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What Data are Health Experts Using

What Data are Health Experts Using

In Their Alarming COVID Modelling Forecasts?

NORTHERN ONTARIO ~~~~~~  February 24, 2021  (LSN)  What Data Are Health Experts Using in Their Alarming COVID Modelling Forecasts?

We hear a lot about the modelling public health officials use to project COVID case numbers as  we move through the pandemic. One of the prime purposes of this modelling, we are told, is to ensure COVID cases don’t overwhelm the health care system. We aren’t told what data or systems they use for this modelling, only that it is key to determining what measures and restrictions should be put in place in their response approach to “flatten the curve”.

Models Seem to Over Forecast

While most models, to date, have vastly over estimated COVID spread, deaths, and hospitalizations, our governments still insist on using them, maintaining  that they are “following the science. Modelling recently

released by our Federal Chief Health Officer Dr. Theresa Tam has predicted such a drastic rise in COVID cases by mid-March that it is literally “off the chart”. This has left many experts scratching their heads including Dr. Martha Fulford, an Infectious Disease Physician at Hamilton Health Sciences and Assistant Professor at McMaster

Dr. Therasa Tam’s February modelling chart, note the yellow and light grey lines predicting an extreme rise in COVID cases.

University. She told the Sun newspaper, “For me the model is only as good as the data imputed and we need to know what the underlying assumptions and data are.”

https://torontosun.com/news/health-officials-cant-explain-dr-tams-rocket-ship-modelling?fbclid=IwAR3-g4oOwM-WKBQauPOEkm3_QGinxUEPcfnJLoKZyBwoPgGyav3wdw0eYS0

 

Opposition MPs, upon the release of Dr. Tam’s new modelling did question Public Health Agency of Canada (PHAC) experts in that regard at a meeting of the Parliamentary Health Committee, but received no clear answers on what the modelling had been based upon, except that it seems highly focused on the new “variants”. This is not the first example of Tam’s

penchant for “extreme case” scenarios. Her models released in the previous three months all fell short of  a predicted dramatic rises in COVID cases. Now, as she is predicting more dire circumstances on the horizon, Canada has seen COVID cases declining since mid-January. Of course, our health officials and government’s like to attribute this to the lockdowns imposed in many provinces in December. Yet, statistics from other countries, including the US, where some states actually began re-opening in January, and states like Florida and the South Dakota never locked down, are also experiencing the same downward trends from mid-January and continuing into February. This same trend is occurring

 

in virtually every western country in the world. This includes Sweden, which didn’t lockdown, and the United Kingdom (UK), which did, and was also one of the first countries to report new COVID “variants”. This can  be quickly confirmed by quick check on the Worldometer web site. https://www.worldometers.info/coronavirus/#countries

Given these statistics, it is hard to believe lockdowns made any great difference in COVID’s case trajectory. Did Dr. Tam’s team take any of this data into consideration when developing this latest model?

Here in Ontario we again don’t know what “science” was followed in the modelling released in

early January 2021 that predicted such a massive increase in daily COVID cases that it led the government to impose an extended province-wide lockdown, coupled with a stern “stay at home order.” Now, as cases diminish, Premier Doug Ford, will no doubt attribute it to the lockdown, despite the downward trending of COVID cases in other countries.

Are health experts are wilfully ignoring certain factors in their modelling process to produce such dire scenarios?

Vaccines?

COVID 19 vaccines are now available. That being said, the Liberal government has done a woefully poor job of actually acquiring vaccines for Canadians. This is partly due to the fact that they entered into a partnership with China to produce a boatload of vaccines back in June. Except the deal fell through when China refused to send vaccine samples to Canada to conduct trials, leaving the Liberal government placing late vaccine orders with other suppliers. Still, we have been told that this dearth in vaccines is coming to an end and soon we will awash in doses. Does Dr. Tam not have faith in the government’s ability to actually acquire vaccines? That is what her modelling seems to reflect.  

 

Seasonal Flu Statistics?

Has the drastic drop in seasonal influenza cases been factored into these modelling scenarios? This trend surfaced early in 2020 once the coronavirus began spreading across Canada. A Health Canada Flu Watch Report for the week of March 15, 2020 noted a sharp decrease in laboratory detections of the flu and a reduction in hospitalizations in both the adult and paediatric populations. A later Health Canada Flu Watch Report for the week of February 7, 2021 continued to report that influenza activity remains low across the country. https://www.canada.ca/en/public-health/services/diseases/flu-influenza/influenza-surveillance/weekly-influenza-reports.html

One would think such modelling would take into consideration the fact that hospital admissions

for seasonal influenza dropped dramatically over the course of 2020 and the pattern is continuing into 2021. Thus the fewer hospital beds occupied by flu patients, means more beds for COVID patients.

