Weather controls the viral load in the atmosphere

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Weather controls the viral load in the atmosphere

Weather controls the viral load in the atmosphere

Weather controls viral load in the atmosphere, viral load changes levels of infections and deaths; Lockdowns/other restrictions have prevented natural herd immunity

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Weather controls the viral load in the atmosphere

Natural progression vs Vaccines

*The big pharma, political and media scam. Had no vaccines been used, deaths would have been exactly as they turned out. Vaccines made no difference whatsoever"

Throughout history seasonal airborne virus outbreaks have occurred, all with the same progression during the season. Deaths rise from the beginning of September one year, reach a peak in January the next year, and fall to the end of August that year. This pattern of airborne respiratory virus deaths is the well known and documented Gompertz curve. Image 1.

Season 2020/21, when the vaccines were introduced during December 2020 just before the peak of the season. Correlations between deaths and the number of vaccinations were very high. But correlation does not prove cause. Image 2

Image 3. How did deaths keep rising when 6 million people had been vaccinated? When 45-50 million vaccinations had been achieved how did deaths increase from July2021?

SARS CoV-2/COVID-19 deaths to end of 2020/21 season

Analysis Post government data manipulation, Image 1 shows the final deaths: total all causes, respiratory and COVID-19 deaths by year from 2010/11 to 2020/21. Plus total population, comparisons and excess deaths.

Image 1.

Using 2017/18 as a benchmark (highest respiratory deaths season in the 10 year record) prior to COVID-19.

2019/20 season

  • 110,154 respiratory+COVID deaths were 30,002 more than 2017/18
  • All causes of deaths vs population were 0.88%. 0.04% higher than 2017/18 and 2014/15 seasons
  • Respiratory+COVID-19 deaths were 18.4% vs all causes of death, 2.9% higher than 2012/13 season
  • Excess deaths 30,002

2020/10 season

  • 132,678 respiratory+COVID deaths were 52,526 more than 2017/18
  • All causes of deaths vs population were 0.85%. 0.01% higher than 2017/18 and 2014/15 seasons
  • Respiratory+COVID-19 deaths were 22.8% vs all causes of death, 7.3% higher than 2012/13 season
  • Excess deaths 52,526

Considering the 2019/20 season with 30,002 excess deaths and as reported in parliament 26,000 of these were caused by the NHS transferring hospital patients with covid-19 to care homes would have meant on 4,002 excess deaths.

2020/21 excess deaths of 52,526 can be attributed to lockdowns, social distancing, group control and wearing of face masks from March 2020, all of which prevented natural herd immunity.

Image 2.
All causes of death vs respiratory deaths and COVID-19 deaths.
Not as drastic as was shown by the media and government.

Image 3.
Age related deaths at the peak of the 2020/21 season shows the percentage of deaths by age group. With 99.3% of deaths occurring in people older than 40 *there was absolutely no need for anyone under the age of 40 to be vaccinated. Vaccinating children, whose natural immune system works perfectly well was/is tantamount to child abuse.

COVID-19 deaths - government data manipulation 06Aug2021

In the report below, there is a section on data from Office of National Statistics (ONS) where the data on respiratory deaths made no sense. The first change started on the 08Jan2021 with a significant rise in weekly respiratory deaths from 678 on the 01Jan2021 to 7,025 per week on the 08Jan2021, considering the average respiratory deaths from 2010-2019 was just 1,372, this was a nonsensical major change.

As of 06Aug2021 the ONS have made another significant change to the data as seen in figure 1 opposite.

They have added two caveats "deaths involving respiratory disease (any mention on the death certificate)" - the published data from 08Jan2021.
There is now a new caveat "deaths due to respiratory disease (underlying cause)" - the changed data. This is more in line with the 10-year average of respiratory deaths, actually below the average of 2010-2019.

They now have the same two caveats for COVID-19. These caveats have never been published previously; the new data makes a little more sense for respiratory deaths, but questionable for being too low.

Fig 1 shows the difference in data changes made to respiratory deaths 06Aug2021

However, the daily data for COVId-19 has this caveat:

"Deaths within 28 days of positive test
Latest data provided on 22 August 2021
Last 7 days
52 (8.2%)
Rate per 100,000 people: 0.9"

How is it possible to know if a patient tested 28 days prior to death did not recover from COVID-19 and died from another prime condition. Surely when a patient dies, the medics should put the prime reason for death on the certificate.

The difference between weekly reporting from ONS
and is interesting.

Plotting weekly COVID-19 deaths from ONS and daily (7-day count-back) from coronavirus data shows some large discrepancies. Which of these data is correct?

Fig 2 COVID-19 deaths: weekly ONS compared to daily (7-day count-back)

The scale is deceptive as both data sets appear to be very similar. It is only when actual differences are plotted that and the total added for each do the large discrepancies appear, particularly at the peak of 2020/21 season.

