COVID Vaccines not being tested to work, CBD a COVID Lung Saver?, Shoes thee COVID carrier and Data.

This week we review disturbing vaccine study requirements, CBD an incredible gem if possibly protecting the lungs and restoring oxygen levels, and a strong correlation as to shoes being an unrecognized major disease vector. In addition to looking at COVID data correlations to which countries are locking down in response Sars-COV-2 to those which have not or have done little. #covidvaccine #covidvector #covidnews Data Sources API for DataFrames: The COVID Tracking Project Our wold in Data (Oxford) Links: https://www.eurekalert.org/pub_releases/2020-10/uoo-ecw102220.php#.X5N_7_DuPM0.wordpress https://www.eurekalert.org/pub_releases/2020-10/b-cvt102020.php#.X5OGbCHAYR8.wordpress https://www.eurekalert.org/pub_releases/2020-10/mcog-chr101620.php#.X45lOsCeu4k.wordpress https://wwwnc.cdc.gov/eid/article/26/7/20-0885_article

Vitamin D may be more effective than masks and distancing combined for COVID ?

Vitamin D may be more effective than masks and distancing combined for COVID ?

In patients older than 40 years they observed that those patients who were vitamin D sufficient were 51.5 percent less likely to die from the infection compared to patients who were vitamin D deficient or insufficient with a blood level of 25-hydroxyvitamin D less than 30 ng/mL.

Holick, who most recently published a study which found that a sufficient amount of vitamin D can reduce the risk of catching coronavirus by 54 percent, believes that being vitamin D sufficient helps to fight consequences from being infected not only with the corona virus but also other viruses causing upper respiratory tract illnesses including influenza. “There is great concern that the combination of an influenza infection and a coronal viral infection could substantially increase hospitalizations and death due to complications from these viral infections.”

#covid19 #sarscov2 #vitaminD

Kaufman HW, Niles JK, Kroll MH, Bi C, Holick MF (2020) SARS-CoV-2 positivity rates associated with circulating 25-hydroxyvitamin D levels. PLOS ONE 15(9): e0239252. https://doi.org/10.1371/journal.pone.0239252

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))

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). https://doi.org/10.1038/s41421-020-00197-3

https://www.nature.com/articles/s41421-020-00197-3#ethics

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 https://helda.helsinki.fi/bitstream/handle/10138/317682/insitufo.pdf?sequence=1&isAllowed=y 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