By Brian – Re-Blogged From WUWT
A detailed analysis of the University of Manitoba’s recent model prepared on behalf of the Canadian Government illustrates exaggerated and incalculable conclusions. These explicitly theoretical projections, which have little evidence to support them, set an unrealistic foundation of what is considered a success or not with regards to Dr. Tam’s policies. In this case, the models that are used to predict the effects of Sars-Cov-2 adapts a completely unrealistic and unattainable worst-case scenario. Essentially any result, and every result possible, will be hailed as a resounding success – which is disingenuous. The virus would not come close to manifesting the chaos projected, even among a society with the loosest of policies. Fortunately, there are examples as many countries had their own approach in fighting the virus.
The basics of the SEIR model are rudimentary – though filled with several bells and whistles that create the sense of false precision. This commentary will go through these one at a time. I find it important to note the modelling is unrealistic, serves to spread unwarranted fear in the Canadian population, and a breach of the trust placed in the Government of Canada by all Canadian citizens.
Canada’s COVID-19 Modeling
Fatality and Nursing Homes
Over 80% of all Sars-Cov-2 deaths in Canada are from Long term Care (“LTC”) and nursing home facilities[ii]. The inhabitants of these facilities are the oldest and weakest among the population. In fact, only wealthy populations have extensive LTC communities which are mainly in Europe and North America. Over 50% of Sars-Cov-2 fatality in the US and Europe are from these facilities[iii][iv].
The spread in these facilities is nosocomial. Meaning, not random population spread but a contagious virus dropped into a closed environment that then rips through the residents and staff[v],[vi]. These were all belatedly protected and we tragically saw the results of this inaction. Random spread can have no relationship to LTC spread if proper policy and funding is in place. It is important to note that the Canadian government modelers openly reference non-random influenza spread from a 2017 paper, but do not account for this in their modeling. This is completely inconsistent.
Having said that, any model that does not account separately for LTC spread and LTC fatality is simply a failure in illustrating the complete picture of the virus. The single largest source of risk and fatality not being broken out means this Canadian model has no ability to properly project fatality.
“Conclusions. Our study revealed a highly structured contact and movement patterns within the LTCF. Accounting for this structure—instead of assuming randomness—in decision analytic methods can result in substantially different predictions.” (https://doi.org/10.1177/0272989X17708564)
Infectious Fatality Rate (“IFR”)
IFR is simply defined as one risk of dying if infected and is not to be confused with Case Fatality Rate (“CFR”) which divides fatality by confirmed cases. CFR is an irrelevant statistic unless testing rate is relatively constant. The CFR misrepresents the danger of the virus. Incidentally, a corollary is new case counts that do not predict new fatality which sounds like a paradox but is statistically true. Unfortunately, the media is obsessed with case counts, but they are the least valuable statistic in describing the state of spread currently available, including the danger of the virus.
IFR varies by age and this is universal to all countries[vii]. All Canadian Government models[viii] have used an IFR of 1.2% – or 15x the true non-LTC Sars-Cov-2 risk, despite very strong evidence in March that its was 0.1% – 0.35%, or, near the flu. Even the CDC when adjusted for asymptomatic infection has IFR 0.1%-0.35% inclusive of LTC fatality. Recently Alberta concluded its antibody study. Based on the results alone, the IFR in Alberta at the time as ~0.35%, however 75% of those fatalities were LTC[ix]. Non-LTC IFR is 0.08% – which is 50% less risky than the common influenza[x].
For the general population, the Canadian Government models intended to dictate health policy say the virus is 17X more deadly than reality – which is misleading and instills an unwarranted fear in Canadian citizens.
Canadian Government Estimates (per million) of Hospitalisations, ICUs, and Fatalities vs Alberta Serology Based Actual Percentages (Ex-LTC)
A true predictive model would break out IFR by age and separately break out LTC fatality[xi]. The LTC break out is important – an Ontario government study concluded an LTC resident was 13x more likely to die than the same age non-LTC resident.
Serology studies in Africa and India – with poor health care relative to Canada show IFR’s 0.005% – 0.06%. These younger populations with no LTC community have virtually no risk to dying despite little access to treatment.