Fig 3 differences between weekly ONS and weekly (7-day count-back) reporting

Figure 3 shows some large differences, 1,100 at the peak of the epidemic, between the two government reporting sites, weekly ONS and daily

The totals for the period 11Sep2020 to 06Aug2021 are 81,046 from ONS and 88,338 from the coronavirus government site, an 8.3% difference. Which one is correct?

Table 1 shows seasons, using the new data, 2019/20 and 2020/21 in comparison with reference period 2017/18 season

Figures in red are forecast from 06Aug2021 to end of season 31Aug2021
In table 1 there is little difference in all causes of deaths 5.7% increase in 2019/20 season and just 3% increase in 2020/21 against the reference season of 2017/18, which, accounting for a 2% population increase, the difference are 3.7% and 1% respectfully. The reason 2017/18 was selected; it was the highest respiratory and all causes of deaths data from 2010-2019, without lockdown or other restrictions and the NHS coped.

It can be argued season 2019/20 had an excess of deaths of 30,000 respiratory/COVId-19 deaths but COVID-19 deaths for 2020/21 season look over exaggerated considering all causes of deaths and the new and unlikely respiratory deaths. Perhaps this is a key case of died with, and not prime cause of death.

If ONS can make such major changes with respiratory deaths, is it possible COVID-19 deaths have been over exaggerated containing many instances of deaths which were "died with COVID-19 and not as a prime cause of death. Which would make COVID-19 data considerably lower than has been published, particularly in the 2020/21 season.

The 2020/21 season data is nonsensical, with little change to all causes of deaths and now, a major decrease in respiratory deaths in comparison to both 2017/18 and 2019/20 seasons, but far larger increase in COVID-19 deaths.

Counting deaths of COVID-19, where an unreliable positive test has occurred within 28 days of death, is a bizarre metric to use. All it does is inflate COVID-19 deaths. How difficult is it for a medic to know the prime cause of death?

What would make more sense is, bearing in mind COVID-19 is a respiratory disease, that there was 30,000 more respiratory deaths and 30,000 less COVID deaths in the 2020/21 season and 10,000 more respiratory deaths and 10,000 less COVID-19 deaths in the 2029/20 season. This would have meant 30,583 COVID-19 deaths in 2019/20 and 53,990 in 2020/21 giving a total of 84,573 much lower than the 131,854 being reported.

The totals of respiratory and COVID-19 added together are considerably higher than the 2017/18 season respiratory figures, 30,002 more in 2019/20 and 53,375 in 2020/21 which obviously put a great strain on the NHS
Considering all the above data manipulation, all causes of death for both seasons (2019/20 and 2020/21) added together gives an increase of just 49,735 over the 2017/18 season.

Given the effect of the enforced regulations has had on the nation both economically and psychologically, based on greatly exaggerated COVID-19 deaths, shows how badly the whole escapade has unraveled.

For the future all respiratory viruses such as SARS-CoV-2, an airborne respiratory virus, should be treated as such in terms of data and added to respiratory deaths and not as a stand-alone entity where the prime cause of death is not properly recorded.

Viral load the key to understanding airborne respiratory virus transmission and deaths

During winter there is less light (shorter days, lower UV radiation), higher humidity, lower temperature, cloudier, with lower pressure, and higher wind speeds. The left side of image opposite shows how virus particles (virions) during winter are trapped in the lower atmosphere, closer to humans, with more virions to breathe in, more infection occurs. Once these virus particles have been inhaled, they multiply in the respiratory system, and are exhaled adding to the viral load in the lower atmosphere. Also, during winter there is little thermal convection, thus virus particles are trapped in the lower atmosphere, hence the strong correlation between respiratory deaths and temperature (-0.74).

With higher wind speeds in winter, the viral load can be moved laterally, following the wind direction, providing greater transmission spread.

Higher viral load is prime time for more serious respiratory infection and higher deaths. The zeniths of seasonal airborne virus outbreaks are strongly aligned to the nadir of LOD.

On the right of the image, with higher levels of sunshine (increased UV radiation), longer daylight, drier air and higher temperature, the virus particles can rise, due to thermal convection, to high levels in the atmosphere, out of reach of humans on the surface. The higher the virions attain the more they are exposed to UVB radiation which irradiates the virus particles by breaking down their mRNA. This leads to greatly reduced viral load near the surface. This is the most effective time to gain natural herd immunity. Because there are few virions in the lower atmosphere at this time of year, if a person inhales a few virus particles the consequences are that of a mild infection at worst. There are simply not enough virions in the lower atmosphere to cause serious illness.

