. The DEGs related genes and chromosomal location (A). The DEGs information regarding host response to COVID-19 (B), Ebola (C), MERS-CoV (D) , H1N1 (E) and SARS-CoV (F) viral infections. The pvalues were scaled were scaled across gene profiles according to maximum and minimum values (ppvalue). The circles size and color is linked to DEGs significance and gene expression (LogFC) scores, respectively.

Figure 2 : Analysis of the gene enrichment of DEGs correlated with the host response to COVID-19. Categories of GO terms (A), significance scores (-10log-pvalue) (B), and number of associated DEGs (C). The COVID-19-associated DEGs (D), status across the studied infectious diseases (E), and selected linked GO terms (F).

Figure 3: The Venn diagram of viral associated genes. The number of uniquely shared genes associated with the host response to COVID-19, Ebola, H1N1, MERS-CoV, and SARS-CoV viral infections.

Figure 4: The Venn diagram of viral associated GO terms. The number of uniquely shared GO terms of DEGs associated with the host response across COVID-19, Ebola, H1N1, MERS-CoV, and SARS-CoV viral infections.

Figure 5: The PPIs network of DEGs associated with COVID-19. The PPI of host expressed DEGs under COVID-19 infection. DEGs shared between COVID-19 and Ebola, H1N1, MERS-CoV, and SARS-CoV are color-coded according to kind of infection. The gene node size is relative to its interaction activity. DEGs are collected in different groups according to their level of interaction activity.

Figure 6: The PPIs network and gene enrichment analysis of highly interactive genes associated with COVID-19.

Figure S1 : The PPI network and gene enrichment analysis of the 173 genes that characterized the host response of COVID-19.

Figure S2: The PPI network and gene enrichment analysis of the 58 genes that are uniquely shared between COVID -19 and Ebola viral infections .

Figure S3 : The PPI network and gene enrichment analysis of the 51 genes that are uniquely shared between COVID-19 and MERS-CoV viral infections.

Figure S4 : The PPI network and gene enrichment analysis of the 31 genes that are uniquely shared between COVID-19, Ebola, and MERS-CoV viral infections.

Figure S5 : The gene expression heatmap of genes COVID-19 shares with different viral infections.

Figure S6 : The PPI network and gene enrichment analysis of genes that are differentially expressed across studied viral infections and shared with COVID-19.

Table S1 : The data information used in this study.

Table S2: The information of DEGs associated the host response of COVID-19, Ebola, H1N1, MERS-CoV, and SARS-CoV viral infections.

Table S3: The Venn analysis results of DEGs and GO terms uniquely shared across of COVID-19, Ebola, H1N1, MERS-CoV, and SARS-CoV viral infections.

Table S4: Selected gene enrichment analysis of uniquely shared group of genes across the host response of COVID-19, Ebola, H1N1, MERS-CoV, and SARS-CoV viral infections.

Table S5: The gene expression information of DEGs that COVID-19 share with the studied infectious diseases.

Table S6: Selected gene enrichment analysis of uniquely shared group of GO terms across the host response of COVID-19 and studied viral infections.

The folder contains the heatmap plots obtained from the gene burden analysis considering non-coding variants. The plots are provided in a static format (.jpeg) and in a more interactive one (.html). Additionally, tables generating the heatmaps are provided in a .txt file format.

AI Ambulance Demand Heat-Map Planner Market Outlook

As per our latest research, the global AI Ambulance Demand Heat-Map Planner market size reached USD 627 million in 2024, reflecting the rapid adoption of artificial intelligence in emergency response systems. The market is projected to grow at a robust CAGR of 16.2% from 2025 to 2033, reaching a forecasted value of USD 2,068 million by 2033. The primary growth driver for this market is the increasing need for real-time data analytics and predictive modeling to optimize ambulance deployment and reduce emergency response times.

A key growth factor propelling the AI Ambulance Demand Heat-Map Planner market is the surging global urbanization, which has led to higher population densities and more complex city infrastructures. These conditions significantly heighten the demand for efficient emergency medical services, requiring advanced solutions that can dynamically allocate ambulance resources based on predictive analytics. AI-powered heat-map planners are transforming how emergency medical services (EMS) respond to incidents by analyzing vast datasets, including historical call data, traffic patterns, and demographic information, to forecast demand hotspots. This capability allows EMS providers to proactively position ambulances in anticipation of emergencies, substantially reducing response times and improving patient outcomes. Additionally, the integration of AI with IoT and real-time GPS tracking further enhances the precision and reliability of these solutions, making them invaluable for modern urban environments.