 

COVID Case Statistical Comparisons?

One would also think that, given we have been in pandemic mode for over a year now, looking at past COVID statistics and comparing them to ongoing and current data would be useful to such predictive modelling. Statistics deal in numbers and percentages and when one looks at the percentages, recorded by Health Canada, in terms of COVID deaths and hospitalizations, those

numbers have remained constant throughout the course of the pandemic. Would such consistencies not be of value in predictive modelling exercise as it might relate to health resourcing?

 

COVID Deaths: A comparison of the percentages of total COVID deaths from October 2020 (no lockdown) and from February 2021 (post lockdown), reveal that rates in various age groups remained remarkably constant as the chart below illustrates. The overall percent of COVID deaths in those aged 50 and under was 0.9% in October compared to 1.2% in February—a .03 difference. Meanwhile, those over the age of 70 account for a whopping 90 percent of COVID deaths.  Given that this has became clear two months into the pandemic one would think more effort would be focused on targeted measures to protect this group of people, particularly those in retirement facilities. Instead our governments seem to prefer an approach of rotating and revolving lockdowns that impact everything from mental and physical health,  to educating children and the economy. Do health experts take these impacts into account when they go through their modelling process, or it is pure tunnel vision on cases and only cases?

October 17

Percentage

February 12

Percentage

0-19

0

0-19

0

20-29

0.1

20-29

0.1

30-39

0.2

30-39

0.3

40-49

0.6

40-49

0.8

50-59

2.4

50-59

2.6

60-69

7.3

60-69

7.6

70-79

18.1

70-79

18.9

80+

73.8

80+

69.7

 

COVID Hospitalizations: The percent of COVID patients admitted to hospital for the same two periods also remains consistent. The percentage of admissions in those under the age of 50 in October was 17.3%, and in February the percentage was 16.7%, a difference of 0.6%. Would such information, if factored in with the drop in flu hospitalizations, be of value in determining medical resources needed, as they relate to COVID, rather than looking solely at overall positive case numbers, which seem, largely to be, what the models have been based on?

October 17

Percentage

February 12

Percentage

0-19

1.4

0-19

1.5

20-29

3.1

20-29

3.1

30-39

5.1

30-39

5.1

40-49

7.7

40-49

7.1

50-59

13.9

50-59

12.1

60-69

17.2

60-69

16.6

70-79

20.6

70-79

21.0

80+

31.1

80+

34.2

COVID Intensive Care Unit Admissions: Again the pattern continues when comparing the percentage of ICU admissions for the same two periods. The difference in the under 50 age group is 1.5 percent more admissions in October, compared to February. With such information at their fingertips would it not make sense for health experts to use it to determine the medical resources and facilities required to meet these numbers? Essentially, if you know from past experience that 1.3 percent of Canadians under the age of 19 who contract COVID will require ICU treatment, could you not plan accordingly given that 1.3 percent has remained constant for the past ten months?  If you know, from statistical data that some 21 percent of Canadians between the ages of 70 and 79, who contract COVID will require hospitalization, could you not plan for that by allotting beds accordingly? 

October 17

Percentage

February 12

Percentage

0-19

1.3

0-19

1.3

20-29

3.4

20-29

2.6

30-39

4.9

30-39

4.8

40-49

9.3

40-49

8.7

50-59

20.4

50-59

17.8

60-69

24.6

60-69

26

70-79

23.5

70-79

25.7

80+

12.5

80+

13.2

 

Stoking Fear Never Stops

These percentages have remained constant throughout the pandemic, during lockdowns, during re-openings and during the peak of the pandemic waves. Yet, we seldom hear about this from

our media, instead they bombard Canadians with COVID numbers and statistics, from alarming reports of rising case numbers to the daily death counts and now, of course the new “more contagious” variants from the UK, South Africa, and Brazil. Yet, again, when one looks at statistics from these countries, the variants have had little impact on COVID case numbers.  The UK, as mentioned earlier is in a downward trend and case numbers in South Africa have been dropping consistently since mid-January as illustrated in the Worldometer chart. In Brazil, while there hasn’t been a significant drop in case numbers, neither has there been any dramatic spike. Did Dr. Tam look at this data while creating her models? 

