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Covid- 19 Pandemic Data Analysis Assignment Sample

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Covid- 19 Pandemic Data Analysis Assignment Sample

Introduction - Covid- 19 Pandemic Data Analysis

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Corona virus condition 2019 (COVID-19) seems to be a communicable disease infection characterized by the "SARS-CoV-2 virus". This has had a disastrous effect upon the world's largest demographic trends, actually resulting in over approximately 5.5 million deaths around the world. It really has surfaced as being the most important global health emergency ever since the pandemic of influenza which had happened in the time of 1918. When the first instances of such a primarily respiration strep infection have always been published throughout delayed December 2019 throughout Wuhan, Hubei Province, China, "SARS-CoV-2" speedily spread around the world, prompting the "World Health Organization or WHO" to proclaim it a worldwide disease outbreak on March 11, 2020. Corona viruses were indeed viruses which infects that causes illnesses including such affecting the gastrointestinal problems. Predisposing disease can ballpark from either the respiratory illness to much more serious illnesses, such as asthma. Corona viruses were named after those who appear underneath a magnifying glass. The virus has always been made up of a center of genetic information enveloped by a protein-spiked wrapper. It has the aesthetic of a throne as a result of all this. In Latin, the term Corona indicates "crown." Corona viruses seem to be highly infectious, which means they spread among both humans as well as animals. MERS-CoV has been found to be conveyed from small ruminant caravans to living beings, as well as SARS-CoV had also been found towards be conveyed from single origin cats to living beings. The reference of such "SARS-CoV-2 or COVID-19" outbreak is unknown, and yet explorations are underway to recognize the arboviral root cause of the problem. The outbreak for the disease of COVID-19 had already resulted in a large number of civilian casualties from around world, trying to pose an entrepreneurial spirit aimed at improving healthcare quality, preparing meals methodologies, and safe working conditions. The pandemic's economy is heavily reliant is catastrophic: million people around the world seem towards be at danger of dropping into absolute poverty, as well as the number of slum dwellers, which would be considered likely at well almost approximately 692 million, might rise by approximately of about approximately 130 million at the cease of the year Businesses are seems to be upon the verge of closing down. Almost the majority of the world's largest 3.5 billion working-age people would have been at risk of serious harm. Coworkers throughout the shadow economy found towards be particularly vulnerable because with a significant percentage insufficient state assistance, access to adequate medical treatment, and production factors. Numerous people seem unable to nourish themself as well as their own family members all through shutdowns so even though they complete absence the means to support themselves With most individuals, no income means neither lunches, and at best, less nutritionally sustenance. The cholera epidemic must have caused havoc upon that vital food foundation, uncovering its frailties. Border shutdowns, protectionist measures, as well as incarceration indicators had already made it difficult for farm owners to connect marketplace, such as towards buy input data as well as market their products, and also for farm workers towards harvest crops, interrupting internal as well as overseas supply chain operations and also lowering access to nutritional, secure, and diversified nutrition. The flu epidemic had also annihilated jobs and put millions of community livelihoods in jeopardy. As wage earners quit their benefits, become ill, or die, millions of women as well as men's food security were indeed jeopardized, with dramatic reductions in barriers, especially the much more marginalized communities, such as local producers as well as indigenous peoples, bearing the brunt of the burden.

In the description of the data set it can be said that there are two data sets: the first one name is project data one and second one name is project data two. In project data 1 there are almost 13000 records about the information of covid-19 full. In the data set the first variable is the continent the countries have been given. Then the next variable is location in the location the name of the countries have been given after that the next variable is date, in the date variable the data has been taken in when the information of the covid-19 been observed. After that there is the variable of new cases where the new cases number has been given, then in the data set new days have been given in each country and the number of new cases have been varied with the dates of the observation. The number of tests for covid-19 have been given after that the next variable is new vaccination.

Then the next data set is combined with few variables which is actually the effect of covid-19 outbreak or pandemic on different countries. So the first variable is continent, after that the variable is location where different countries name have been given, after this variable the next variable is population of the population of each country's in between the data set have been given. Then the next variable is population density where the density of the population for each country has been given, then the next variable is median age after that it the people whose age are 65 or over have been given for each of the country then the expectancy of the life has been given for each country after that the GDP per capita has been given for or every country in the data set then the value has been given for the the extreme poverty level for each of their countries of the data set.

Then there is another data set which has made manually the data of those countries feature Australia China India Sweden Russia United Kingdom and United state have been given in the data set there are new cases new death new test vaccination population population density median age GDP per capita death per million has been given for those seven countries.

Analysis

For analyzing the progression of the data of the pandemic of covid-19 in terms of different countries' total number of cases of covid-19, the data set where the new cases and the location of the cases indicates the countries name have been imported with the help of R studio analysis code. There have been shown a graph with the use of the library function ggplot2 and with the library function of tidyverse (Cost et al. 2021). After that the data has been modified and the countries name are Australia China India Sweden Russia United Kingdom and United States data have been separated. Then on the number of new cases of covid-19 and on the name of the countries a graph has been shown using the library function of jiji plot and with the library function of tidyverse.

After that the data set is combined from the data set of project data 1 and from the data set of project data 2. And in the data set the death per million has been calculated on the countries of Australia China India Sweden Russia United Kingdom and United State. For the numeric summaries of the combined data set found the mean value, the median value and the standard deviation (Babi? et al. 2020). The mean value of new cases in the data set is 2374.286, the mean value of the new cases per million is 0.002051478. And the mean value for the density of the population is 134.7 631. The median value for new cases is 770, the median value for the new cases per million is 0.001128915, median value for the population density is 35.608. and the standard deviation for new cases is 2927.4 61, the standard deviation for or new cases per million is 0.002190971. Then a bar graph has been shown on the new cases vs the cases which are new on per million in those countries.

