Hypothesis
The first case of COVID-19 was reported in the United States on January 20, 2020. Now, as of May 6, 2020, there are 1,195,605 reported cases, and 65,307 deaths. Through this pandemic there have been mutiple precautionary steps taken by the state governments to slow the spread of COVID-19. One of these steps includes a mandatory “stay-at-home” order, which many states implemented. This is because COVID-19 is believed to spread mainly from person-to-person or through respiratory droplets produced when an infected person coughs, sneezes, or talks.
I hypothesize that this data will reveal a positive correlation between rates of COVID-19 cases and deaths per county, rates of mobility per county, and county population. The data being used was provided by the NYTimes, as well as the Google COVID-19 Community Mobility Reports. While there are many other factors to be taken into consideration (such as age, health status, and socio-economic status) this report will be focusing mainly on community mobility and total population of ten counties in North Carolina. These counties are Wake, Mecklenburg, Guilford, Durham, Forsyth, Cumberland, Lenoir, Columbus, Hyde, and Tyrrell. The counties were chosen because they highly populuous counties (Mecklenburg, Wake, Durham, Guilford, Cumberland, and Forsyth) as well as low populated counties (Hyde and Tyrrell). I have also included Lenoir and Columbus Counties because their population is mid-sized. North Carolina Demographics .
- In this study, the highly populated counties showed more dynamic changes in infection and death rates, so they will be the main focus of this report. The mid-small sized counties acted as the control group.
Maps 1 and 2
These two maps show the increase of COVID-19 deaths per county in the United States with a close up of North Carolina. As stated before, this data was recorded through January 21, 2020 to May 6, 2020. The data set used to make these maps was published by the NYTimes.
The counties are also color coded according to population size. In the country map, the darker area have higher populations while the lighter are less populated. For the North Carolina map the higher populations are color coded green, while the red areas are less populated.
North Carolina COVID-19 Cases and Deaths Per County (as of May 6, 2020)
Here is another visual to show the total number of COVID-19 cases and deaths per county in North Carolina. Wake, Mecklenburg, Durham, and Guilford county are among the top 5 counties with the most COVID-19 cases and deaths. Forsyth and Cumberland are shown to comparatively have a significantly lower number of cases and deaths despite having similar population sizes. I have also included Hyde County and Tyrrell county, which are the least populous counties, as well and Lenoir and Columbus which are the middle.
Mobility Data
This is the data provided by the Google Community Mobility Report. In this report the mobility in each county is analyzed based on visits to locations like “Retail & Recreation”, “Grocery & Pharmacy”, and “Parks”.
In North Carolina as a whole, there has been a 27% decrease in mobility to “Retail & Recreation”, a 1% decrease in mobility to “Grocery & Pharmacy”, and a 57% increase in mobility to “Parks”.
Wake County
Retail & Recreation: -42% compared to baseline
Grocery & Pharmacy: -11% compared to baseline
Parks: +61% compared to baseline
Mecklenburg County
Retail & Recreation: -43% compared to baseline
Grocery & Pharmacy: -11% compared to baseline
Parks: +33% compared to baseline
Durham County
Retail & Recreation: -42% compared to baseline
Grocery & Pharmacy: -12% compared to baseline
Parks: +20% compared to baseline
Guilford County
Retail & Recreation: -34% compared to baseline
Grocery & Pharmacy: -3% compared to baseline
Parks: +78% compared to baseline
Forsyth County
Retail & Recreation: -28% compared to baseline
Grocery & Pharmacy: -3% compared to baseline
Parks: +27% compared to baseline
Cumberland County
Retail & Recreation: -24% compared to baseline
Grocery & Pharmacy: +2% compared to baseline
Parks: +35% compared to baseline
Columbus County
Retail & Recreation: -1% compared to baseline
Grocery & Pharmacy: +19% compared to baseline
Parks: N/A
Lenoir County
Retail & Recreation: -7% compared to baseline
Grocery & Pharmacy: +16% compared to baseline
Parks: N/A
Hyde and Tyrrell County
These two counties were not present in the data provided by Google.