The models and the media’s unquestioning support of them all seem designed to stoke fear in people and it works.  There are many Canadians that live in fear of going to work, or shopping,

fear for their elderly parents or fear sending their kids to school. It also provokes anger and cynicism in a growing number of people who don’t believe COVID is a dangerous as they have been lead to believe, resulting in an increasing number of public protests against lockdowns and other rigid and arbitrary restrictions such as official stay at home orders, curfews, and severe curtailment of outdoor activities.

 

Most people, by now, know that COVID kills the elderly, particularly those in continuing care homes where once it enters such a facility it spreads like wildfire. Over the course of ten months, most of our governments have not found a way to fix this, other than shutting the entire province or country down. It seems, that based on the current mysterious modelling exercises carried out by our federal health experts, we can expect this trend to continue. Health experts will force more lockdowns with this modelling and then take questionable credit for the reduced number of cases, due to arbitrary restrictions, unless more people start pushing back and questioning their rationale and challenging their conclusions.

Northern Ontario 
Kenora, Rainy River, Dryden, Thunder Bay, Terrace Bay Marathon, Sault Ste Marie, Sudbury, North Bay, Ontario

#LSN_Health  #LSN_ONNews

By: Roxanne Halverson
Former TBT anchor and reporter
Living in Ottawa (retired RCMP Senior Emergency Management Planner

Northern Ontario
Kenora, Rainy River, Dryden, Thunder Bay, Terrace Bay Marathon, Sault Ste Marie, Sudbury, North Bay, Ontario

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Follow the numbers for TBDHU Easter Month Long Lockdown

Population of Thunder Bay District (2016)  146,048
April 8th 1st stay of stay at home order for 28 days

Date New
Cases
Active
Cases 
Resolved 
Cases
Deceased Hospitalized ICU
Apr 3 26 183 2673 53 12 3
Apr 5   9 163 2701 54 14 4
Apr 6  1 149 2716 54 14 5
Apr 7 15 148 2732 54 10 4
Apr 8  3 104 2779 54 10 4
Apr 9 12 101 2793 55 12 5
Apr 10 13   98 2809 55 13 5
Apr 11 Not  Reported        
Apr 12 13 91 2829 55 14 5
Apr 13 9 87 2841 56 13 4
Apr 14 4 72 2860 56 11 4
Apr 15 4 61 2875 56 10 5
Apri16 8 57 2885 58 12 4
Apr 17 4 56 2890 58 9 3
    Not  reported  Today    
             
Apr 20 11 54 2909 60 7 2
Apr 21  6 52 2916 61 7 2
             

 

Follow the Ontario numbers for Easter Month Long Lockdown

Ontario's 3rd lockdown start April 3 at 12.01 am. 
April 8th 1st stay of stay at home order for 28 days

Date Test Cases Date  Test  Cases  Date  Test  Cases 
April 1  62,300 2,557 Apr 16 64,300 4,812      
Apr 2 121,400 3,089 Apr 17  56,900 4,362      
Apr 3   3,009 Apr 18 53,800 4,250      
Apr 4   3,041 apr 19 42,900 4,447      
Apr 5    2,938 Apr 20 40,600 3,469      
Apr 6 37,500 3,065 Apr 21 51,900 4,212      
Apr 7 49,900 3,215            
Apr 8 63,800 3,295            
Apr 9 61,400 4,227            
Apr 10 61,400 3,813            
Apr 11 56,400 4,456            
apr 12 47,900 4,401            
Apr 13 42,200 3,670            
Apr 14  54,200 4,156            
Apr 15 65,600 4,736            

 

TBDHU goes into Grey Lockdown March 1st 12.01 am.

