“We have a chance to do something extraordinary. As we head out of this pandemic we can change the world. Create a world of love. A world where we are kind to each other. A world were we are kind no matter what class, race, sexual orientation, what religion or lack of or what job we have. A world we don’t judge those at the food bank because that may be us if things were just slightly different. Let love and kindness be our roadmap.”
― Johnny Corn (https://www.goodreads.com/quotes/tag/pandemic)



INTRODUCTION


Since coronavirus (Covid-19) outbreak, it has affected 212 countries and territories around the world. In the United States of America, cases started to soar at an alarming rate since Covid-19 hit the country in mid-March. The U.S. has more confirmed cases and deaths than any other country worldwide, and all 50 states have been affected. In Califonia, the cumulative infections have risen too.

Orange County is a region in Southern California. It’s known for Anaheim’s Disneyland Resort, and cities with surf beaches include Huntington Beach, Newport Beach and Laguna Beach. Anti stay-home protests is growing in Orange County since Govnor Gavin Newsom issued the stay-home orders on March 19, despite the continued spread of the coronavirus. The control of coronavirus in Orange County has continued fluctuating due to the special population structure and geographical environment. It also brings moral challenges. There are a lot of interesting facts which have raised concerns related to virus spreading and controlling in subgroups. Because of the complexity, it is crucial to analyze in depth in subgroups to develop targeted programs to delay, mitigate and control the virus.

As of May 16, 2020, the virus has killed 87 people out of 4396 confirmed cases in Orange County since the pandemic began. The ICU Occuppied rate has exceeded red zone, comes to 92%. 5.7% people who are confirmed have to go to hospital to ask help and 2% covid19 patients died.


KEY FINDINGS


  1. The “Stay-at-Home” strategy works when Orange County fights with COVID-19.

  2. The increasing rate of infection is slower, but still not pass the inflection point.

  3. Men are more at risk than women for worse outcomes and death, no matter infection rate or death rate, independent of age.

  4. The most risk age for infection is 24-34 years old. The most severe cases and the highest death rates are among the elderly who are 65+.

  5. Latino community is the mostly impacted, and White community is the second. However, Blacks and Native American are suffering higher death rate than other community members.

  6. Ethics issues need to be conerned.


DATA ANALYSIS


All the data sets are web scraped from Orange County Open Data portal and local government website.

Trend Analysis I: the Effectiveness of Current Policy

People are arguing that no testing, no confirming. Testing more, confirming more. How can we know that the lock-down and social distancing policy work? The following analysis focuses on the effectiveness of current policy.

  • Hypothesis I

The increasing of confirmed cases is due to the increasing of testing.

  • Testing Method

The Figure 1 compares the culmulative confirmed cases of Covid 19(bar plot) in Orange County to culmulative tested cases(line plot). Please notice: in order to conveniently comparing to each other, the number of culmulative tested cases is in one-ten of real cases.

  • Analysis

Both plots keep increasing upward. Before the mid of April, the trends are parallel. However, after that, the gap between two is increasing.

  • Conclusion

It clearly shows the total confirmed cases aren’t increasing along with testing. It indicates that the strategy of Orange County works. Therefore, we reject the null hypothesis.



Trend Analysis II: the Indicator of the Future

How can we know that the virus is under controll? The rate of increasing new cases is a reference. However, it is not enough. Another indicator: the accelerated rate is more appropriate.

  • Indicator: Accelerated rate

The accelerated rate is the increasing rate of new cases.

  • How does the indicator work?

The accelerated rate is fluctuating around zero. If the accelerated rate is positive, it means the new cases are increasing. On the contrary, if the accelerated rate is negative, or there is a continous decreasing trend, then the situation is getting better and better.

  • How to use it?

The Figure2 shows a increasing trend for the new cases, and the accelerated cases fluctuate a lot. When the accelerated line below 0, the virus will be under control.



Gender Analysis

Disease outbreaks affect women and men differently, and pandemics make existing inequalities by gender.

According to Figure 3, on one hand men are easier to be infected by the virus than women. On another hand, if men and women have the same prevalence, men with COVID-19 are more at risk for worse outcomes and death, independent of age.


Age Analysis

The age structure of a county’s population has huge implications for predicting the threshold at which health care capacity is particularly overwhelmed.

