COVID19 Analytics – Mask Trash and Shoes a Major Spreader, Newsom & Fauci Being Odd, Florida Wins

Our weekly review of the current COVID data and country comparisons as well as other oddities such as Mask Litter, Trash Cans, and Shoes being unintended spreaders. All this under the guise of Amateur Python Analytics. Brief CSV File Request Code below (Pandas). That will allow you to pull Oxford University Data up to the current date. Enjoy 😉

This is a long one, next week I will make it A LOT shorter.

#covid19 #sarscov2 #data

Code Snippet:
import pandas as pd
import csv
import requests
younameit = pd.read_csv(‘https://covid.ourworldindata.org/data/owid-covid-data.csv’)

https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/bulletins/deathsduetocoronaviruscovid19comparedwithdeathsfrominfluenzaandpneumoniaenglandandwales/deathsoccurringbetween1januaryand31august2020

https://wwwnc.cdc.gov/eid/article/26/7/20-0885_article

https://www.eurekalert.org/pub_releases/2020-10/uoh-rci100120.php#.X3fUGZsAGM0.wordpress

https://www.cidrap.umn.edu/news-perspective/2020/04/commentary-masks-all-covid-19-not-based-sound-data

Pandemic Charting – Weaponizing Uncertainty – Countries Do better with a Light touch – Python Data

Lockdowns a Complete Failure compared to controls – Countries that did not? Python Analysis Part 2

Part 2 as promised. We compare cases and death per million from industrialized countries which did little to nothing to Great Britain and the United States. The Data extrapolated is from: https://ourworldindata.org/coronaviru…
#covid19 #lockdown #socialdistancing

(Volume is kind of Choppy midpoint)
Additional Code: From Part 1:
datasw = data.loc[data.iso_code==’SWE’, :]
datagb = data.loc[data.iso_code==’GBR’, :]
dataus = data.loc[data.iso_code==’USA’, :]
datasg = data.loc[data.iso_code==’SGP’, :]
datajp = data.loc[data.iso_code==’JPN’, :]
datako = data.loc[data.iso_code==’KOR’, :]
datatw = data.loc[data.iso_code==’TWN’, :]
dataall = [datagb,dataus,datasw,datasg,datajp,datako, datatw]
dataall = pd.concat(dataall)
dataall
dataall.datetime = pd.to_datetime(dataall.date)
dataall.set_index(‘date’, inplace=True)
fig, ax = plt.subplots(figsize=(50,25))
dataall.groupby(‘iso_code’)[‘new_cases_smoothed_per_million’].plot(legend=True,fontsize = 20, linewidth=7.0)
ax.legend([‘Great Britain = Lockdown’,’Japan = No LD’, ‘South Korea = No LD’, ‘Singapore = LD JUNE -Migrant LD HIghest POP Density’, ‘Sweden = No LD’, ‘Taiwan = No LD’,’USA = Lockdown’],prop=dict(size=50))
comp = dataall.loc[‘2020-09-18’]
comp.set_index(“iso_code”, inplace=True)
comp= pd.DataFrame(comp[[‘total_cases_per_million’,’total_deaths_per_million’]])
plt.rc(‘legend’, fontsize=50)
comp.plot.bar(rot=0, figsize=(20,20),fontsize=30)

Pandemic Over? COVID-19 World data Amateur Python Analysis

From an educational perspective, we review current COVID-19 data and arrive look at lockdowns and population density appears to have no numerical effect currently on COVID-19. In any case, this is more about exploring the code from a beginner’s standpoint with Python and DataFrames.
#covid19 #pandemicover #coviddata
CSV files found here:
https://ourworldindata.org/coronaviru…
Code: (Had to remove the angle brackets)
import numpy as np
import pandas as pd
from scipy import stats
import statsmodels.api as sm
import matplotlib.pyplot as plt
import pandas as pd
from scipy.stats import spearmanr
from scipy.stats import kendalltau
from scipy.stats import pearsonr
from scipy import stats
import seaborn as sns
import warnings
warnings.filterwarnings(“ignore”)
#Pandemic
Claim Currently Invalid —Ralph Turchiano
data = pd.read_csv(‘owid-covid-data-19SEP2020.csv’)
data.info()
pd.set_option(‘max_columns’, None)
data.tail(5)
data[‘date’] = pd.to_datetime(data[‘date’])
data.info()
data_18SEP = data[data[‘date’]==’2020-09-18′]
data_ind = data_18SEP[data_18SEP[‘human_development_index’]=.8]
data_ind.head(10)
data_ind.drop([‘iso_code’,’continent’,’handwashing_facilities’,’stringency_index’,], axis=1, inplace=True)
data_ind.columns
data_ind[‘extreme_poverty’].fillna(0, inplace=True)
data_compare = pd.DataFrame([data.loc[37991],data.loc[41736]])
data_compare
data_compare.set_index(‘location’,inplace=True)
data_compare[‘total_cases_per_million’]
data_Swe_USA=pd.DataFrame(data_compare[[‘total_cases_per_million’,’new_cases_per_million’,’new_deaths_per_million’]])
data_Swe_USApd.DataFrame(data_compare[[‘total_cases_per_million’,’new_cases_per_million’,’new_deaths_per_million’]])
data_Swe_USA
data_ind.drop([‘date’,’new_cases’,’new_deaths’,’total_tests’, ‘total_tests_per_thousand’,
‘new_tests_per_thousand’, ‘new_tests_smoothed’, ‘new_tests’,
‘new_tests_smoothed_per_thousand’, ‘tests_per_case’,’tests_units’,’new_deaths_per_million’,’positive_rate’ ], axis=1, inplace=True)
data_ind.tail()
data_ind.dropna(inplace=True)
data_ind.corr(“kendall”)
data_18SEP.tail()
data_18SEP.loc[44310]
data_18SEP.loc[44310,[‘new_cases_smoothed_per_million’,’new_deaths_smoothed_per_million’]]
New =pd.DataFrame(data[[‘new_cases_smoothed_per_million’,’new_deaths_smoothed_per_million’]])
New.corr(‘kendall’)
dataw = data.loc[data[‘iso_code’] == ‘OWID_WRL’]
dataw
dataw.datetime = pd.to_datetime(data.date)
dataw.set_index(‘date’, inplace=True)
data_cl = pd.DataFrame(dataw[[‘new_deaths_smoothed’,’new_cases_smoothed’]])
data_cl.dropna(inplace=True)
data_cl.plot(figsize=(30,12))
data_cl.tail(20)

