Informatics Data Analysis PowerPoint Presentation with Speaker Notes

 

Assignment Content

1. Compare the charts and graphs from the attached reports I, II, and III and come to a consensus of the analysis of the data.

   Select a health care facility like a hospital
   Create a 10- to 15-minute Microsoft PowerPoint Presentation.

   Presentation should have 7 to 9 slides outlining how your selected health care facility or service can benefit from the information you gathered and analyzed in the attached reports I, II and III
   Must provide speaker notes for voice over presentation.
   Be sure to include any conflicts of interest, ethical considerations, or community health effects that may factor into the benefits identified.

   Cite 3 reputable references to support your assignment (e.g., trade or industry publications, government or agency websites, scholarly works, or other sources of similar quality). Slides, citations, speaker notes, and references must follow APA format
    

Running head:

VIRAL INFECTION ANALYSIS REPORT

1

VIRAL INFECTION ANALYSIS REPORT 4

VIRAL INFECTION ANALYSIS REPORT 3

VIRAL INFECTION ANALYSIS REPORT

Jason Parkhill

University of Phoenix

MHA/507

3/15/2021

Rea Burleson

According to the analysis done, the dangerous various has infected people in 50 cities between 2nd April 2017 and 28th April 2017. The top five cities with the highest number of infections are Jacksonville, Miami, Phoenix, Austin, and Houston. The number of cases per city is as follows. Jacksonville 322 cases, Miami 299 cases, Phoenix 289, Austin 281, and Houston 272. Jacksonville is the leading city, while the least affected cities are Omaha with three instances, Virginia Beach with 4 cases, Colorado and Philadelphia with five cases each, and Indianapolis with 7 cases.

The prevalence rate per 100000 people is 0.0485, which is the same as 4.85%. This is because the total number of those who have been infected with the dangerous virus is 4852. Averagely, 97 people in the 50 cities taken for the study have been infected by the virus. From the bar graph, it can also be seen that the virus is quickly spreading in other cities as compared to the rest. This can be connected to the containment measures that individual city has put in place. For example, in a county such as Omaha, only three people have been infected, while in a country such as Jacksonville, a total of 322 people have been infected by the disease.

There is a need to do another research study to understand why the disease is quickly spreading in other countries too fast as compared to the rest. They can be some hidden reasons which cannot be easily unearthed unless severe research is conducted in those cities. On matters concerning resource distribution, the top five cities should be given priority to tame the virus’s spread. Before resource distribution, it’s essential to understand the situation at the ground: the various prevention and curative measures that each city has put in place.

What can be deduced from the chart evaluation is that for the case of a dangerous virus which quickly spread, prevention or containment measures are better than curing the virus (Davies, 2008). That is why some counties, such as Omaha, that have put preventive measures in place have fewer infections while big cities such as Jacksonville are struggling with the high number of infections.

The bar graph showing the number of cases per city is shown below.

Reference

Davies, S. E. (2008). Securitizing infectious disease. International Affairs, 84(2), 295-313.

City vs Cases

Cases New York Los Angeles Chicago Houston Philadelphia Phoenix San Antonio San Diego Dallas San Jose Austin Jacksonville San Francisco Indianapolis Columbus Fort Worth Charlotte Seattle Denver El Paso Detroit Washington D.C. Boston Memphis Nashville Portland Oklahoma City Las Vegas Baltimore Louisville Milwaukee Albuquerque Tucson Fresno Sacramento Kansas City Long Bea ch Mesa Atlanta Colorado Springs Virginia Beach Raleigh Omaha Miami Oakland Minneapolis Tulsa Wichita New Orleans Arlington 189 201 14 272 5 289 95 258 83 109 281 322 76 7 14 98 19 65 30 29 35 61 28 31 23 18 11 146 26 17 12 51 114 187 51 38 215 231 220 5 4 8 3 299 84 9 127 83 248 11

