Zvi comments on Covid 7/​9: Lies, Damn Lies and Death Rates

[Zvi]

That’s it! Thanks. How did you do that calculation?

I see ways to do it but none that aren’t annoying to implement. If I was a better coder I’d be tempted to write a scraper.

[PeterMcCluskey]

I downloaded it in their csv format and whipped up this python program to process it (I hope someone improves on it):

#!/usr/bin/python
# -*- coding: utf-8 -*-

import re, sys, io, getopt, csv

def parse(filename):
    column_dict = {}
    list_states = []
    val_dict = {}
    week_dicts = {'2019' : {}, '2020': {}}
    with open(filename, 'r') as fd:
        reader = csv.reader(fd, delimiter=',', quotechar='"')
        for row in reader:
            if row[0] == 'Jurisdiction of Occurrence':
                for i, fld in enumerate(row):
                    column_dict[fld] = i
                unknown_col = column_dict['Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (R00-R99)']
                continue
            yyyymmdd = row[0][:10].replace('-', '')
            yyyy = row[1]
            week_num = int(row[2])
            tokens = row[3].split('/')
            yymmdd = "%s%02d%02d" % (tokens[2][-2:], int(tokens[0]), int(tokens[1]))
            state = row[0]
            mystery_deaths = row[unknown_col].strip(' ')
            all_deaths = row[column_dict['All Cause']].strip(' ')
            if not (state in list_states):
                list_states.append(state)
            #print("%s %6s %6s %-s" % (yyyymmdd, mystery_deaths, all_deaths, state))
            week_dicts[yyyy][week_num] = yymmdd
            val_dict[yyyy + str(week_num) + state] = (mystery_deaths, all_deaths)
            #break
    num_weeks = len(week_dicts['2020'].keys())
    start_week = 4
    s = "%15s" % ''
    s2 = "%15s" % ''
    for i in range(start_week, num_weeks + 1):
        s += "%6d " % i
        s2 += "%s " % week_dicts['2020'][i]
    print(s)
    print(s2)
    tot_w24 = 0
    for state in list_states:
        s = "%-15.15s " % state
        for week_num in range(start_week, num_weeks + 1):
            try:
                (mystery_deaths2020, all_deaths) = val_dict['2020' + str(week_num) + state]
                (mystery_deaths2019, all_deaths2019) = val_dict['2019' + str(week_num) + state]
            except KeyError:
                s += '       '
                continue
            try:
                delta = (float(mystery_deaths2020 or 0) - float(mystery_deaths2019 or 0)) / float(all_deaths)
            except ValueError:
                print("skip %s %s %s %s %s." % (state, week_num, mystery_deaths2019, mystery_deaths2020, all_deaths))
                s += '       '
                continue
            if week_num == 24:
                tot_w24 += float(mystery_deaths2020 or 0) - float(mystery_deaths2019 or 0)
            s += "%5.2f  " % (100*delta)
        print(s)
    print("tot_w24 %s" % tot_w24)

if __name__ == '__main__':
    import getopt
    avail_opts = ['acct=', 
    ]
    (options, args) = getopt.getopt(sys.argv[1:], '', avail_opts)
    option_dict = {}
    for opt in options:
    option_dict[opt[0][2:]] = opt[1]
    parse(args[0])

[spkoc]

This website seems to do the trick and have charts: https://​​episphere.github.io/​​mortalitytracker/​​#cause=symptoms_signs_and_abnormal&state=Florida

The spike in Unclassified… is huge. They’re fudging the data, ffs.

[Joshua Goings]

It’s incredible. I tried to compare with the COVID-19 deaths, but the chart provided did not seem to be working and only showed unclassified. I worked up a few states like Florida, AZ, TX, NY, and the US based on the link in a blog post here, maybe it will be helpful to look at https://​​joshuagoings.com/​​2020/​​07/​​11/​​coronavirus-deaths/​​