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:…
#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.datetime = pd.to_datetime(
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), 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:…
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
Claim Currently Invalid —Ralph Turchiano
data = pd.read_csv(‘owid-covid-data-19SEP2020.csv’)
pd.set_option(‘max_columns’, None)
data[‘date’] = pd.to_datetime(data[‘date’])
data_18SEP = data[data[‘date’]==’2020-09-18′]
data_ind = data_18SEP[data_18SEP[‘human_development_index’]=.8]
data_ind.drop([‘iso_code’,’continent’,’handwashing_facilities’,’stringency_index’,], axis=1, inplace=True)
data_ind[‘extreme_poverty’].fillna(0, inplace=True)
data_compare = pd.DataFrame([data.loc[37991],data.loc[41736]])
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)
New =pd.DataFrame(data[[‘new_cases_smoothed_per_million’,’new_deaths_smoothed_per_million’]])
dataw = data.loc[data[‘iso_code’] == ‘OWID_WRL’]
dataw.datetime = pd.to_datetime(
dataw.set_index(‘date’, inplace=True)
data_cl = pd.DataFrame(dataw[[‘new_deaths_smoothed’,’new_cases_smoothed’]])

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(“”) 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”) 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))

Honeysuckle Decoction Inhibits SARS-CoV-2

In a new study in Cell Discovery, Chen-Yu Zhang’s group at Nanjing University and two other groups from Wuhan Institute of Virology and the Second Hospital of Nanjing present a novel finding that absorbed miRNA MIR2911 in honeysuckle decoction (HD) can directly target SARS-CoV-2 genes and inhibit viral replication. Drinking of HD accelerate the negative conversion of COVID-19 patients.

#mir2911 #sarcov2 #honeysuckle

Zhou, L., Zhou, Z., Jiang, X. et al. Absorbed plant MIR2911 in honeysuckle decoction inhibits SARS-CoV-2 replication and accelerates the negative conversion of infected patients. Cell Discov 6, 54 (2020).

An easier way to go vegan, Vitamin B12 CAN be produced during grain fermentation

The highest production was found in the rice bran (ca. 742 ng/g dw), followed by the buckwheat bran (ca. 631 ng/g dw), after fermentation. Meanwhile, the addition of L. brevis was able to dominate indigenous microbes during fermentation and thus greatly improve microbial safety during the fermentation of different grain materials. #b12 #vegan #fermentation In situ fortification of vitamin B12 in grain materials by fermentation withPropionibacterium freudenreichii, Chong Xie ISBN 978-951-51-6355-4 (PAPERBACK) ISBN 978-951-51-6356-1 (PDF, http://ETHESIS.HELSINKI.FI) ISSN 0355-1180 UNIGRAFIA HELSINKI 2020

Black raspberries show promise for reducing skin inflammation, allergies

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