Cities

Cases

Running head: DATA ANALYSIS REPORT II 1

DATA ANALYSIS REPORT II 2

DATA ANALYSIS REPORT II 3

Data Analysis Report II

Jason Parkhill

University of Phoenix

MHA/507: Leveraging Informatics in the Health Sector

Rea Burleson

March 22, 2021

According to the analysis done, some ages are susceptible to the dangerous virus while other ages are not. Even in counties where the number of cases is high, like Miami, only a certain age is highly infected. It is clear from the side-side bar graph which has been generated that the age groups which are highly affected are young and the elderly. It can be seen that in all 50 cities, it is either the elderly, those who are above 60 years or the children, those who are below 18 years are highly infected. However, it is also important to note that children below 18 years are susceptible to the virus more than the elderly above 60. To mention the figures, in a city like Miami, the total number of those infected is 299, the number of children affected is 170, while that of the elderly persons affected is 81. In another city, Jacksonville, the total number of people infected is 322, the number of children infected is 93, while those of the elderly persons is 149.

The age groups which seem somehow resistant to the dangerous virus are those aging between 19-30 years and 31- years. It is worth noting that in all 50 cities, the number of youths aging between 19-30 infected by the virus is less than 20 per city. In some cities Indianapolis and Philadelphia, there is no youth aging between 19-30 years is infected with the virus. In Omaha, no one aging between 19 and 60 has been infected with the virus.

This scenario can be connected to the immune system of the human being. The elderly generally have a weak immune system making them easily susceptible to being infected with any disease (Simon et al., 2015). The children also, their immune system is yet to be firm enough to fight some of the diseases. Those who are between 19-30 years have the strongest immune system; that is why they are not susceptible to the virus; however dangerous it may be. Those aging between 31-60 have a relatively strong immune system but not like those aging between 19-30 years.

The prevalence per age demographic is as follows:

a. <18 …….0.43 =43%

b. 19-30……0.07=7%

c. 31-60……0.17=17%

d. 61+……0.36=36%

After evaluating the data, it can be deduced that the elderly and the children are susceptible to diseases due to their weak immune system; hence, they should be given priority whenever a vaccination is given out for any disease. The organization should also consider directing its resources to those aging below 18 years and those aging above 60.

Side by side bar graph

References

Simon, A. K., Hollander, G. A., & McMichael, A. (2015). Evolution of the immune system in humans from infancy to old age. Proceedings of the Royal Society B: Biological Sciences, 282(1821), 20143085.

CASES PER AGE GROUP PER CITY

< 18 New York Los Angeles Chicago Houston Philadelphia Phoenix San Antonio San Diego Dallas San Jose Austin Jacksonville San Francisco Indianapolis Columbus Fort Worth Charlotte Seattle Denver El Paso Detroit Washington D.C. Boston Memphis Nashville Portland Oklahoma City Las Vegas Baltimore Louisville Milwaukee Albuquerque Tucson Fresno Sacramento Kansas City Long Beach Mesa Atlanta Colorado Springs Virginia Beach Raleigh Omaha Miami Oakland Minneapolis Tulsa Wichita New Orleans Arlington 68 64 4 98 2 145 46 114 42 49 151 93 27 3 7 47 11 36 16 14 10 22 10 16 11 8 6 59 15 5 4 19 51 93 22 19 76 129 63 2 1 4 1 170 42 6 71 24 89 4 19-30 New York Los Angeles Chicago Houston Philadelphia Phoenix San Antonio San Diego Dallas San Jose Austin Jacksonville San Francisco Indianapolis Columbus Fort Worth Charlotte Seattle Denver El Paso Detroit Washington D.C. Boston Memphis Nashville Portland Oklahoma City Las Vegas Baltimore Louisville Milwaukee Albuquerque Tucson Fresno Sacramento Kansas City Long Beach Mesa Atlanta Colorado Springs Virginia Beach Raleigh Omaha Miami Oakland Minneapolis Tulsa Wichita New Orleans Arlington 15 16 1 16 0 20 6 15 8 9 12 19 5 0 1 7 1 7 2 2 2 4 1 2 2 1 1 12 2 1 1 3 8 13 3 4 17 12 13 0 0 1 0 18 8 1 10 5 15 1 31-60 New York Los Angeles Chicago Houston Philadelphia Phoenix San Antonio San Diego Dallas San Jose Austin Jacksonville San Francisco Indianapolis Columbus Fort Worth Charlotte Seattle Denver El Paso Detroit Washington D.C. Boston Memphis Nashville Portland Oklahoma City Las Vegas Baltimore Louisville Milwaukee Albuquerque Tucson Fresno Sacramento Kansas City Long Beach Mesa Atlanta Colorado Springs Virginia Beach Raleigh Omaha Miami Oakland Minneapolis Tulsa Wichita New Orleans Arlington 34 56 3 52 1 32 12 34 12 17 39 61 25 1 2 12 2 10 1 5 7 12 4 5 2 2 2 37 2 3 2 7 19 19 7 6 40 25 42 1 1 1 0 30 18 1 32 22 47 2 61+ New York Los Angeles Chicago Houston Philadelphia Phoenix San Antonio San Diego Dallas San Jose Austin Jacksonville San Francisco Indianapolis Columbus Fort Worth Charlotte Seattle Denver El Paso Detroit Washington D.C. Boston Memphis Nashville Portland Oklahoma City Las Vegas Baltimore Louisville Milwaukee Albuquerque Tucson Fresno Sacramento Kansas City Long Beach Mesa Atlanta Colorado Springs Virginia Beach Raleigh Omaha Miami Oakland Minneapolis Tulsa Wichita New Orleans Arlington 72 65 6 106 2 92 31 95 21 31 79 149 19 3 4 32 5 12 11 8 16 23 13 12 8 7 2 41 7 8 5 22 36 62 9 9 82 65 102 2 2 2 2 81 16 3 118 32 97 4