No Canadian government model has performed this basic and necessary inclusion – which causes the modelling to be inaccurate and overstates the severity of the virus.
R0 and R(t) are measurements on rate of viral spread. R0 is the rate of spread assuming no existing interactions with Sars-Cov-2, while R(t) which goes down over time adjusted R(0) for infected, recovered and dead.
The newest model uses R0 of 2.9, 3.3 and 3.7. These are results from old studies in hyperdense China. Side note, Canadian modeling only references old Chinese studies (dated) and the Imperial College/Neal Ferguson study (model failure) while excluding newer and more accurate studies. Models are a tool that require accurate inputs to accurately assess risk. The use of inaccurate inputs leads to inaccurate outputs.[xii],[xiii],[xiv].
Canadian R0, given our lower density (hence lower transmissible interactions) is about 2.0 nationally while early in the spread. There is tremendous supporting documentation/evidence of this, and it is unclear why the Government would only allow a lower bound almost 50% higher than actual and an upper bound almost 100% more.
The misuse of the R0 variable is another main driver – like the similar failed Imperial College model before it – the new Canadian model does not replicate spread in places like Florida or Sweden. It dramatically overestimates real life outcomes and should be compared against reality before providing outcomes to the public that cannot possibly happen under any scenario.
I’ll revisit R0 when discussing heterogeneity below.
There are multiple studies that the maximum infectious period of Sars-Cov-2 is about 8 days (known since early March)[xv]. The average time an infected person can infect another is about 4 days with a maximum of 8. The Canadian government model assumes an average of 10 days – which does not align with observable data. There is no science behind this assumption but has the effect of magnifying model spread and generating unnecessary fear.
Heterogeneity of Spread, Herd Immunity and the Function of T-cells
Herd Immunity Threshold (“HIT”) is defined as the point at which spread can only decay lower i.e. R(t) < 1 permanently[xvi]. Using basic math, HIT is reached when 1-1/R0 of the population is infected. If R0 is 2.0 – then 50% is HIT, if its 3.3 then ~70% need to be infected. But this isn’t true in the real world.
The main (and inaccurate) assumption is that everyone mixes perfectly – a concept called homogeneity. Using an analogy, the Canadian model assumes a bartender at a popular restaurant in downtown Toronto interacts with others the same about in a week as a person living alone in a cabin in the Yukon. The variation in interactions is called heterogeneity – uneven mixing. Uneven mixing lowers HIT. A lot. To assume mixing is equal across all people in Canada is the absolute worst-case scenario mathematically possible.
There are various ways to model heterogeneity, but Dr. Tam’s group explicitly ignores its existence in a government model intended to guide policy[xvii]. They have decided to model only the worst case. Heterogeneity lowers R0 over time as highly interactive individuals spread the virus early and then become blockers – slowing the spread and lowering R0 and R(t). This is one large factor why Sweden[xviii] and other places have reached HIT when looking at their spread at far, far lower levels than this misguided Canadian model.
Heterogeneity is easily evidenced and can be partially quantified by the far higher spread in cities vs rural settings all over the world[xix],[xx]. Not accounting for these concepts – which are easily incorporated – is a breach of trust to Canadian citizens relying on knowledgeable health experts to provide accurate information.
Another related factor is T-cell immunity, a growing and popular area of research. It is not without contestation that Sars-Cov-2 is NOT “novel”; i.e. no one has existing defenses[xxi],[xxii],[xxiii].
- In February (Singapore), Sars-Cov-1 patients showed 100% immunity to Sars-Cov-2 despite being infected 17 years ago.
- We know that common cold coronavirus is cross reactive to Sars-Cov-2 initiating a T-cell response and destroying the virus[xxiv].
- T-cell protection does not create IgG antibodies (what antibody tests measure), but IgG antibodies create long term T-cell protection in at least 83% of cases. Antibody decay translates to long term immunity[xxv],[xxvi].
- T-cell protected persons get the virus but almost always fight it off. They show positive on PCR test but not antibody tests. Studies show on average 1.8x as PCR positive but antibody negative – meaning the virus has spread possibly 1.8x more than antibody tests alone imply. This translates into lower IFR; meaning the virus is even far less deadly than the flu.
