Part I: Plotting Outbreak Data Now that you know where the outbreaks are located, your organization wants to chart out the areas that pose the highest exposure risk. Create a graph or chart usin
Analysis of Data
Shawn Currier
University of Phoenix
January 20, 2025
Part I: Cases by City
Top 5 Cities for Infected Cases:
Jacksonville - 322 cases
Miami - 299 cases
Phoenix - 289 cases
Austin - 281 cases
Houston - 272 cases
Prevalence Rate per 100,000 People:
To determine the prevalence rate per 100,000 people, we need the population of each city.
Prevalence Rate= (Population/Number of Cases) ×100,000
Observations and Deductions:
Geographical Distribution of Cases: It shows that the cases spread cross widely the geographical areas such as Jacksonville, Miami and Phoenix which had higher cases. This sort of geographical distribution could mean that the virus is growing rapidly in some places, perhaps owing to population density, measures to curb its growth, or particular events. It is important for health authorities to dissect the numbers, make use of such numbers to take necessary actions and program accordingly so as to fight such diseases.
Urban Centers as Hotspots: Majority of the identified cities are large cities, and this shows that crowded places are more vulnerable to the infection. Density is mainly manifested in large and congested human traffic areas in the urban centers making the society more prone to contagious diseases (Akrofi, 2024). This observation raises the issue of effectiveness in containing and preventing other outbreaks in cities that have strong public health systems.
Resource Allocation Needs: The cities like Jacksonville with 322 cases, Miami with 299 cases and Phoenix with 289 cases are likely to need immediate distribution of healthcare resources. These are; Hospital beds, Medical staff, testing kits and personal protective equipment (PPE). Proper allocation also means that the healthcare systems are not congested or congesting the health of the population and patients get their fair share of good health.
Prevalence Rate Analysis: While the absolute number of cases is useful, getting the prevalence rate per 100,000 people gives a better view of the virus’s severity in terms of numbers of population. States with a higher incidence rate may require strict measures and public health promotion campaigns to prevent the virus transmission. This analysis also enables us to make comparison of cities with different population on the same platform.
Temporal Analysis: It contains information about the days when the cases were reported and this is extremely useful when coming up with the timeline of the disease. On this basis, health officials are also able to define trends such as whether incidence is rising, waning or steady. This temporal analysis helps to assess the efficiency of the later public health interventions and build prognosis about further tendencies.
Implications for Public Health Policies: The actual data can help to improve the measures at the state level that people should take, for example, lockdowns, distance or travel limitations in high-risk zones. Cities with rising case numbers may require stricter measures to curb the spread, while those with lower or decreasing cases might focus on maintaining current measures to prevent resurgence (Hanson-DeFusco, 2023).
Potential Socioeconomic Factors: Susceptibility levels could also be attributed to high case numbers in some cities with regards to healthcare, public health and the socioeconomic status. Knowledge of these factors will assist in defining programs that will work towards solving issues that lead to the spread of the virus.
Community Outreach and Education: Communication is thus considered or recognized as being very important in containing the outbreak. Local authorities of cities and regions that are most affected by the virus should organize massive sensitization campaigns to ensure citizens know how to protect themselves, what signs to look out for and which health facilities to seek in case of an infection. There are massive possibilities how public health campaigns can contribute to lowering the rates of transmission through such measures, as wearing masks, washing hands, or getting vaccinated.
Future Preparedness: The data also highlights the need for future orientation. Governments should work out comparisons of their capacity to make choices for future precautionary wants to the present outbreak , and each city involved ought to review the reaction plans and healthcare system if and where they are having high number of confirmed cases. Investing in healthcare infrastructure, data collection, and early warning systems can enhance response capabilities.
Part II: Ages Impacted
Which Age Groups Are Most Affected?
The vulnerable age groups with respect to the number of virus outbreaks in 2017 are below 18 and above 61 years old. This is evident in some state capitals such as Houston, Phoenix and Austin where the <18 group has relatively high figure that may mean that the young people are most affected. Similarly, the 61+ age group is notable in the list, cities like Jacksonville and Houston having a higher number of cases. This trend shows that both young and old people’s groups are at higher risk and this may be perceived from the aspect of immune deprived status and frequent interaction in schools and care settings hence a focus on these groups will be appropriate in health promotion and disease prevention.
Which Age Groups Are Least Affected?
Two of the least vulnerable groups are the burgeoning adults of the 19-30 age bracket and the middle aged of the 31-60 bracket. It is evident that in the majority of studied cities these demographics provide lower case numbers compared to the <18 and 61+ groups. For instance, in the New York and Los Angeles cities the percentage of cases in this age group 19-30 is less. Likewise, the age group that is 31- 60 years has been affected compared to the age group of 19- 30; however, the former has received fewer cases than the latter most especially in the Old Age bracket. This lower prevalence can be described by the better immune status and contact with the contexts that have a dramatic effect on <18 and 61+ populations, including schools and eldercare institutions, respectively.
Prevalence Rate per Age Demographic
To calculate the prevalence rate per age demographic, the total number of cases in each age group can be divided by the sum of all cases, then multiplied by 100 to express it as a percentage. To determine the prevalence rate, the total population for each age group is needed. This data is currently unavailable.
<18: This group usually has a high prevalence rate especially because many of the cases are recorded in areas such as Phoenix and Austin. It is sad that they constitute a good number of the overall cases in many cities today.
19-30: This group of age in general tends to record a lower prevalence rate than other age category implying less effect of the outbreaks.
31-60: It is not as prevalent a group as the <18 or 61+ group but is again it does differ from area to area.
61+: This age group also mostly have a high prevalence rate as the <18 group; thus, they are among the vulnerable groups to the virus.
Deductions from the Chart
The bar graph arrangement side by side help to key some of the trends on how various age brackets are affected by the virus. Most affected are the young under 18 years and the elderly over 61 years could be due to; they have underdeveloped or compromised immune systems and because they attend school or are in care homes.
Regional variations are observed as well as the cities such as Houston and Jacksonville indicate the higher number of CO VID cases for the 61+ age group. This could be as a result of variations in the availability of health facilities and, measures exercised by the local authorities in the time of the outbreak, or, proportionately higher number of elderly people in these areas.
These findings have significant policy implications. For the policy maker it can be very useful to find out that certain age groups and regions are more affected than others so that funding and approach can be guided accordingly. For example, measures such as vaccinations, testing, health care support can be targeted at the most vulnerable groups of the population in the vulnerable areas hence a synchrony of checking and treatment strategies.
References
Akrofi, M. M. (2024). Green hotspots? Unveiling global hotspots and shifting trends in carbon credit projects. Sustainable Development.
Hanson-DeFusco, J. (2023). What data counts in policymaking and programming evaluation–Relevant data sources for triangulation according to main epistemologies and philosophies within social science. Evaluation and Program Planning, 97, 102238.
