The research question for the work done in the data visualization assignment is “Which tool presents data more efficiently, Observable HQ or Tableau?” Outline The report on data visualization assignment is based on data visualization using two tools viz. Observable HQ and Tableau along with their comparisons. Literature review framed in next section of data visualization assignment discuses recent studies performed on direction of analysis of dataset based on Corona pandemic. The datasets are downloaded from official National Statistics, UK. This document offers insights of data visualization using ObervableHQ tool as major aim; many different aspects are shown in graphical format related to death rates, infections differentiated by countries, regions, continents, age and gender. Many analysis questions were framed at first before starting the task. The visualizations in ObservableHQ along with codes are shown after literature review section developed in the data visualization assignment. The line, pie and bar charts are selected to represent visualization. Later, visualization using Tableau tool is performed and necessary graphs are generated. Finally, comparison is done between two tools and report is concluded by listing major findings obtained in the data visualization assignment. LITERATURE REVIEW COVID-19 It is evident from the studies considered in this data visualization assignment that hundreds of civilians were destroyed by the sudden production of COVID- 19 inWuhan, Hubei Province, China. However, several affected people reported moderate flu-like effects and healed rapidly. Ruanet al. (2020) consider mild and severe behavioral predictors to allocate high-risk patients better. In longitudinal multicenter research, 68 death cases (68/150, 45 percent) and 82 discharged cases (82/150, 55 percent) of lab-confirmed SARS-CoV-2 disease have been carried out using a Jin Yin-tan and Tongji Hospital database, with the help of Ruanet al. (2020). Patients fulfilled the requirements of release because no infection was observed for at least three days. Their respiratory capacity was increased dramatically, and the findings were adversely changed twice in sequence for SARS-CoV-2 studies. Case details covered profiles, health characteristics, test findings, care voices, and outcomes. The statistical study by Ruanet al. (2020), which contrasted with student's t-test and the Mann — Whitney — Wilcoxon test, described continuous measurements as means (SD) or as medians (lQRs). Categorical variables were represented as numbers (percentage) and contrasted with the same check of p2 or Fisher. The age between the survival category and the release community was substantial (p The patients in the death group were assessed for their survival. There were two peaks in the surviv all time distribution from disease start to death, the first about 14 days (22 cases) , and the second about 22 days (17 cases). There has been no study of the cause of death. Of the 68 fatal cases, 36 (53%) died of respiratory, 5 (7%) died of circulatory failure, 22 (33%) died, and five remaining deaths were due to an unknown cause. Depending on the clinical evidence review, we verified the death sofmyocard it is inseveral cases. This study, first reported by Ruanetal. (2020) utilized in this section of data visualization assignment said that the SARS -CoV-2 infection could cause brilliant my ocard it is. Because of the progression and severe condition of myocarditis, their findings should alert doctors not only to the symptoms of respiratory dys function, but also the signs of cardiac injury. Which models are used to forecast COVID contamination and fatalities within the data visualization assignment? According to the latest Italian press release, there was a record of 27980 contaminated and 2158 COVID-19 fatalities. Because of the rapid growth of the COVID- 19, multiple studies have been conducted in a short period to forecast the phenomenon and its effects. This segment of data visualization assignment provides details on all recent research, most of which apply to predictive analytics. The model of an epidemic prediction comparing infected density and level of symptoms is proposed by Giulia Giordano et al. (2020). A SlDAR THE model has been suggested by the author to redefine the reproduction level. The simulations also demonstrate that after the contrast with real data on the COV lD-19 outbreak in Italy, the proposed model provides specific performance.
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