There are not any different patterns of the data set, just a calculation has been made for the new cases count for each of those seven countries.

Through the analysis of the data set using r studio the mean value of new cases population density and the new cases per million has been found and also the median value and standard deviation of the three variables which are new cases population density and new casess per million has been found. After that a bar graph has been created on the variable of new cases and under the variable of new cases per million.

After that in the next analysis of the data set using r studio the data app bean extract from the the main data set which is about date per millions of the population and the median age the dates set of the population have been calculated in the excel file then an extra row has been created. After that and appropriate visualization of the data app been shown using the scatter plot graph in the r studio using r studio code full stop after that the correlation and the regression analysis has been done on the median age variable and on the variables of deaths per million of the population in r studio using the codes of r studio (Alladio et al. 2021). By the regression analysis the residual have been found 7 times then the coefficients have been found after that the method has been discovered using the attribute code in r studio.

For the last analysis of the data of covid-19 the analysis has been done on the data set in which the number of the vaccination per million of the population has been calculated in the data set. By doing the analysis in the studio the numeric value of vaccination for each million of the population has been done in which the median value is 0.1 64 1475 for the new vaccination on par million population, the median value of new vaccination on per million population is 0.1 8919 24 and the standard deviation for new vaccination on 5 million population is is 0.2 550 405. There is also the histogram that has been shown on the new vaccination of per million population.

Conclusion

In the conclusion it can be concluded that each and every part of the analysis has been done using the analysis of R studio, and the output and the output graph have been shown using the R studio Code.

References

Journals

Alladio, E., Visintin, L., Lombardo, T., Testi, R., Salomone, A. and Vincenti, M., 2021. The impact of COVID-19 pandemic and lockdown on alcohol consumption: a perspective from hair analysis. Frontiers in psychiatry, 12, p.387.

Babi?, ., Turk, R. and Macan, J., 2020. Toxicological aspects of increased use of surface and hand disinfectants in Croatia during the COVID-19 pandemic: a preliminary report. Archives of Industrial Hygiene and Toxicology, 71(3), pp.261-264.

Cost, K.T., Crosbie, J., Anagnostou, E., Birken, C.S., Charach, A., Monga, S., Kelley, E., Nicolson, R., Maguire, J.L., Burton, C.L. and Schachar, R.J., 2021. Mostly worse, occasionally better: impact of COVID-19 pandemic on the mental health of Canadian children and adolescents. European child & adolescent psychiatry, pp.1-14.

da Silva Neto, R.M., Benjamim, C.J.R., de Medeiros Carvalho, P.M. and Neto, M.L.R., 2021. Psychological effects caused by the COVID-19 pandemic in health professionals: a systematic review with meta-analysis. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 104, p.110062.

Forte, G., Favieri, F., Tambelli, R. and Casagrande, M., 2020. COVID-19 pandemic in the Italian population: validation of a post-traumatic stress disorder questionnaire and prevalence of PTSD symptomatology. International journal of environmental research and public health, 17(11), p.4151.

Gobbi, S., P?omecka, M.B., Ashraf, Z., Radzi?ski, P., Neckels, R., Lazzeri, S., Dedi?, A., Bakalovi?, A., Hrusti?, L., Skórko, B. and Almazidou, K., 2020. Worsening of preexisting psychiatric conditions during the COVID-19 pandemic. Frontiers in psychiatry, 11, p.1407.

Holingue, C., Kalb, L.G., Riehm, K.E., Bennett, D., Kapteyn, A., Veldhuis, C.B., Johnson, R.M., Fallin, M.D., Kreuter, F., Stuart, E.A. and Thrul, J., 2020. Mental distress in the United States at the beginning of the COVID-19 pandemic. American journal of public health, 110(11), pp.1628-1634.

Hudaefi, F.A., Caraka, R.E. and Wahid, H., 2021. Zakat administration in times of COVID-19 pandemic in Indonesia: a knowledge discovery via text mining. International Journal of Islamic and Middle Eastern Finance and Management.

Khan, I., Haleem, A. and Javaid, M., 2020. Analysing COVID-19 pandemic through cases, deaths, and recoveries. Journal of Oral Biology and Craniofacial Research, 10(4), pp.450-469.

Martorell-Marugán, J., Villatoro-García, J.A., García-Moreno, A., López-Domínguez, R., Requena, F., Merelo, J.J., Lacasaña, M., de Dios Luna, J., Díaz-Mochón, J.J., Lorente, J.A. and Carmona-Sáez, P., 2021. DatAC: A visual analytics platform to explore climate and air quality indicators associated with the COVID-19 pandemic in Spain. Science of the Total Environment, 750, p.141424.

Radanliev, P., De Roure, D. and Walton, R., 2020. Data mining and analysis of scientific research data records on Covid-19 mortality, immunity, and vaccine development-In the first wave of the Covid-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(5), pp.1121-1132.

Utsunomiya, Y.T., Utsunomiya, A.T.H., Torrecilha, R.B.P., Paulan, S.D.C., Milanesi, M. and Garcia, J.F., 2020. Growth rate and acceleration analysis of the COVID-19 pandemic reveals the effect of public health measures in real time. Frontiers in Medicine, 7, p.247.

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