Citrus Phytochemical promising, SARS-CoV-2 may be on path to be weaker than the Common Cold, + DATA

This Week we Cover: Another common cold virus? Modeling SARS-CoV-2’s progress through the ages. Can limonene be a possible candidate for evaluation as an agent or adjuvant against infection, immunity, and inflammation in COVID-19? New study: Without right messaging, masks could lead to more COVID-19 spread Depression and stress could dampen efficacy of COVID-19 vaccines Should you avoid alcohol when getting a coronavirus vaccine? Plus we cover data in Hospital Occupancy, Where are the vaccines, still no Correlations, How are Florida and Sweden doing, etc… #limonene #coviddata #sarsdata Links in order as above:……………

Annals of Internal Medicine “Mask Benefit Claims Inconclusive”, Lund University Lockdown Models Heavily Flawed” Plus new Data Vaccine to Population Ratio

Annals of Internal Medicine “Mask Benefit Claims Inconclusive”, Lund University Lockdown Models Heavily Flawed” Plus new Data Vaccine to Population Ratio

This week we review the above plus start mining data in reference to COVID-19 Vaccine rate and Hospital Occupancy correlations overall.

#facecoverings #lockdown #SARS-CoV-2

Update Alert 4: Masks for Prevention of Respiratory Virus Infections, Including SARS-CoV-2, in Health Care and Community Settings

Model used to evaluate lockdowns was flawed

data, peer reviewed, study, lockdown effectiveness, pandemic, data analytics, python, matploblib, pandas, biostatistics, corona, sarscov2 data, masks, face covering, danmask19, seaborn, owid, our world in data, covid testing, , vector, danish study, denmark study, virus, micron, aerosolized, covid aerosol, sars-cov-2 micron size, mask filtration, vitamin d, uvc , mask, mask effectiveness, surgical mask harms, nasal deposition, airflow, b cell, denmask, Sweden, owid, healthdata, micronized, airborne

Sars-COV-2 confirmed aerosolized below 1um, Masks may officially do more harm than good, plus data

1. We review now the very strong hypothesis that Sars-COV-2 is now aerosolized on a sub-micron level greatly reducing any impact PPE may have and greatly increasing transmission vectors. 2. Immunity to COVID-19 appears to last at least 8 months with significant B cell memory. 3. Plus deep dive into global Data that really shows there is no real clear pandemic mitigation strategy that is having any real-world effect except unnecessarily causing hardship among the subject populace.

#sarscov2 #aerosolized #covid

Data Sources
HealthData .gov
Our World in Data
Covid Tracking Project

Article Links (Current New) :
Assessment of Air Contamination by SARS-CoV-2 in Hospital Settings

UH Mānoa researcher examines why people choose to wear face coverings

COVID immunity lasts up (at least) to 8 months, new data reveals

Masks may be worse than no mask if COVID-19 is aerosolized. UV light, Cadmium, and Vit. D all Corr,

This week we look at one of the worst cases of media bias to date in reference to the study: Effects of mask-wearing on the inhalability and deposition of airborne SARS-CoV-2 aerosols in human upper airway. Cadmiun and heightened mortality, UV light in relation to COVID and Vitamin D, again.
#sarscov2aerosol #micron #surgicalmask
Study Sources:
‘Alarmingly high’ vitamin D deficiency in the United Kingdom

COVID-19 does not damage auditory system, Tel Aviv University and Galilee Medical Center study finds

LED lights found to kill coronavirus: Global first in fight against COVID-19

‘The mask matters: How masks affect airflow, protection effectiveness
Effects of mask-wearing on the inhalability and deposition of airborne SARS-CoV-2 aerosols in human upper airway
In this study, we found that the protective efficacy of a mask for the nasal airway decreases at lower inhalation flow rates. Particularly at 15 l/min, the nasal retention of 1 µm–3 µm ambient aerosols is even higher by wearing a 65% filtration mask than without a mask at all.’

SARS-CoV-2-like particles very sensitive to temperature

New study links cadmium to more severe flu, pneumonia infections

COVID-19 spread increases when UV levels decrease

COVID-19 as the Leading Cause of Death in the United States

Reference to the DENMASK study:

API Sources:
Our wold in Data
COVID Tracking Project

Minimum Sizes of Respiratory Particles Carrying SARS-CoV-2 and the Possibility of
Aerosol Generation

Molnupiravir, may fully suppress virus transmission within 24 hours – Green Tea inhibits Sars-COV-2

Today we look at this week’s COVID data and research 1 Molnupiravir ( MK-4482 ), may fully suppress virus transmission within 24 hours 2 Chemical compounds in foods can inhibit a key SARS-CoV-2 enzyme 3 Vehicles and Testing Sites possible COVID vectors 4 BMJ says no to Many school COVID restrictions Plus tons of HeatMaps, Correlations, Graphs, and Charts questioning whether many world leaders have any clue about anything actually. 😉


COVID-19 Surgical Mask Random Trial Offer Little to No Protection, British Gov’t Wrong on most Data

This week we run the data analytics on Face Coverings and Country Stats using Seaborn and Pandas as well as cover the DANMASK-19 randomized trial, The Daily Mails fight with the British Government attempt to terrorize it, citizens, with bad COVID data. The Possible transmission of COVID-19 from pets to humans continues to gain traction, etc… #covid19 #masks #facecoverings Data API Sources: Our World in Data Covid Tracking Project Effectiveness of Adding a Mask Recommendation to Other Public Health Measures to Prevent SARS-CoV-2 Infection in Danish Mask Wearers A cluster randomised trial of cloth masks compared with medical masks in healthcare workers

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)