The California Department of Public Health maintains a current watch list of counties that are being monitored for worsening coronavirus trends. There are six criteria used to place counties on the watch list:
1. Doing fewer than 150 tests per 100,000 residents daily (over a 7-day average)
2. More than 100 new cases per 100,000 residents over the past 14 days…
3. 25 new cases per 100,000 residents and an 8% test positivity rate
4. 10% or greater increase in COVID-19 hospitalized patients over the past 3 days
5. Fewer than 20% of ICU beds available
6. Fewer than 25% ventilators available
Here, condition 2 will be examined with tables containing:
– total NEW cases in the 5 worst counties
– cumulative cases in the 5 worst counties
– number of unsafe counties
addednewcases <- coviddata %>% # filter data by state of interest
filter(state == stateofinterest) %>%
group_by(county, date) %>%
summarise(cases = sum(cases), fips) %>%
mutate(newCases = cases - lag(cases)) %>% # creates new column with daily new cases
ungroup()
mostnewcases <- addednewcases %>% # finds top 5 counties with most new cases per day
filter(date == max(date)) %>%
slice_max(newCases, n = 5) %>%
select(county, newCases)
mostcumulativecases <- addednewcases %>% # finds top 5 counties with most cumulative cases
filter(date == max(date)) %>%
slice_max(cases, n = 5) %>%
select(county, cases)
Los Angeles, San Bernardino, San Diego, Fresno and Orange County are the counties with the most new cases reported daily. Both Los Angeles and San Bernado reported over a thousand.
Most New Cases California Counties
| Los Angeles |
809 |
| San Diego |
265 |
| Orange |
185 |
| Fresno |
159 |
| San Bernardino |
156 |
Los Angeles, Riverside, Orange, San Bernardino, and San Diego are the counties with the most total reported cases, this could be due to be being the largest 5 counties in California. Los Angeles is the only reporting more than 50,000 cases currently.
Most Cumulative Cases California Counties
| Los Angeles |
253,985 |
| Riverside |
55,073 |
| Orange |
52,121 |
| San Bernardino |
50,699 |
| San Diego |
42,742 |
Per Capita Calculations
PopulationEstimates <- read_excel("~/github/geog-176A-labs/data/PopulationEstimates.xls", skip = 2)
# dim(PopulationEstimates) # examine dimensions of excel file
# head(PopulationEstimates,5)
populationestimates <- PopulationEstimates %>% # select only necessary columns
select(fips = "FIPStxt", state = "State", Area_Name, pop2019 = "POP_ESTIMATE_2019")
CovidandPopulation <- addednewcases %>%
inner_join(populationestimates, by = "fips" ) # join to previous data
mostnewcasespc <- CovidandPopulation %>% # finds top 5 counties with daily new cases per capita
filter(date == max(date)) %>%
group_by(county) %>%
mutate(casepercap = cases / pop2019) %>%
ungroup() %>%
slice_max(casepercap, n = 5) %>%
select(county, casepercap)
mostcumulativecasespc <-CovidandPopulation %>% # finds top 5 counties with cumulative cases per capita
filter(date == max(date)) %>%
group_by(county) %>%
mutate(newcasepercap = newCases / pop2019) %>%
ungroup() %>%
slice_max(newcasepercap, n = 5) %>%
select(county, newcasepercap)
Examining the same data with respect to population size, it can be seen that the 5 highest counties of new daily cases per capita are Imperial, Kings, Kern, Tulare and Merced County. While most total cases per capita are San Bernardino, Stanislaus, Kings, Fresno, and Marin County.
Most New Cases California Counties Per Capita
| Imperial |
0.0622134 |
| Kings |
0.0464038 |
| Kern |
0.0341423 |
| Tulare |
0.0324199 |
| Merced |
0.0307584 |
Most Cumulative Cases California Counties Per Capita
| Kings |
0.0002615 |
| San Benito |
0.0002388 |
| Monterey |
0.0002027 |
| Lake |
0.0001708 |
| Fresno |
0.0001591 |
numofdays <- 14
greaterthan100 <- CovidandPopulation %>%
filter(date > (max(date) - numofdays)) %>%
group_by(county, date) %>%
summarise(ncases = 100000 * (sum(newCases) / pop2019)) %>%
filter(ncases > 100)
2 counties have more than 100 cases per 100,000 residents in the past 14 days, that leaves 55 counties deemed as safe.
California Counties with > 100 cases within 2 weeks
| Imperial |
2020-09-04 |
125.8174 |
| Kings |
2020-09-04 |
171.3090 |
Examining impact of scale on COVID cases
statesofinterest <- c("New York", "California", "Louisiana","Florida")
coviddatastate <- coviddata %>%
filter(state %in% statesofinterest) %>%
group_by(state, date) %>%
summarise(cases = sum(cases)) %>%
# creates new column withe daily new cases
mutate(newCases = cases - lag(cases), roll7 = rollmean(newCases, 7, fill = NA, align="right")) %>%
ungroup()
coviddatastate %>%
ggplot(aes(x = date, y = newCases)) + geom_col(aes(y = newCases, col = state))+
geom_line(aes(col = state)) + geom_line(aes(y = roll7), size = 1) +
facet_wrap(~state, nrow = 2) + ggthemes::theme_economist_white()+ ggthemes::scale_color_few() +
theme(legend.position = "bottom") +
theme(legend.position = "NA") +
labs(title = "Daily new cases by State", y = "Daily New Count", x = "Date",
caption = "Lab 02")
coviddatastate %>%
inner_join(populationestimates, by = c("state" = "Area_Name") ) %>%
group_by(state, date) %>%
summarise(newcases = sum(newCases), pop2019, .groups = "drop") %>%
mutate(newcasepercap = (newcases / pop2019), roll7 = rollmean(newcasepercap, 7, fill = NA, align="right"))%>%
ggplot(aes(x = date, y = newcasepercap)) +
geom_col(aes(y = newcasepercap, col = state))+
geom_line(aes(col = state)) + geom_line(aes(y = roll7), size = 1)+
facet_wrap(~state, nrow = 2) + ggthemes::theme_economist_white() + ggthemes::scale_color_few() +
theme(legend.position = "none") +
labs(title = "Daily new cases Per Capita by State", y = "Daily New Count Per Capita", x = "Date",
caption = "Lab 02")
This graph was scaled by population, and it can be seen that Louisiana’s situation is more dire while California’s appears better. California has the largest population among these states and after scaling, it can be seen that they have had the smallest reported spike in daily cases. New York and Florida are close together in population and appear to have a similar sized spike in daily cases. Whereas before the scaling it appeared Florida had the largest spike, and California and New York’s was similar. Although New York’s was in April while Florida’s happened in July. Louisiana has the smallest population by far with roughly 4.6 million (New York, California and Florida have a population greater than 19 million) but, after scaling, has the largest spike in daily new cases per capita.
