GP.F: Univariate Graphs (10 points)
InstructionsIf you need a reminder about graphing one variable at a time in R, watch this video.
There are a variety of conventional ways to visualize data—tables, histograms, bar graphs, etc.
Now that your data have been managed, it is time to graph your variables one at a time and examine both center and spread. Include univariate graphs of your two main constructs.These main constructs, or variables, should be managed (from GP.D).
• Each graph should contain clearly labeled x-axes, y-axes, and legends, if applicable. Write a few sentences describing what your graphs reveal in terms of shape, spread, and center (if variable is quantitative) and most/least frequent categories if variable is categorical.
Submit your assignment as a PDF document on Canvas.
GP.F: Univariate Graphs (10 points) GP.F: Univariate Graphs (10 points) Instructions If you need a reminder about graphing one variable at a time in R, watch this video. There are a variety of conventional ways to visualize data—tables, histograms, bar graphs, etc. Now that your data have been managed, it is time to graph your variables one at a time and examine both center and spread. Include univariate graphs of your two main constructs. These main constructs, or variables, should be managed (from GP.D). • Each graph should contain clearly labeled x-axes, y-axes, and legends, if applicable. Write a few sentences describing what your graphs reveal in terms of shape, spread, and center (if variable is quantitative) and most/least frequent categories if variable is categorical. Submit your assignment as a PDF document on Canvas. 1 https://youtu.be/mwLyXHfCPdE Instructions Sabrina Maloy Lynda Maxfield Michelle Najarian Kaylee Anderson COMM 3710 GP. D 10/22/2020 2. Select the variables and possibly rows, of interest and run frequency distributions of your chosen variables. Symptom Variables: Fever, Chills, Runny Nose, Congestion, and Cough. PHYS1A, PHYS1B, PHYS1C, PHYS1D, PHYS1F total symptoms: SYM_TOT Other Variables: Income Level & Population Density INC_BANNER & P_DENSE 3a. The name of the program you used to run frequency distributions RStudio 3b. Output that displays three of your variables in frequency tables. - Report descriptive statistics for each variable (e.g., mean, median, standard deviation, proportions). Frequency of having 1-5 symptoms: NA represents (2) No Symptoms, (77) Not Sure, (98) Skipped on Web Summary of SYM_TOT Mean: 1.57 Median: 1 Frequency Tables: Symptom by Population Density & Income Income: Population: Proportions of Symptom Occurrences in Urban Populations By Income 3c. Describe the results of your frequency tables in a few sentences. Of the individuals sampled who experienced between 1-5 of the COVID symptoms we examined, 60.10% had only one symptom; those with two symptoms totaled 26.41%. The mean number of symptoms experienced was 1.57. Individuals in the $60K-$125K income group had the most symptoms: 31.25% of the total sample. Individuals living in urban areas made up 77.52% of the sample who experienced symptoms. GP.C: Literature Review Lynda Maxfield Michelle Najarian Sabrina Maloy Kaylee Anderson The relationship between income, population, and disease transmission with COVID-19, or novel Coronavirus. To begin learning how COVID-19 spread may relate to population density and income levels, we used keywords such as COVID AND “population density,” COVID AND income, and COVID AND “disease transmission.” Since COVID-19 is a new health crisis, we expected to have difficulty finding research, but scientists have been working hard to catch up. New studies, combined with existing research on earlier, global respiratory infections, offer promising information about a possible correlation of virus spread rates between densely populated and lower-income areas instead of rural or more affluent communities. These articles speak to research done on both current and former widespread respiratory-related infections, pandemic or otherwise. ● Ahmed, W., Angel, N., Edson, J., Bibby, K., … Mueller, J. (2020). First confirmed detection of SARS-CoV-2 in untreated wastewater in Australia: A proof of concept for the wastewater surveillance of COVID-19 in the community. Science of The Total Environment, 728, 1-8. doi.org/10.1016/j.scitotenv.2020.138764 ● Koh, W. C., Naing, L., & Wong, J. (2020). Estimating the impact of physical distancing measures in containing COVID-19: An empirical analysis. International Journal of Infectious Diseases, 100, 42-49. doi:10.1016/j.ijid.2020.08.026 ● Maroko, A., Nash, D., & Pavilonis, B. (2020). COVID-19 and inequity: A comparative spatial analysis of New York City and Chicago hot spots. J Urban Health 97, 461–470 (2020). doi-org.ezproxy.lib.utah.edu/10.1007/s11524-020-00468-0 ● Muhammad, A., Bin, X., Mohammed, G. (2020). Transmission of SARS-CoV-2 via fecal-oral and aerosols–borne routes: Environmental dynamics and implications for wastewater management in underprivileged societies. Science of the Total Environment, 743, 1-7. doi.org/10.1016/j.scitotenv.2020.140709 ● Pourghasemi, H. R., Pouyan, S., Heidari, B., Farajzadeh, Z., Fallah Shamsi, S. R., Babaei, S., . . . Sadeghian, F. (2020). Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020). International Journal of Infectious Diseases, 98, 90-108. doi:10.1016/j.ijid.2020.06.058 ● Shima H., Sadegh S. & Reid E. (2020). Does density aggravate the COVID-19 pandemic?, Journal of the American Planning Association, 86:4, 495-509, DOI:10.1080/01944363.2020.1777891 ● Williams, D. R., & Cooper, L. A. (2020). COVID-19 and health equity - A new kind of "herd immunity". JAMA - Journal of the American Medical Association, 323(24), 2478-2480. doi:10.1001/jama.2020.8051 ● Zhang, C.H. & Schwartz, G.G. (2020). Spatial disparities in coronavirus incidence and mortality in the United States: An ecological analysis as of May 2020. The Journal of Rural Health, 36: 433-445. doi:10.1111/jrh.12476 ● Dahab, M., Van Zandvoort, K., Flasche, S., Warsame, A., Ratnayake, R., Favas, C., . . . Checchi, F. (2020). COVID-19 control in low-income settings and displaced populations: What can realistically be done? Conflict and Health, 14(1), 1-6. doi: 10.1186/s13031-020-00296-8 ● Tran, B., Xuan, H., Giang H., Nguyen, L., Hoang, V., Giang Thu, H., Men Thi, L., Huong Thi, . . . Ho, Roger, C.M. (2020). Studies of novel coronavirus disease 19 (COVID-19) pandemic: A global analysis of literature. International Journal of Environmental Research and Public Health, 17(11), 4095. doi: 10.3390/ijerph17114095 Common patterns, interesting themes: ● Connectivity within populations is thought to be a more substantial factor for COVID-19 spread than is strictly the density. Though findings show that minority populations with low educational attainment “have significantly higher infection rates” (Shima et al., 2020, p. 504), density “is unrelated to confirmed virus infection rates” (p. 505). One study suggests that improved health outcomes are related to increased social distancing connected to higher incomes and education, communities with fewer minorities, and more manager-level workers (Maroko et al., 2020). ● Social distancing did in fact show fewer cases of COVID-19. Not only did this apply to the current pandemic of COVID-19, but it has also applied to past respiratory diseases like SARS-COV-2. ● Densely populated cities are considered hotspots of this pandemic, this means larger cities and their surrounding metropolitan areas are disproportionately affected to COVID-19. Additionally, there is a positive correlation with poverty and cases of COVID-19. ● Multiple articles suggest it is better to improve medical care for low income people to keep the survival rate up, this includes hospital care, oxygen, ventilators, and overall doctors experience. Low income/ high risk individuals are also advised to stay at home as much as possible and when they are out to wear a mask, the article suggests preventative measures. https://doi-org.ezproxy.lib.utah.edu/10.1111/jrh.12476 ● SARS-CoV-2 ribonucleic acid (RNA) is being detected in fecal matter from both symptomatic and asymptomatic patients; yet, the samples seem to indicate more prevalence in the wastewater “than the confirmed cases” (Ahmed et al., 2002, p. 6). Researchers suggest improving wastewater evaluation may improve prediction regarding community spread widely, but specifically “in remote communities and confined populations” (p. 7). Question we are curious about: ● Knowing that population density and poverty positively correlate with COVID-19 cases, what are steps that society or the government can take to prevent these disparities? ● If population density does not aggravate the conception of COVID-19, how does the early intervention of crisis flatten the curve? ● How accurate can reporting be in lower-income communities if they have less access to medical care and are less likely to be tested for the novel Coronavirus? ● Does income and population density correlate to symptoms felt by COVID-19 positive patients, either symptomatic or asymptomatic? ● Does educational attainment or poverty correlate to existing individual, family, organization education or understanding of COVID-19 and disease transmission? ● What is the difference in survival rate between low-income and high-income COVID- 19 patients? ● Do the experienced symptoms cluster in patterns across low and high income groups? Do the two groups share similar symptoms or do they differ by group? ● Can the high death rates in NYC be attributed to how their wastewater is managed (environmental factor)? How does the infection to death rate in other cities compare to the ratio of infection to death in NYC?