Interestingly, the confirm rate in different age group is different from the death rate. The data show that the most risk age for infection is 24-34, and 50% infection cohert is between age 24-54. It makes sense since younger adults encounter many others at work and school. From Figure 3, there exist a empirical evidence: while it is possible that the elderly have higher possibility to be infected than younger people, such as by living in nursing homes, however, younger adults encounter many others infection risk way more than the elderly.

With the risk for serious disease and death from Covid-19 rising with age. Still, the most severe cases and the highest death rates are among the elderly. 70% of death cases were in age group 65+. In contrast, no deaths were reported among people younger than 24.

Another fact that should be drawn more attention is, although no babies died, but the Covid-19 cases in infants can be severe or critical. It may cause respiratory syncytial issue, which is known to cause severe illness in children.


Figure4: Confirmed Rate vs Death Rate By Ages


Race and Ethnicity Analysis

The coronavirus is having an increasingly disproportionate impact on races in Orange County. Coming to comfirmed cases, Latino community is the mostly impacted, and White community is the second. The proportion of both is 76% out of total confirmed cases. Interestingly, in Figure 5, data show that Latinos and Whites have been less affected by COVID-19 relative to their population size than other races. However, after adjusted for population proportion, Blacks and Native American are suffering higher death rate than other communities.



CASES IN CITES


There are 39 cities in Orange County. The city mostly impacted by COVID-19 is Santa Ana, and the second is Anaheim. Huntington Beach is at the third position. Those three cities account for 54% of the whole county.


MAP


The map displays the locations of all 39 cities in Orange county and total confirmed cases in each city.

How to use it

  1. Every dot represents a location of a city in Orange County, CA.
  2. The color of dots change from blue to red. The value range: 0-700.
  3. The size of circle indicates the total number of confirmed cases.
  4. Click “+” or “-” to zoom in or zoom out the map.
  5. Click every single dot, a message box will be popped out to show the city name and the number of conformed cases in each city.

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DASHBOARD


The dashboard combines the critical daily updated COVID-19 data of Orange County together, provides a convenient way to summarize data people really want to know.


1.Menu

  • visualization.
  • Map. Mark the location and total confirmed cases in each city.
  • CasesInCity. Bar plot for total confirmed cases in each city
  • Data table. Can be filter by county.
  • Resources
  • About *Social share link
  • Source code for dashboard.


  1. Visualization Indicators
  • Days Shelter in Place: total days of shelter-in-place since May 19, 2020.
  • Confirmed New: newest daily data of confirmed cases
  • Confirmed | Death(Total): cumulative confirmed cases vs cumulative death
  • ICU Occupied Rate: include covid10 patients and non-covid19 patients. If the ICU occupied rate less than 70%, the color of arc is green;if the ICU occupied rate greater than 70% and less than 90%,the color of arc turn orange; if the ICU occupied rate greater than 90% , the clor of arc turn red.
  • In ICU Currently: how many people are in ICU currently
  • Hospitalized Rate: covid 19 patients have to stay in hospital/total confirmed.
  • Fatality Rate: death number of covid 19 patients in hospital/total confirmed.


  1. Analysis Chart
  • Culmulative cases bar chart
  • Trend of new cases and accelerated cases
  • Race and Ethnicicy. Proportion of culmulative confirmed cases in different races.
  • Death Rate VS Population Rate By Race


(https://rpubs.com/Carol2008/covid19Dashboard)


ETHICS ISSUES


Orange County is always proud of its diversity and tolerant to different culture and different communities. However, there is growing evidence showing that increases in racist rhetoric have coincided with increases in racist attacks.

Since mid of February, Asians in Orange County have been subjected to bullying, threats, racist abuse, and discrimination that appear related to the pandemic. Those disturbing evidence invokes deep fear among Asian community. Because the history of the injustices like the internment of 120,000 Japanese Americans during WWII, or the Los Angeles riots which invoked a series of civil disturbances related to Korean American that occurred in Los Angeles County in April and May 1992, Asian community Asians fall into deep fear and anxiety. They tried self-defend. Some people even called to form orgnization similar to militia to protect themselves. Those dangerous action was called halt by local policy department.