Another significant driver is the increasing governmental emphasis on public health and safety, which has resulted in enhanced funding for digital transformation within healthcare and emergency response sectors. Governments and municipal bodies across the globe are investing heavily in smart city initiatives, where AI-based ambulance demand heat-map planners play a critical role in optimizing resource allocation. These investments are not only aimed at improving operational efficiency but also at achieving better regulatory compliance and meeting stringent response time targets set by health authorities. The COVID-19 pandemic further accelerated this trend, as healthcare systems worldwide recognized the need for scalable, data-driven solutions to manage surges in emergency calls and optimize ambulance fleet deployment during crises.

Technological advancements in machine learning, big data analytics, and cloud computing are further catalyzing the growth of the AI Ambulance Demand Heat-Map Planner market. Modern AI algorithms can process and learn from massive, heterogeneous data sources, providing actionable insights that were previously unattainable. The adoption of cloud-based platforms has democratized access to these advanced analytics tools, enabling even mid-sized hospitals and regional EMS providers to benefit from AI-driven planning solutions. Furthermore, ongoing research and development activities are enhancing the accuracy and scalability of these systems, allowing for customization according to local needs and regulations. This technological evolution is expected to continue driving market expansion over the forecast period.

From a regional perspective, North America currently dominates the AI Ambulance Demand Heat-Map Planner market, owing to its advanced healthcare infrastructure, significant investments in AI technologies, and supportive regulatory environment. Europe follows closely, with growing adoption in countries such as Germany, the UK, and France, driven by robust healthcare systems and increasing focus on smart city initiatives. The Asia Pacific region is expected to witness the fastest growth, fueled by rapid urbanization, expanding healthcare networks, and rising government investments in digital health. Latin America and the Middle East & Africa are also emerging as promising markets due to increasing awareness of the benefits of AI in emergency response and gradual improvements in healthcare infrastructure.

Component Analysis

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The folder contains the heatmap plots obtained from the gene burden analysis considering non-synonymous variants. The plots are provided in a static format (.jpeg) and in a more interactive one (.html). Additionally, tables generating the heatmaps are provided in a .txt file format.

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains a serious pandemic. COVID-19 vaccination is urgent needed for limiting SARS-CoV-2 outbreaks by herd immunity. Simultaneously, post-marketing surveillance to assess vaccine safety is important, and collection of vaccine-related adverse events has been in progress. Vision-threatening ophthalmic adverse events of COVID-19 vaccines are rare but are a matter of concern. We report a 45-year-old Japanese male with positive for HLA-DR4/HLA-DRB1*0405, who developed bilateral panuveitis resembling Vogt-Koyanagi-Harada (VKH) disease after the second dose of Pfizer-BioNTech COVID-19 mRNA (BNT162b2) vaccine. Glucocorticosteroid (GC) therapy combined with cyclosporine A (CsA) readily improved the panuveitis. The immune profile at the time of onset was analyzed using CyTOF technology, which revealed activations of innate immunity mainly consisting of natural killer cells, and acquired immunity predominantly composed of B cells and CD8+ T cells. On the other hand, the immune profile in the remission phase was altered by GC therapy with CsA to a profile composed primarily of CD4+ cells, which was considerably similar to that of the healthy control before the vaccination. Our results indicate that BNT162b2 vaccine may trigger an accidental immune cross-reactivity to melanocyte epitopes in the choroid, resulting in the onset of panuveitis resembling VKH disease.

BackgroundCoronavirus disease 2019 (COVID-19) is a global pandemic. Previous studies have reported dyslipidemia in patients with COVID-19. Herein, we conducted a retrospective study and a bioinformatics analysis to evaluate the essential data of the lipid profile as well as the possible mechanism in patients with COVID-19.MethodsFirst of all, the retrospective study included three cohorts: patients with COVID-19, a healthy population, and patients with chronic obstructive pulmonary disease (COPD). For each subject, serum lipid profiles in the biochemical data were compared, including triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). Furthermore, bioinformatics analyses were performed for exploring the biological or immunological mechanisms.ResultsIn line with the biochemical data of the three cohorts, the statistical result displayed that patients with COVID-19 were more likely to have lower levels of TC and HDL-C as compared with healthy individuals. The differential proteins associated with COVID-19 are involved in the lipid pathway and can target and regulate cytokines and immune cells. Additionally, a heatmap revealed that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections were possibly involved in lipid metabolic reprogramming. The viral proteins, such as spike (S) and non-structural protein 2 (Nsp2) of SARS-CoV-2, may be involved in metabolic reprogramming.ConclusionThe metabolic reprogramming after SARS-CoV-2 infections is probably associated with the immune and clinical phenotype of patients. Hence, metabolic reprogramming may be targeted for developing antivirals against COVID-19.