Population of Thunder Bay District (2016)  146,048

Date  New
Cases
Active
Cases 
Resolved
Cases 
Deceased  Hospitalized 
ICU
Feb 27   335 1218 30 23 7
Feb 28   343 1239 30 26 9
March 1 56 376 1262 30 29 9
March 2  40 374 1304 30 26 9
March 3  26 389 1314 31 29 10
March 4 61 397 1366 32 29 10
March 5 48 389 1422 32 27 8
March 6 40 386 1465 32 35 11
March 7 111 470 1492 32 37 11
March 8 30 462 1529 33 36 10
March 9  58 458 1589 35 29 9
March 10 46 414 1677 37 31 10
March 11 46 423 1714 37 35 9
March 12 82 435 1784 37 35 8
March 13 43 446 1816 37 36 9
March 14 40 437 1865 37 37 9
March 15 51 446 1906 38 44 7
March 16 35 403 1984 38 39 8
March 17 68 424 2030 39 38 8
March 18 40 406 2088 39 39 12
March 19 38 399 2133 39 44 15
March 20 32 379 2185 39 45 16
March 21 20 362 2222 39 35 12
March 22  9 325 2267 40 35 12
March 23 29 305 2316 40 31 7
March 24 25 286 2355 45 26 5
March 25 33 283 2390 46 28 5
March 26 20 259 2434 46 28 4
March 27 29 233 2488 47 20 3
March 28  21 216 2526 47 19 4
March 29 12 207 2547 47 19 4
March 30 25 199 2576 51 17 3
March 31 34 216 2592 52 18 3
April 1 23 210 2621 52 18 3
             
             
             
             

This is the total number of deaths among cases in which COVID-19 was determined to be a contributing or underlying cause of death

 

Number of Test and Number of Cases of COVID Ontario

Date Tests Cases Date Tests  Cases  Date Tests Cases
Feb 11 68,800 945 Mar 1 35,000 1,023 Mar 19 56,100 1,745
Feb 12 62,000 1,076 Mar 2  30,800 966 Mar 20 52,100 1,829
Feb 13 58,800 1,300 Mar 3 52,600 958 Mar 21 49,200 1,791
Feb 14 48,700 981 Mar 4 65,600 994 Mar 22 31,100 1,699
Feb 15 27,000 964 Mar 5  64,700 1,250 Mar 23 32,600 1,546

Feb 16

30,400 904 Mar 6 57,800 990 Mar 24 52,000 1,571
Feb 17 34,000 847 Mar 7 46,600 329 Mar 25 60,100 2,380
Feb 18 56,200 1,038 Mar 8 38,100 568 Mar 26 53,400 2,169
Feb 19 65,400 1,150 Mar 9  33,300 1,185 Mar 27 61,000 2,453
Feb 20 57,200 1,228 Mar 10 54,100 1,316 Mar 28  50,200 2,448
Feb 21 Not report Not Report Mar 11 60,600 1,092 Mar 29  39,500 2,094
Feb 22 31,200 1,058 Mar 12 64,600 1,371 Mar 30  36,100 2,336
Feb 23 26,000 975 Mar 13  58,400 1,468 Mar 31 52,500 2,333
Feb 24  54,900 1,054 Mar 14 47,600 1,747 April 1  62,300 2,557
Feb 25 66,400 1,138 Mar 15 34,000 1,268      
Feb 26 64,000 1,258 Mar 16 28,500 1,074       
Feb 27 59,400 1,185 Mar 17 49,100 1,508      
Feb 28 49,200 1,062 Mar 18 58,600 1,553      
                 

 

Covid and test During Ontario Lockdown

December 26th is day one of lockdown in Ontario 

Date Tests #Cases  Date Test Cases Date  Tests Cases
Dec 26   2,142 Jan 11     Jan 26 30,700 1,740
Dec 27   2,005 Jan 12     Jan 27 55,200 1,670
Dec 28   1,939 Jan 13     Jan 28 64,700 2,093
Dec 29   2,553 Jan 14     Jan 29 69,000 1,837
Dec 30 39,200 2,923 Jan 15     Jan 30 59,600 2,063
Dec 31   3,328 Jan16  73,900 3,056 Jan 31 49,400 1,848
Jan 1   2,476       Feb 1 30,400 1,969
Jan 2   3,363 Jan 17     Feb 2 28,600 745
Jan 3 49,800 2,964 Jan 18     Feb 3 52,400 1,172
Jan
4
    Jan 19     Feb 4 64,500 1,563
Jan 5 35,200 3,128 Jan 20     Feb 5  62,700 1,670
Jan 6     Jan 21 70,300 2,632 Feb 6 62,300 1,388
Jan 7     Jan 22 71,800 2,662 Feb 7 51,700 1,489
Jan 8     Jan 23 63,500 2,359 Feb 8  28,300 1,265

Jan 9

72,900 3,443 Jan 24  48,900 2,417 Feb 9 30,800 1,022
Jan 10     Jan 25 36,000 1,958 Feb 10 52,500 1,072

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