7

HIGH RISK AREAS WORD REPORT ASSIGNMENT

Jason Parkhill

University of Phoenix

MHA/507

3/29/2021

Rea Burleson

Figure showing the Smallest to Largest values Custom sort settings in Excel Worksheet.

Figure showing the Pivot Chart Fields for the Excel Data.

Figure showing the Pivot Chart illustration for February Cases.

Figure showing March cases in pictorial view using Pivot Chart.

Figure showing the February March and April cases using the Pivot Chart.

The Pivot Chart indicates that

.

Figure showing the sorting criteria for the Low infection rates.

The OR conditions selects the cities where the February cases are lower than 19, March Cases less than 78 and April Cases lower than 84. These values come from the descriptive statistical values given by the three months i.e., February, March and April.

Figure showing the sorting Criteria for the High Infection rates

The sorting criteria for the high values such that it uses the values above the mean 10 for February. Above 35 for March and above 95 cases for April. Note that these values originate from the descriptive statistics obtained through the Excel Data Analysis. For instance, the mean for February cases is 10.9411 with standard error of ±1 while that for March is 41±6 making 35 an ideal option for classifying the Highest cases. Results from these values ranks Jacksonville as the riskiest city in April, New Orleans in March and Austin in April.

City

Feb. Cases

March Cases

April Cases

New York

19

56

189

Los Angeles

6

12

201

Houston

19

19

272

San Diego

3

38

258

San Jose

10

23

109

Austin

28

59

281

Jacksonville

10

97

322

Fort Worth

8

26

98

Las Vegas

1

22

146

Tucson

2

9

114

Fresno

13

45

187

Long Beach

13

19

215

Mesa

2

35

231

Atlanta

11

35

220

Miami

21

72

299

Tulsa

8

43

127

New Orleans

12

101

248

Table showing the Cities with the most and least cases where the green color denotes the low rates and Red the high infection rates.

High Risk

Low Risk

Cities

Jacksonville
Austin
Miami
New Orleans
Mesa
Long Beach

Los Angeles
San Jose
Fort Worth
Las Vegas
Tucson
New York

The same technique illustrated above works with government agencies such as the Center of Disease control in determining the cases caused by variants that includes a U.S map with the various confirmed cases in the country. Additionally, in other specific statistical analysis like the cases by countries, deaths and currently the vaccinations for the COVID-19 (Center of Disease Control and Prevention, 2021). In current statistical analysis for instance in estimating the level of cumulative incident based on individual age groups, it uses the estimates and age groups information with the symptomatic illnesses and hospitalizations. This information goes further by expressing their individual place of residence which helps in disease control and management (Wu and McGoogan, 2020).