Elaborado pela equipe do projeto SIG Litoral (https://www.ufrgs.br/sig) coordenado pelo Prof. Ricardo Dagnino (https://www.professor.ufrgs.br/dagnino) com base nos dados de contágio de Coronavírus Covid-19 coletados pelas Secretarias Municipais do Litoral Norte do Rio Grande do Sul (18 Coordenadoria Regional de Saúde) e pela Secretaria Estadual de Saúde do Rio Grande do Sul.Os municípios pertencentes a 18 CRS são: Arroio do Sal, Balneário Pinhal, Capão da Canoa, Capivari do Sul, Caraá, Cidreira, Dom Pedro de Alcântara, Imbé, Itati, Mampituba, Maquiné, Morrinhos do Sul, Mostardas, Osório, Palmares do Sul, Santo Antônio da Patrulha, Tavares, Terra de Areia, Torres, Tramandaí, Três Cachoeiras, Três Forquilhas e Xangri-lá.Dados deste MapviewerEste mapa está baseado na camada de visualização - Feature Layer (hospedado, visualização) RS_mun_2018_inserir_automatico_visualizacao (https://arcg.is/19fDPr) criada a partir do shape RS_mun_2018_inserir_automatico (aqui) e na camada RS_mun_2018_litoral (https://arcg.is/1L4bqn).A partir da visualização foram criados três webmaps:“Covid19 - Taxa de casos ativos nos Municípios do Rio Grande do Sul-2” - https://arcg.is/0m08CK“Covid19 nos Municípios do Rio Grande do Sul” - https://arcg.is/bq14a"Compilação de dados de Covid19 nos Municípios do Rio Grande do Sul" - https://arcg.is/1TfL81A partir dos dados da camada de visualização são calculados os centróides e criados heatmaps: Covid19 - Casos ativos nos Municípios do Rio Grande do Sul - Heatmap - https://arcg.is/1feTmH Covid19 - Taxas casos Covid19 por mil hab. nos Municípios do Rio Grande do Sul - Heatmap - https://arcg.is/1e9iW0Os mapas estão ligados a duas versões de Dashboard:Painel de casos de Coronavírus (Covid-19) no Rio Grande do Sul - versão desktop: bit.ly/Covid19_RS_desktop_editPainel de casos de Coronavírus (Covid-19) no Rio Grande do Sul - versão móvel: bit.ly/Covid19_RS_movel_editPara saber mais consulte o texto:Dagnino, R.; Weber, E. J.; Panitz, L. M. Monitoramento do Coronavírus (Covid-19) nos municípios do Rio Grande do Sul, Brasil. SocArXiv, 28 Mar. 2020. https://doi.org/10.31235/osf.io/3uqn5Bases de dados:Dagnino, R.; Weber, E.; Panitz, L. Coronavírus (Covid-19) nos municípios do Rio Grande do Sul. Harvard Dataverse, V1, 28 mar. 2020. https://doi.org/10.7910/DVN/JK4STLVisite a página do projeto SIG Litoral da Universidade Federal do Rio Grande do Sul: www.ufrgs.br/sigConsulte os dados em: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JK4STL