Stay At Home Orders for Each County
Mecklenburg County’s SAHO went into effect March 26, 2020
Wake County’s SAHO went into effect March 27, 2020
Guilford County’s SAHO went into effect March 25, 2020
Durham County’s SAHO went into effect April 4, 2020
Forsyth County’s SAHO went into effect March 27, 2020
Cumberland County’s SAHO went into effect March 30, 2020
Columbus, Lenoir, Tyrrell, and Hyde County SAHO went into effect on March 30 in accordance to the state order.
Analysis
After analyzing the population sizes of each county and their number of COVID-19 cases/deaths, the data showed a positive correlation between the population size and the amount of cases/deaths. The next step in analyzing this data would be to see if population density and the number of COVID-19 cases/deaths also positively correlate.
The data that shows the similarities in population size between the six most populated counties chosen suggests that there is probably another factor that influences the number of cases and deaths in a given county. This is where it could be beneficial to analyze the data provided by Google’s Community Mobility Report data.
For Wake and Guilford Counties there has been a major spike in community “Park” mobility, as according to the Google Mobility Report. This increase in mobility in these park locations, paired with the already high population size, could be a contributor to the high COVID-19 case and death rates that are shown in these counties.
In Forsyth and Cumberland county, there is also an increase in “Park” mobility. It is important to note that this increase is almost half the percentage of what Wake and Guilford Counties are experiencing. However, there is less of a decrease in mobility to “Retail & Recreation” and “Grocery & Pharmacy”. This could account for why there are still cases and deaths present in these counties, but also why these numbers are signifcantly lower than the other counties.
It may also be a possibility that the amount of cases and death in some of the counties, like Durham and Mecklenburg County, are being affected by surrounding counties with a higher case and death rate.
For instance, Durham County is adjacent to Wake County. Even though Durham County has lower mobility rates compared to Wake County, it may be affected by its more mobile neighbor.
The next step was to account for when these counties put into effect a Stay-At-Home order. The assumption here was that the counties who implemented a Stay-At-Home order sooner would have less cases and deaths.
When looking at the news reports that stated when a specific county was said to start the Stay-At-Home order, there initally seemed to be no correlation due to the fact that the orders were initiated at similar times. However, when you compare the dates that the orders were put out to the number of cases that were present in the county at the time, then it could be assumed that the Stay-At-Home orders had a significant impact on the growth of cases and deaths.
For instance, both Forsyth and Wake County started their Stay-At-Home orders on March 27, 2020. At the time, as shown by Map 2, Wake County had 121 cases while Forsyth only had 17.
Cumberland County also did not start their Stay-At-Home orders until March 30, 2020. At that time, the county only had 15 reported cases of COVID-19.
For the rest of the more populous counties, Mecklenburg had 181 cases on the day they implemented the Stay-At-Home order, Durham also had 181 cases, and Guilford had 22 cases.
For the county who were not as populated (Lenoir, Columbus, Hyde, and Tyrrell) they followed the state mandated Stay-At-Home on March 30, 2020. At that point, Hyde and Tyrrell had no cases/deaths, and Lenoir and Columbus Counties had 3 and 2 cases and no deaths.
Conclusion
The factor that seems to have had the most impact on the number of cases and deaths of COVID-19 in these counties are when the Stay-At-Home order were implemented. Those counties (Forsyth, Cumberland, Lenoir, Columbus, Hyde, and Tyrrell) who enacted these orders while their case/death count was low/nonexistent now have less cases and deaths compared to the other counties who waited.
In the case of Guilford County, their Stay-At-Home order was given when they still only had 22 cases in the county. This does not account for why they are now among the counties with the highest number of cases and deaths caused by COVID-19. They do, however, have a 78% increase in mobility around “Parks” and not as high as a decrease in mobility in regards to “Retail & Recreation” and “Grocery & Pharmacy”. These factors could be to blame for the high case/death count in the county.
Overall, it could be said that while mobility could have an effect on case and death count, the Stay-At-Home orders could be responsible for overall levels.
In future studies, it might be usefully to compare this data to population density, and demographics such as age and socio-economic status. From this data, it may narrow down why some counties are more affected than others, like metropolitan areas. Additionally, if there are higher number of 55+ year old citizens in a given population than that might account for a high death rate.
Socio-economic status could also play an important role. For area in which there are higher numbers of essential workers, such as postal workers, grocery store clerks, etc., they might be playing a role in the transmission of COVID-19 due to the fact that they come into the most contact with others.