On 21Oct2021 weekly COVID-19 deaths for England & Wales of 812 are 760 per week lower than the 11 year average of 1,572 (2010-2020) for respiratory deaths in England & Wales (the 3rd Benchmark)

The viral load is controlled by weather. Deaths today reflect what weather conditions were about 18 days ago, it takes roughly this time from infection of upper then lower respiratory system to death.

The most robust measure for all seasonal airborne viruses is by understanding deaths, not by testing transmission with unknown numbers of false positives.This is like saying someone has flu!

There needs to be full understanding of viral load in the atmosphere, and the 18 days lag to death, to appreciate the progression of all airborne respiratory viruses.

Natural Herd Immunity vs Restrictions

SARS CoV-2 is an airborne respiratory virus, just like the previous 10 years airborne respiratory viruses.

All of these airborne viruses have followed the Gompertz curve based on the level of UV radiation (length of day & sunshine), temperature, humidity, and wind speed. What makes infections and deaths for 2019/20 and 2020/21 much more than previous years? Lack of herd immunity.

Lockdowns, social distancing, group control and face-mask wearing all prevent natural herd immunity.

No more updates to this section

Lockdown & restrictions Actual vs Logical

405 days lost opportunities to gain herd immunity

SARS CoV-2 is an airborne seasonal respiratory virus. Just like Influenza, Spanish Flu, Hong Kong Flu, H1N1, H1N2, Bird Flu etc.All of these viruses act in virtually the same ways, the death rates begin to rise from September and start to fall from January. They follow the well documented Gompertz curve.

The reason, in September levels of UV radiation and temperatures begin to fall, and then begin to rise again in January. UV radiation breaks down the virus's mRNA, reducing its potency and lessens its lethality. This happens with all airborne viruses.

If the governments aim was to "Save the NHS", their first objective, would be to look back at historical data and see, at the peak of the 2015 weekly respiratory deaths for England & Wales were 3,521, when the NHS coped without lockdown or any other restrictions.

This should have been their benchmark. Had this logical approach been adopted, lockdown and restrictions should have been lifted on the 18Feb2021

Length of Day (LOD) vs Sunshine & LOD vs Deaths

Sunshine on a daily basis is erratic, which means using this metric for long-term forecasting airborne respiratory virus progression becomes a problem. However using length of day (LOD) for showing the potential for UV radiation, given the length of day changes are small on a daily basis and shows the decreases and increases in daylight between winter and summer.

When sunshine and LOD are compared at the same location, in this case Birmingham, the following results showed the correlation between the two was a moderate 0.54. This is due the erratic nature of sunshine on a daily basis. When polynomial trends were added LOD had an R^2 of 0.9591 with sunshine polynomial trend almost matching.

Comparing LOD with historical respiratory deaths was an interesting exercise, producing a correlation of -0.84, a strong to very strong relationship.It shows one Gompertz curve for each season (no 2nd, 3rd or 4th waves). All other anomalies away from the perfect Gompertz curve are due to short term weather conditions.

Understanding the seasonality of airborne respiratory virus outbreaks is essential to help in forecasting for the future. Using LOD as a long range forecast and sunshine hours, temperature and wind direction and speed for short term forecasting (1-15 days ahead) would be a major help in planning resources for the NHS..

Viral load and the weather

Throughout history airborne respiratory virus outbreaks have been seasonal. In winter, deaths from respiratory disease caused by airborne viruses increase substantially. Conversely, during spring and summer deaths significantly decrease.

SARS CoV-2 virus/COVID-19 disease is no different.

UVB radiation is the biggest killer of all airborne viruses.

During winter when sunshine levels are low, day length is shorter, higher humidity, more cloud and lower temperature airborne viruses are trapped in the lower atmosphere increasing viral load.

During spring and summer when sunshine levels are higher, length of day is longer, humidity lower and temperatures higher, virus particles are raised high into the atmosphere by thermal convection. At this time deaths from airborne respiratory viruses such as SARS CoV-2/COVid-19 disease are significantly lower.This is the time to gain herd immunity.

Herd immunity has worked very well in the past. Lockdowns, social distancing, group restrictions and face mask wearing prevented this natural process.

^All logical benchmarks to lift restrictions have been surpassed
for E&W^

The first logical benchmark was set at 3,572, the peak of the 2014/15 respiratory outbreak, when the NHS coped without lockdown or any other restrictions. This was passed on 18Feb2021

The second logical benchmark was set at 2,827 when the government unlocked on 10thMay2020 in the 2020 Mar-May outbreak. This was passed on 23Feb2021.

The third benchmark was set at 1,934 the lowest peak for respiratory deaths for the last 11 years. This was passed on the 02Mar2021 (1,772)

The fourth benchmark was set at 1,572, the average respiratory deaths for seasons 2010/11-2019/20. This was passed on 05Mar2021

People have asked why not include other respiratory deaths like Influenza and pneumonia. The reason is, we have never been locked down or had any restrictions until Cov-2/COVID-19 for at least the last 11 years. Don't forget CoV-2 is an airborne respiratory disease and acts just like seasonal flu, also an airborne respiratory virus..

