Right now, New York seems to be doing pretty well. Why do you think New York’s handling of the pandemic was the worst?
What did South Dakota do right? Honestly asking, I haven’t seen anything either way.
Illinois has done well. The governor took steps early and has been updating the public daily – useful press conferences with respected experts. Ditto Chicago, which took a major step earlyish by cancelling the huge St. Patrick’s Parade and where the mayor has also been proactive in communicating with the public.
Both the state and Chicago maintained strict countermeasures even under pressure from parts of the business community and some rural Republican officials. The reopening has been very gradual, in measured steps based on the relevant numbers. And both the governor and mayor have been clear that countermeasures will be reapplied if the numbers require. Chicago just re-banned all indoor service in bars that don’t serve food and personal services (shaves and facials) that would require removing one’s mask.
At least in my experience in metropolitan Chicago, the mask requirements have made indoor mask-wearing common and apparently uncontroversial. I see lots of people outdoors not wearing them (and many people wearing them) but being very good about keeping at least a six-foot distance.
Connecticut seems to have done a great job throughput, with one exception: allowing the virus to spread in nursing homes at the beginning of the crisis.
Other than that significant misstep, we did a good job of flattening and crushing the curve. There is currently little community transmission of the virus in Connecticut. Now we’re just waiting for the virus to inevitably leak back in from where it is raging out of control in other parts of the country.
On that note, Rachel Maddow had an interesting interview two nights ago with Dr. David Ho (the world-famous virologist who is famous for contributing to the current treatment for controlling HIV) about how sequential state responses have greatly extended the timeline of the pandemic in the U.S. Because every state is acting independently, we failed to get the virus under control everywhere at the same time, and it is now hopscotching around the country.
This is a pretty nice visualization of states’ case and death numbers in context:
Hard to say how much credit/blame is due to governments, though.
Decades of planning went into their pandemic readiness plan, the lynchpin of which was to be inhospitable to human habitation.
NY has already killed everybody and there’s few vulnerable left. Cuomo sent elderly people to a certain death in nursing homes and De Blasio was telling people to go see a movie in March. The numbers show how bad it was:
New York: 1,679 deaths per million
US: 449 deaths per million
No lockdown and only 138 deaths per million. Far below the US avg of 449.
Why didn’t you go with Alaska? They’re creaming SD in lower Deaths per Million.
Oh, I thought you were being serious, not just pointing out the empty states. If you have a serious answer, I’ll be happy to read your cites that SD did better than other empty places.
NY definitely had some missteps in the beginning, when no one knew what to do. If only other states had learned from their errors. Instead, Florida, Texas, Louisiana, and even California are totally fucking this up for those of us who sacrificed early on.
The death rate ranks about 15th lowest.
But it’s the state with the 5th lowest population density, and ranks 1st for hospital beds per capita at twice the national average.
These are two massive preexisting advantages that obviously are nothing to do with the state’s response to the pandemic.
It’s a political jab only, but no real substance of note, as facts are that the death rate in NY nursing homes due to Covid were far lower then other states, every state at first was following the same guidelines of the feds (Trump). Ny did well considering they were the hotspot of the virus (Again party due to the feds (Trump).)
Nearly One-Third of U.S. Coronavirus Deaths Are Linked to Nursing Homes - The New York Times
South Dakota did a horrible job in the meat plant in Sioux Falls – for a while in April, it was the worst infection hot spot in the whole nation. Impressive, for such an empty state. And their death figures are cooked because so many employees there actually live (and died) just across the border in Minnesota or Iowa – because SD is such a lousy place to live.
Obviously, that poster can’t be seriously suggesting that the lack of a lockdown was what made SD’s response great. He or she can’t be saying that NY shouldn’t have locked down. So, I’d love for that poster to come back and explain further, rather than just a couple of drive-by posts with some out-of-context statistics.
@agzem, care to explain?
Nonsense. That’s probably not true even in NYCity; and many upstate counties have had few cases and have many people probably not yet exposed – in part because the shutdown, while far from total, plus the example of what was happening in the City led a good percentage of people to be very careful about possible exposure even before there were any confirmed cases within miles of them.
Here is the current seven day rolling average of deaths per day per one million residents. Delaware is a little weird here. They reported 49 deaths yesterday but only 11 in the seven days prior to that. I think, without checking, that those 49 were a reclassification of previous deaths as covid deaths and that 49 people didn’t actually die on the same day when one or two was the typical number.
South Dakota is in 37th place edged only slightly by New York in 39th for what it’s worth.