REFERENCES

Center of Disease Control and Prevention. (2021). Estimated Disease Burden of COVID-19. Accessed on 27th March 2021. Retrieved from

Estimated Disease Burden of COVID-19 | CDC

Wu, Z., & McGoogan, J. M. (2020). Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention. Jama, 323(13), 1239-1242.

APRIL CASES

Sum of April Cases Albuquerque Arlington Baltimore Boston Charlotte Chicago Colorado Springs Columbus Dallas Denver Detroit El Paso Indianapolis Kansas City Louisville Memphis Milwaukee Minneapolis Nashville Oakland Oklahoma City Omaha Philadelphia Portland Raleigh Sacramento San Francisco Seattle Virginia Beach Washington D.C. Wichita 51 11 26 28 19 14 5 14 83 30 35 29 7 38 17 31 12 9 23 84 11 3 5 18 8 51 76 65 4 61 83 Sum of March Cases Albuquerque Arlington Baltimore Boston Charlotte Chicago Colorado Springs Columbus Dallas Denver Detroit El Paso Indianapolis Kansas City Louisville Memphis Milwaukee Minneapolis Nashville Oakland Oklahoma City Omaha Philadelphia Portland Raleigh Sacramento San Francisco Seattle Virginia Beach Washington D.C. Wichita 15 2 8 2 2 3 0 5 13 11 14 4 2 13 15 7 3 3 4 15 1 0 1 6 2 9 13 10 0 18 12 Sum of Feb. Cases Albuquerque Arlington Baltimore Boston Charlotte Chicago Colorado Springs Columbus Dallas Denver Detroit El Paso Indianapolis Kansas City Louisville Memphis Milwaukee Minneapolis Nashville Oakland Oklahoma City Omaha Philadelphia Portland Raleigh Sacramento San Francisco Seattle Virginia Beach Washington D.C. Wichita 2 1 1 1 1 0 0 1 4 2 4 3 0 3 2 2 1 1 1 3 1 0 0 1 0 2 2 1 0 2 1

FEB CASES

Total Albuquerque Arlington Atlanta Austin Baltimore Boston Charlotte Chicago Colorado Springs Columbus Dallas Denver Detroit El Paso Fort Worth Fresno Houston Indianapolis Jacksonville Kansas City Las Vegas Long Beach Los Angeles Louisville Memphis Mesa Miami Milwaukee Minneapolis Nashville New Orleans New York Oakland Oklahoma City Omaha Philadelphia Phoenix Portland Raleigh Sacramento San Antonio San Diego San Francisco San Jose Seattle Tucson Tulsa Virginia Beach Washington D.C. Wichita 2 1 11 28 1 1 1 0 0 1 4 2 4 3 8 13 19 0 10 3 1 13 6 2 2 2 21 1 1 1 12 19 3 1 0 0 23 1 0 2 6 3 2 10 1 2 8 0 2 1

MARCH CASES

Total Albuquerque Arlington Atlanta Austin Baltimore Boston Charlotte Chicago Colorado Springs Columbus Dallas Denver Detroit El Paso Fort Worth Fresno Houston Indianapolis Jacksonville Kansas City Las Vegas Long Beach Los Angeles Louisville Memphis Mesa Miami Milwaukee Minneapolis Nashville New Orleans New York Oakland Oklahoma City Omaha Philadelphia Phoenix Portland Raleigh Sacramento San Antonio San Diego San Francisco San Jose Seattle Tucson Tulsa Virginia Beach Washington D.C. Wichita 15 2 35 59 8 2 2 3 0 5 13 11 14 4 26 45 19 2 97 13 22 19 12 15 7 35 72 3 3 4 101 56 15 1 0 1 78 6 2 9 9 38 13 23 10 9 43 0 18 12