There is also a lot of confusion about reporting of other respiratory deaths, in just one week between 01Jan2021 and 08Jan2021 the number jumped from 678 to 7,025, There is no rational answer, better ask ONS..

Weather controls the viral load in the atmosphere

Peaks & Troughs of Seasonal Airborne Virus Outbreaks

The table to the right shows the troughs and peaks dates for weekly respiratory deaths in England & Wales

2020 and 2021 include CoV-2/COVID-19

Noticeable is, trough dates are around the end of August/beginning of September, about 3-4 weeks before the Autumnal equinox. Most peak dates are within the first 2 weeks of January, about 3-4 weeks after the winter solstice. This indicates UV radiation as a key factor.

A definitive seasonal pattern apart from 2020 semi-seasonal CoV-2/COVID-19 and 2016 when the peak was just 99 more than 07 January.

The semi-seasonal CoV-2/COVID-19 outbreak is questionable as with a novel virus it wasn't recognised as such during Dec2019, Jan/Feb 2020 and only became known about during March,

As someone who was infected with COVID-19 in mid Dec2019, probably in Lisbon and confirmed by classic symptoms, x-rays, and CT scan. It was probably diagnosed as a bad chest infection, as was mine, therefore it probably was an extension of the 2019/20 seasonal epidemic.

Summer vs Winter Deaths

During summer when temperatures rise we often hear about heatwave deaths in the media.

These two plots show all causes of deaths in Summer vs Temperature (Max) and in Winter vs Temperature (Max)

Average weekly deaths for England & Wales 2010-2019 10,032

Average weekly Summer deaths
2010-2019 9,502

Average weekly Winter deaths
2010-2019 10,627

Which equates to 58,500 more deaths per year in winter than summer.

The Weather Effect on Seasonal Airborne Virus Outbreaks

Does weather affect airborne virus epidemics? I have read some papers which argue against a weather "effect" and say no. The peak and trough information on the last 11 years in the UK does indicate a seasonal trend.

Because the Mar/Apr2020 CoV-2 epidemic was not considered as fully seasonal, I looked at weekly all causes of death and respiratory deaths from 2010 to a benchmark.

It is quite possible that the Mar/Apr epidemic was seasonal, there may have been many thousands of people who had respiratory symptoms (COVID-19) during Dec2019, Jan, and Feb 2020 and were treated as a bad flu without needing hospital treatment, just two courses of antibiotics from the GP. (I was one of them Dec/Jan with X-ray and CT scan as evidence, I did need hospital visits for the scans) and this was before SARS CoV-2/COVID-19 had been recognised..

When you see the correlations on the plots on the left they confirm seasonal airborne viruses are controlled by the weather, particularly UV radiation which kills airborne viruses. Because airborne viruses a very light in weight they can reach high altitudes and hence stimulated by UVA (which boosts vitamin-D synthesis and enhancing the innate immune system, UVB during summer months, and probably UVC at the very highest levels...

Deaths data from ONS is weekly for England & Wales and therefore the temperature, sunshine (UV) data comparisons have to be averaged weekly. This significantly weakens the correlation for Sunshine (UV) which is the key marker. UV radiation destroys airborne viruses hence reduces the viral load in the atmosphere

Day Length vs Airborne Viruses Respiratory Deaths & COVID-19

In the previous section we looked at how weather affects airborne viruses respiratory deaths. Two major problems were obvious, firstly death rates are compiled weekly by ONS, and secondly because of that the weather data has to be UK weekly means. This is shown up by moderate correlations.

Because weather is very variable, it could be wet, cloudy and cool in the north, but dry, with bright sunshine and warm in the south. Averaging weather never gives a good result. Having thought about this problem we decided to look at day length, as this does nor vary by much across the country. As the death rate data is for England & Wales, we chose Birmingham as a central point to collect day length hours and minutes per day.

The charts opposite show, on the left hand column weekly death rates versus average 7-day count back (weekly) day lengths.

Correlations ranged between weak to moderate, but with an obvious three week lag between deaths and day lengths. In the charts on the right we lagged the death rates by 3 weeks and the correlations increased dramatically to moderate and strong.

This shows day length is an important factor to improve forecasting ability for future airborne virus outbreaks. the longer the day length the more likely there is more sunshine. And it is sunshine (UV radiation) which kills airborne viruses, always has done and always will do.

The number of deaths depends on two factors, the potency of the virus and what happened in the previous season. this is shown by many deaths in the 2014/15 season (strong correlation) and far less deaths in the next season 2015/16 (moderate correlation).