| Rank | State | Deaths | WoW |
|---|---|---|---|
| 1 | Arizona | 10.97 | +11.58% |
| 2 | Delaware | 8.36 | +1325.00% |
| 3 | South Carolina | 8.02 | +73.05% |
| 4 | Louisiana | 6.42 | +65.87% |
| 5 | Mississippi | 6.29 | +11.97% |
| 6 | Florida | 5.64 | +20.63% |
| 7 | Alabama | 5.04 | +7.45% |
| 8 | Texas | 4.93 | +28.87% |
| 9 | Georgia | 4.17 | +85.63% |
| 10 | Nevada | 3.94 | +46.55% |
| 11 | California | 2.62 | +10.17% |
| 12 | Tennessee | 2.57 | +33.70% |
| 13 | New Mexico | 2.45 | +38.46% |
| 14 | Ohio | 2.29 | +128.05% |
| 15 | Idaho | 2.08 | +52.94% |
| 16 | Massachusetts | 1.99 | -9.43% |
| 17 | North Carolina | 1.96 | +13.39% |
| 18 | Arkansas | 1.94 | +2.50% |
| 19 | Iowa | 1.81 | -2.44% |
| 20 | Indiana | 1.72 | +47.27% |
| 21 | Utah | 1.69 | +35.71% |
| 22 | North Dakota | 1.69 | +80.00% |
| 23 | Rhode Island | 1.62 | -14.29% |
| 24 | Missouri | 1.54 | +20.00% |
| 25 | Maryland | 1.49 | +12.50% |
| 26 | Oklahoma | 1.41 | +34.48% |
| 27 | New Jersey | 1.37 | -58.13% |
| 28 | Pennsylvania | 1.28 | -12.88% |
| 29 | Illinois | 1.26 | -6.67% |
| 30 | New Hampshire | 1.26 | +140.00% |
| 31 | Montana | 1.20 | +0.00% |
| 32 | Washington | 1.16 | +10.71% |
| 33 | Kansas | 1.13 | +109.09% |
| 34 | Nebraska | 1.11 | -0.00% |
| 35 | Wisconsin | 1.10 | +136.84% |
| 36 | Kentucky | 1.06 | -13.16% |
| 37 | South Dakota | 0.97 | -33.33% |
| 38 | Colorado | 0.97 | +44.44% |
| 39 | New York | 0.95 | -18.75% |
| 40 | Oregon | 0.95 | +27.27% |
| 41 | Virginia | 0.90 | -1.82% |
| 42 | Minnesota | 0.84 | -17.50% |
| 43 | District of Columbia | 0.81 | -55.56% |
| 44 | Connecticut | 0.68 | -64.58% |
| 45 | Michigan | 0.64 | -35.71% |
| 46 | Alaska | 0.39 | +inf% |
| 47 | Maine | 0.32 | -25.00% |
| 48 | Hawaii | 0.30 | -25.00% |
| 49 | Wyoming | 0.25 | -66.67% |
| 50 | West Virginia | 0.24 | -40.00% |
| 51 | Vermont | 0.00 | +nan% |
Where’s this data from?
I have a Python script that screen scrapes each of the state pages and outputs a table in markdown format.
Here it is if anyone is interested.
import re
import requests
import datetime
import pandas as pd
from bs4 import BeautifulSoup
# Set parameters
deaths_or_cases = "Deaths"
per_capita = True
# Get populations
pop_file = "state_populations.txt"
pop_dict = {}
states = []
f = open(pop_file, 'r')
for line in f:
st, pop = line.strip().split('\t')
pop_dict[st] = int(pop)
if st != 'US':
states.append(st)
f.close()
# Screen scrape both states.
out_dict = {}
for state in states:
heading = "Daily New " + deaths_or_cases + " in " + state
url_st = state.lower().replace(' ', '-')
url = 'https://www.worldometers.info/coronavirus/usa/' + url_st + '/'
webpage = requests.get(url)
soup = BeautifulSoup(webpage.text, 'lxml')
graph_divs = soup.findAll("div", {"class": "row graph_row"})
for gd in graph_divs:
h = gd.find('h3')
if h.text.lower() == heading.lower():
break
script = gd.find('script').text
lines = script.split('\n')
lines = [line.strip() for line in lines]
# Get and clean x values from html source.
x = next((line for line in lines if line.startswith('categories')))
x = re.search('\[(.+?)\]', x).group(1)
x = x.replace('"','')
x = x.split(',')
x = [datetime.datetime.strptime(d + ' 2020', '%b %d %Y') for d in x]
# Get and clean y values from html source.
y = next((line for line in lines if line.startswith('data')))
y = re.search('\[(.+?)\]', y).group(1)
y = y.replace('null','0')
y = y.split(',')
if per_capita:
y = [1000000*int(d)/pop_dict[state] for d in y]
else:
y = [int(d) for d in y]
# Put that stuff into a dataframe and calculate rolling averages.
df = pd.DataFrame()
df['Date'] = x
col = state + ' Daily ' + deaths_or_cases
df[col] = y
df.set_index('Date', inplace = True)
rcol = state + ' 7-day Avg'
df[rcol] = df[col].rolling('7d').mean()
wcol = state + ' WoW'
df[wcol] = df[rcol].pct_change(periods=7)
# For each state put 7 day avegae and week over week change
# into the output dictionary
out_dict[state] = [df.iloc[-1,:].loc[rcol],
df.iloc[-1,:].loc[wcol]]
print("|Rank|State|"+deaths_or_cases+"|WoW|")
print("|:---|:---|:---|:---|")
out_nice = sorted([[k, v[0], v[1]] for k, v in out_dict.items()],
key = lambda x: -x[1])
for i, row in enumerate(out_nice):
s, d, c = row
out = [str(i + 1),
s,
"{:.2f}".format(d),
"{0:+.2%}".format(c)]
print("|" +"|".join(out)+"|")
The comparison is per capita.