All leaders should speak with a clear, strong, unified voice whenever hate occur in the communities and find common ground, build empathy, and promote respect. Similarly, when some leaders attempt to identify this disease with Asian neighbors and cast blame upon them for this crisis – it is our responsibility to reject these ideas and those who promote them. Government should pass laws to increase penalties for people engaging in violent or threatening behavior based on race, gender, religion, age, disabilities or sexual orientation.

The COVID-19 virus does not discriminate. It does not differentiate based on race, ethnicity or nationality. It attacks everyone. To combat an attack on all of us, we must remain united. Any societal divisions we inflict upon our community is a detriment to our safety and strength in times of crisis. Adversity must not divide us, especially now.


FURTH EXPLORATION


There is still a lot to explore.

  1. The relationship between beach openning and virus spreading.
  2. Combine and break down further of age and race data, to explore the impact of COVID 19 on different age range in different race.
  3. The data about the COVID 19 impact on Children should be collected and analyzed more to reveal the reason of the severe illness among children.
  4. Collect and analyze the the impact on poor people and homeless fellows.
  5. Explore the relationship between insurance coverage and fatality rate between different groups.


DATA ACQUISITION, CLEANING, AND WRANGLING


  • In the procedure of data collecting, cleaning, processing, analizing, the software which is mainly used is R.

  • All the data sets are web scraped from Orange County Open Data portal and local government website.

  • The range of data above is from April 1st to May 15, 2020. The point data are based on May 15, 2020 data.

Data Source

All the data sets are web scraped from Orange County Open Data portal and local government website.

Data Set From Link
Covid19 in Counties,CA The California Health and Human Services Agency (CHHS) https://data.chhs.ca.gov/dataset/6882c390-b2d7-4b9a-aefa-2068cee63e47/resource/6cd8d424-dfaa-4bdd-9410-a3d656e1176e/download/covid19data.csv
Covid19 in Age,Orange OCgov.com https://occovid19.ochealthinfo.com/coronavirus-in-oc
Covid19 in Cities,Orange OCgov.com https://occovid19.ochealthinfo.com/coronavirus-in-oc
Covid19 Race and Ethnicity Data California Department of Health https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/COVID-19/Race-Ethnicity.aspx#

Data Cleaning

  1. change column names
    relpace column names which contains space inside to dash.

  2. change data type
    change factor to numeric.

  3. remove delimiter
    remove “,” in numeric values.

Data Wrangling

  1. automatically update data
    everytime the program runs, the data will be updated to current date automatically.

  2. subset data
    filter Orange County data from data set which contains all counties information.

  3. data transformation
    shife down one line to get the new cases increasing (difference from latest day to last latest day)

  4. build function
    create a function to replacec comma in numeric value.

  5. combine data
    because come same race of “other” and "Native to 2 lists

Analysis Method

  1. web scraping
    libraries I have used: XML, RCurl, rlist.

  2. Analyzing tools
    sofeware:R.
    libraries: flexdashboard, knitr, DT, rpivotTable, ggplot2, plotly, dplyr, openintro, highcharter, ggvis, data.table, widgetframe, leaflet, grid, tidyverse, ggmap, leaflet.extras, htmltools, maps, mapproj, mapdata, XML, RCurl, rlist.

Layout

  1. dashboard
    flexdashboard

  2. layout
    HTML, CSS, R Markdown


CONCLUSION


“According to historians, pandemics typically have two types of endings: the medical, which occurs when the incidence and death rates plummet, and the social, when the epidemic of fear about the disease wanes.”(How Pandemics End, by Gina Kolata, Newyork Times)

Orange county is not hit by the pandemic the most. Although the spreading rate is slower, the infection may be spike because of beach openning and policy loosen.

It exposes a lot of problems during the pandemic. The reason behind it should be noticed and analysis further. It includes the following.

  1. Gender discrimination: men are more vulnerable than women

  2. Race discrimination: Latinos are infected more and Blacks are dead more

  3. Age discrimination: elders are impacted the most

  4. Insurance discrimination: poor people and homeless people should be concerned and take care of

Besides, governments should take urgent steps to prevent racist and xenophobic violence and discrimination linked to the Covid-19 pandemic while prosecuting racial attacks against minorities.




        CP101 
        
        Instructor: Karen Chapple
        
        GSI: Manuel Santana Palacios
       
        Created by: Huiqing Fu 
        
        Spring 2020
        
        05/16/2020
        
        UC Berkeley