remedy – Engineering Evil

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))

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

Elderberry shown to fight Influenza at multiple stages

The phytochemicals from the elderberry juice were shown to be effective at stopping the virus infecting the cells, however to the surprise of the researchers they were even more effective at inhibiting viral propagation at later stages of the influenza cycle when the cells had already been infected with the virus.

#Elderberry #Influenza # anthocyanidin

Golnoosh Torabian, Peter Valtchev, Qayyum Adil, Fariba Dehghani. Anti-influenza activity of elderberry (Sambucus nigra). Journal of Functional Foods, 2019; 54: 353 DOI: 10.1016/j.jff.2019.01.031

https://www.sciencedirect.com/science/article/pii/S1756464619300313?via%3Dihub

Bad Breath quickly eliminated by a Ginger compound

As the results of this study show, the pungent principle of ginger, the so-called 6-gingerol, makes the level of the enzyme sulfhydryl oxidase 1 in saliva increase 16-fold within a few seconds. The saliva and breath analyses carried out on human volunteers show that the enzyme breaks down malodorous sulfur-containing compounds

Matthias Bader, Theresa Stolle, Maximilian Jennerwein, Jürgen Hauck, Buket Sahin, Thomas Hofmann. Chemosensate-Induced Modulation of the Salivary Proteome and Metabolome Alters the Sensory Perception of Salt Taste and Odor-Active Thiols. Journal of Agricultural and Food Chemistry, 2018; 66 (29): 7740 DOI: 10.1021/acs.jafc.8b02772

Lung Function Decline Dramatically Slowed with a Flavonoid

Previous research has shown that the plant-produced chemicals known as flavonoids have beneficial antioxidant and anti-inflammatory properties. Anthocyanins, the type of flavonoid investigated in the current study, have been detected in lung tissue shortly after being ingested, and in animals models of chronic obstructive pulmonary disease (COPD). The plant chemicals appear to reduce mucus and inflammatory secretions.

Dietary Intake of Anthocyanin Flavonoids and Ten Year Lung Function Decline in Adults from the European Community Respiratory Health Survey (ECRHS) V. Garcia Larsen, R. Villegas, E.R. Omenaas, C. Svanes, J. Garcia-Aymerich, P.G.J. Burney, D. Jarvis, and ECRHS Diet Working Group B23. ENVIRONMENTAL EPIDEMIOLOGICAL INVESTIGATIONS IN ASTHMA. May 1, 2018, A2797-A2797

Anthocyanin, Flavonoids, COPD, FEV1, FVC, lung, Lung Age, Lung Decline, Lung Function, Remedy, Food

Kefir may help Hypertension and Neuroinflammation

Kefir may help Hypertension and Neuroinflammation

Drinking kefir may have a positive effect on blood pressure by promoting communication between the gut and brain. Kefir is a fermented probiotic milk beverage known to help maintain the balance of beneficial bacteria in the digestive system.

Probiotic Kefir Antihypertensive Effects in Spontaneously Hypertensive Rats Involves Central and Peripheral Mechanisms

Mirian A. Silva-Cutini, Sarah C. Peaden, Francesca E. Mowry, Henri A.G. Ducray, Ludmila P. Globa, Iryna B. Sorokulova, Tadeu U. Andrade, and Vinicia C. Biancardi

The FASEB Journal 2018 32:1_supplement, 924.2-924.2