COVID-19 Tracking Data API and Data Anomalies (No Correlations? Cases to Hospitalizations Increases)

Is there a correlation between Positive cases and Hospitalizations? Below is the API for python access, open to all who desire to filter the data. I want to just give easy access to all the beginner students data scientists out there, such as myself..Explore and Discover: **My Apologies It says High Def, but does not look High Def on video here**

Code: import matplotlib.pyplot as plt import pandas as pd from scipy import stats import statsmodels.api as sm import requests import time from IPython.display import clear_output response = requests.get(“https://covidtracking.com/api/v1/us/daily.csv”) covid = response.content ccc = open(“daily.csv”,”wb”) ccc.write(covid) ccc.close() df = pd.read_csv(“daily.csv”, index_col = ‘date’) df.head() data = df[[‘positiveIncrease’,’hospitalizedIncrease’]] dataT = df[[‘positiveIncrease’,’hospitalizedIncrease’,’hospitalizedCurrently’]] dataD = df[[‘hospitalizedIncrease’,’deathIncrease’]] dataT.head(20) plt.figure(figsize=(20,10)) Y = data[‘positiveIncrease’] X = data[‘hospitalizedIncrease’] plt.scatter(X,Y) plt.ylabel(“Tested Positive Increase”) plt.xlabel(“Hospitalization Increase”) plt.show() Y1 = sm.add_constant(Y) reg = sm.OLS(X, Y1).fit() reg.summary() data.plot(y=[‘hospitalizedIncrease’,’positiveIncrease’],xticks=data.index[0:len(data):30], rot=90, figsize=(20,10) ) for x in range(len(data)): plt.figure(figsize=(20,10)) plt.xticks( data.index.values[0:len(data):30], rotation = 90, fontsize=20 ) plt.plot(data.tail(x))

Aromatherapy may reduce stress and anxiety , WVU researcher suggests

Aromatherapy may reduce nurses’ stress, WVU researcher suggests

The researchers found that participants felt significantly less stressed, anxious, fatigued and overwhelmed after wearing the aromatherapy patches. The levels of anxiety and fatigue they reported fell by 40 percent, and their stress levels and feelings of being overwhelmed decreased by half.

***The patches were infused with a citrusy blend of essential oils: lemon, orange, mandarin, pink grapefruit, lemongrass, lime and peppermint.

#aromatherapy #stress #medical

Reven, Marian & Humphrey-Rowan, Janelle & Moore, Nina. (2020). West Virginia University Oncology Nurses Don Aromatherapy Patches: A Pilot Feasibility Study/ “The International Journal of Professional Holistic Aromatherapy” (IJPHA) Volume 8, Issue 4, Spring 2020.

https://www.researchgate.net/publication/340487906_West_Virginia_University_Oncology_Nurses_Don_Aromatherapy_Patches_A_Pilot_Feasibility_Study_The_International_Journal_of_Professional_Holistic_Aromatherapy_IJPHA_Volume_8_Issue_4_Spring_2020

Cloth Masks May Prevent Transmission of COVID-19: An Evidence-Based, Risk-Based Approach

https://www.acpjournals.org/doi/10.7326/M20-2567

Beware of false negatives in diagnostic testing of COVID-19

https://www.eurekalert.org/pub_releases/2020-05/jhm-bof052620.php

Pandemic likely to cause long-term health problems, Yale School of Public Health finds

https://www.eurekalert.org/pub_releases/2020-05/ysop-plt052020.php#.Xss0KInDXgQ.wordpress

Social isolation increases the risk of heart attacks, strokes, and death from all causes

https://www.eurekalert.org/pub_releases/2020-05/sh-sii051820.php#.XssyzNlZeQ4.wordpress

Social isolation linked to more severe COVID-19 outbreaks

https://www.eurekalert.org/pub_releases/2020-05/p-sil051920.php#.XssxctNHIyM.wordpress

stress, aromatherapy, isolation, social isolation, oils, essential oils, stress, anxiety, covid, covid19, sars, rt-pcr, testing, accuracy, covid19 test accuracy, false negative, Reverse transcriptase polymerase chain reaction test

possibly

More COVID Research Information Censored

More COVID Research Censored CDC and the WHO, to my dismay, are either directly or indirectly controlling the flow of information and research, possibly creating an echo chamber of bias. The level of censorship is getting so out of control; it is highly likely now it may be resulting in harm in a variety of societal dimensions. As well as the Freedom to Speech is becoming rapidly stratified among those in positions of wealth, power, or fame, It is becoming painfully apparent that self-proclaimed thought leaders may not be behooving us in times of crisis, manufactured, self-inflicted, or real. At the very least, by not reviewing and growing from our errors, we are, in all essence, committed to repeating them. Freedom of Speech, in its most basic form, is simply the freedom to speak. Take that right away from one, and you build a case to take it away from all, for, of course, your own protection. #censorship #freedomofspeech #covid