April growth rates by county

gr <- function(date, cases) {
  imax <- length(date)
  stopifnot(imax == length(cases))
  (log(cases[imax]) - log(cases[1])) / as.numeric(date[imax]-date[1])
}
aprildata <- 
  filter(cov19county, state == 'Virginia',
         !is.na(fips),
         date >= as.Date('2020-04-01'),
         date <= as.Date('2020-04-30')
         )
growthrates <- 
  group_by(aprildata, county, fips) %>%
  summarise(rate=gr(date, cases), cases=max(cases)) %>%
  mutate(td=log(2)/rate) %>%
  arrange(desc(rate))

#hist(filter(growthrates, rate>0)$td, breaks=25)
ggplot(growthrates, aes(x=td)) + geom_histogram(bins=25, alpha=0.7) + theme_bw()
## Warning: Removed 7 rows containing non-finite values (stat_bin).

Counties in the UVA catchment:

uvacounties <- filter(growthrates, fips %in% sampleCounties$fips)
print(uvacounties)
## # A tibble: 11 x 5
## # Groups:   county [11]
##    county               fips    rate cases    td
##    <chr>                <chr>  <dbl> <int> <dbl>
##  1 Buckingham           51029 0.123     55  5.65
##  2 Fluvanna             51065 0.118     73  5.86
##  3 Highland             51091 0.116      2  6   
##  4 Greene               51079 0.0814    10  8.52
##  5 Orange               51137 0.0707    27  9.80
##  6 Waynesboro city      51820 0.0631    11 11.0 
##  7 Madison              51113 0.0571    14 12.1 
##  8 Albemarle            51003 0.0495    80 14.0 
##  9 Louisa               51109 0.0487    41 14.2 
## 10 Nelson               51125 0.0464     7 14.9 
## 11 Charlottesville city 51540 0.0429    51 16.1

Counties with doubling times less than 10:

print(filter(growthrates, td <= 10))
## # A tibble: 77 x 5
## # Groups:   county [77]
##    county                fips   rate cases    td
##    <chr>                 <chr> <dbl> <int> <dbl>
##  1 Richmond              51159 0.198   141  3.50
##  2 Southampton           51175 0.176   115  3.94
##  3 Falls Church city     51610 0.171    26  4.05
##  4 Page                  51139 0.166    89  4.17
##  5 Montgomery            51121 0.148    54  4.69
##  6 Manassas Park city    51685 0.147    53  4.71
##  7 Harrisonburg city     51660 0.137   406  5.05
##  8 Colonial Heights city 51570 0.137    47  5.06
##  9 Augusta               51015 0.136    39  5.11
## 10 Buchanan              51027 0.132    16  5.25
## # ... with 67 more rows

Counties with doubling times between 10 and 20

print(filter(growthrates, td > 10, td<=20))
## # A tibble: 35 x 5
## # Groups:   county [35]
##    county           fips    rate cases    td
##    <chr>            <chr>  <dbl> <int> <dbl>
##  1 Frederick        51069 0.0691    97  10.0
##  2 Hopewell city    51670 0.0664    24  10.4
##  3 Roanoke city     51770 0.0653    35  10.6
##  4 Norfolk city     51710 0.0644   188  10.8
##  5 Buena Vista city 51530 0.0644     5  10.8
##  6 Campbell         51031 0.0631    11  11.0
##  7 Waynesboro city  51820 0.0631    11  11.0
##  8 Wythe            51197 0.0631    11  11.0
##  9 Giles            51071 0.0630     4  11  
## 10 Alleghany        51005 0.0596     5  11.6
## # ... with 25 more rows

Counties with doubling times greater than 20

print(filter(growthrates, td >20, rate>0))
## # A tibble: 10 x 5
## # Groups:   county [10]
##    county            fips    rate cases    td
##    <chr>             <chr>  <dbl> <int> <dbl>
##  1 Rockbridge        51163 0.0339     5  20.4
##  2 Tazewell          51185 0.0339     5  20.4
##  3 Lunenburg         51111 0.0330     4  21  
##  4 King and Queen    51097 0.0257     2  27  
##  5 Mathews           51115 0.0257     4  27  
##  6 Poquoson city     51735 0.0257     6  27. 
##  7 Williamsburg city 51830 0.0257    20  27. 
##  8 Amherst           51009 0.0218    10  31.8
##  9 James City        51095 0.0181   155  38.2
## 10 Lexington city    51678 0.0162     3  42.7

No growth or no cases observed

print(filter(growthrates, rate==0))
## # A tibble: 6 x 5
## # Groups:   county [6]
##   county         fips   rate cases    td
##   <chr>          <chr> <dbl> <int> <dbl>
## 1 Bristol city   51520     0     1   Inf
## 2 Covington city 51580     0     1   Inf
## 3 Craig          51045     0     2   Inf
## 4 Floyd          51063     0     1   Inf
## 5 Lancaster      51103     0     1   Inf
## 6 Norton city    51720     0     2   Inf