ObjectiveThe primary objective of this study was to analyze CpG dinucleotide dynamics in coronaviruses by comparing Wuhan-Hu-1 with its closest and most distant relatives. Heatmaps were generated to visualize CpG counts and O/E ratios across intergenic regions, providing a clear depiction of conserved and divergent CpG patterns.Methods1. Data CollectionSource : The dataset includes CpG counts and O/E ratios for various coronaviruses, extracted from publicly available genomic sequences.Format : Data was compiled into a CSV file containing columns for intergenic regions, CpG counts, and O/E ratios for each virus.2. PreprocessingData Cleaning :Missing values (NaN), infinite values (inf, -inf), and blank entries were handled using Python's pandas library.Missing values were replaced with column means, and infinite values were capped at a large finite value (1e9).Reshaping :The data was reshaped into matrices for CpG counts and O/E ratios using meltpandas[] and pivot[] functions.3. Distance CalculationEuclidean Distance :Pairwise Euclidean distances were calculated between Wuhan-Hu-1 and other viruses using the scipy.spatial.distance.euclidean function.Distances were computed separately for CpG counts and O/E ratios, and the total distance was derived as the sum of both metrics.4. Identification of Closest and Distant RelativesThe virus with the smallest total distance was identified as the closest relative .The virus with the largest total distance was identified as the most distant relative .5. Heatmap GenerationTools :Heatmaps were generated using Python's seaborn library (sns.heatmap) and matplotlib for visualization.Parameters :Heatmaps were annotated with numerical values for clarity.A color gradient (coolwarm) was used to represent varying CpG counts and O/E ratios.Titles and axis labels were added to describe the comparison between Wuhan-Hu-1 and its relatives.ResultsClosest Relative :The closest relative to Wuhan-Hu-1 was identified based on the smallest Euclidean distance.Heatmaps for CpG counts and O/E ratios show high similarity in specific intergenic regions.Most Distant Relative :The most distant relative was identified based on the largest Euclidean distance.Heatmaps reveal significant differences in CpG dynamics compared to Wuhan-Hu-1 .Tools and LibrariesThe following tools and libraries were used in this analysis:Programming Language :Python 3.13Libraries :pandas: For data manipulation and cleaning.numpy: For numerical operations and handling missing/infinite values.scipy.spatial.distance: For calculating Euclidean distances.seaborn: For generating heatmaps.matplotlib: For additional visualization enhancements.File Formats :Input: CSV files containing CpG counts and O/E ratios.Output: PNG images of heatmaps.Files IncludedCSV File :Contains the raw data of CpG counts and O/E ratios for all viruses.Heatmap Images :Heatmaps for CpG counts and O/E ratios comparing Wuhan-Hu-1 with its closest and most distant relatives.Python Script :Full Python code used for data processing, distance calculation, and heatmap generation.Usage NotesResearchers can use this dataset to further explore the evolutionary dynamics of CpG dinucleotides in coronaviruses.The Python script can be adapted to analyze other viral genomes or datasets.Heatmaps provide a visual summary of CpG dynamics, aiding in hypothesis generation and experimental design.AcknowledgmentsSpecial thanks to the open-source community for developing tools like pandas, numpy, seaborn, and matplotlib.This work was conducted as part of an independent research project in molecular biology and bioinformatics.LicenseThis dataset is shared under the CC BY 4.0 License , allowing others to share and adapt the material as long as proper attribution is given.DOI: 10.6084/m9.figshare.28736501

PurposeStaySafe PH is the Philippines’ official contact tracing software for controlling the propagation of COVID-19 and promoting a uniform contact tracing strategy. The StaySafe PH has various features such as a social distancing system, LGU heat map and response system, real-time monitoring, graphs, infographics, and the primary purpose, which is a contact tracing system. This application is mandatory in establishments such as fast-food restaurants, banks, and malls.Objective and methodologyThe purpose of this research was to determine the country’s willingness to utilize StaySafe PH. Specifically, this study utilized 12 latent variables from the integrated Protection Motivation Theory (PMT), Unified Theory of Acceptance and Use of Technology (UTAUT2), and System Usability Scale (SUS). Data from 646 respondents in the Philippines were employed through Structural Equation Modelling (SEM), Deep Learning Neural Network (DLNN), and SUS.ResultsUtilizing the SEM, it is found that understanding the COVID-19 vaccine, understanding the COVID-19 Delta variant, perceived vulnerability, perceived severity, performance expectancy, social influence, hedonic motivation, behavioral intention, actual use, and the system usability scale are major determinants of intent to utilize the application. Understanding of the COVID-19 Delta Variant was found to be the most important factor by DLNN, which is congruent with the results of SEM. The SUS score of the application is "D", which implies that the application has poor usability.ImplicationsIt could be implicated that large concerns stem from the trust issues on privacy, data security, and overall consent in the information needed. This is one area that should be promoted. That is, how the data is stored and kept, utilized, and covered by the system, how the assurance could be provided among consumers, and how the government would manage the information obtained. Building the trust is crucial on the development and deployment of these types of technology. The results in this study can also suggest that individuals in the Philippines expected and were certain that vaccination would help them not contract the virus and thus not be vulnerable, leading to a positive actual use of the application.NoveltyThe current study considered encompassing health-related behaviors using the PMT, integrating with the technology acceptance model, UTAUT2; as well as usability perspective using the SUS. This study was the first one to evaluate and assess a contact tracing application in the Philippines, as well as integrate the frameworks to provide a holistic measurement.