The new government model does not even bother to address to existence of T-cell immunity despite its widespread acceptance in the medical community – which further compounds the inaccuracy of the model used to derive policy.
These new model outcomes have no basis in reality and should not be used for policy planning. Better and more accurate models do exist, but it is unclear why the Canadian government does not use them. This new model is beyond worst case – it is an impossibility like the models before it. It is intended only as a counterfactual. Furthermore, it has been paid for by Canadian taxpayers, who’s trust has been which depend on accurate information. Although I would prefer it were not true, I believe the model is being used purely as a preplanned counterfactual defense to Dr. Tam and her group’s expensive and mostly ineffective policy actions.
The most likely outcome in Canada assuming no lockdown is 10-15% antibody spread or 18-28% true spread including T-cells and about 4,000-8,000 non-LTC residents fatalities from Sars-Cov-2 (government estimate in April – 300,000). It is unclear that any interventions beyond full lockdowns have any material effect to slow viral spread; and full lockdown have tremendous cost. In fact, it’s very debatable that lockdowns have any net positive effect on fatality. The idea of ‘better safe than sorry’ policies undertaken not just by Canada, but other countries, are starting to show irreparable damage to citizens. This could be due to damage to the economic livelihoods of the citizenry, increases in mental illness, drug abuse, child abuse, incremental global famine, child development, etc[xxvii]. This is largely due to poor information communication, lack of education on the subject matter, and a lack of putting statistics into real world context. This only instills fear which can illicit irrational, sometimes dangerous behavior by citizens. I need not get into examples of what fear and irrational behavior can do within a society historically as there are countless amounts of them[xxviii]. To put it into graphical context, Franklin Templeton put out a survey to gauge fear of death from Sars-Cov-2 among all age groups.
Is this rational thought? Is this how we want people to live their daily lives? Between the ages of 18-64, there are a great many other things that have a higher chance of causing death outside of Sars-Cov-2. Not to mention people who are already struggling hard with mental illness. Many people who struggle with addiction depend so much on having structure, going to school/job, having hobby’s, meeting with friends etc. Video conferencing does very little for those who struggle with addiction. By enforcing isolationist policies, the biggest support of having ‘normality’ in their daily lives is eliminated and thus take a part in destroying the foundation of any form of happiness. What if they also have families, what if the person they depend on for their livelihood is the one that struggles with addiction? There are an estimated 2 million people who subscribe to Alcoholics Anonymous[xxix], and these are those who admit that they have a problem. If even 10% of them completely lose control of their lives because of these ill-conceived policies, that’s 200,000 people minimum who have their livelihoods destroyed with very little means to recover.
Granted, masks, basic social distancing, hand washing all may have an effect, but they appear to be less effective than we have been led to believe by the Canadian government. Most spread can be explained be reasonable heterogeneity models and T-cell immunity
The single best NPI the Canadian government can do is to open borders with no restriction to herd immune countries (Sweden, US, India, Mexico, France, and Brazil among others). Canada will import lots of immune “blockers” and almost no live infections. These blockers will serve to reduce R0 and R(t) – a concept easily modeled. This single action is an order of magnitude more helpful in slowing spread permanently than masks, further lockdowns or even handwashing. It is permanent and has the effect of positive economic and social benefit (all other NPI’s are varying degrees of negative).
We should all implore Dr. Tam and our highly compensated health experts to incorporate widely available empirical evidence to provide projections that accurately represent the risk of Sars-Cov-2 to Canadians. Its very probable that the true outcome of such work will demonstrate the risk from Sars-Cov-2 was not and is not severe, outside of nursing homes. The work is also likely to show all the interventions, costs, and fear to slow its inevitable spread was not necessary. Yes, it would be a devasting blow to Dr. Tam and our government’s reputation, but the good of Canadians is what matters. The current model has no basis in reality and has constituted a breach of the trust placed in Dr. Tam by the Canadian citizenry.
I will reiterate, I prefer it were not true, but building such an obviously counterfactual model so Dr. Tam can later point to the outcomes being better than the model and say “see, I saved lives” seems to be the only point of the modelling exercise. This serves nothing but to instill unwarranted fear in the citizenry and provide a façade of competency in government policy.
Its disappointing that a knowledgeable individual such as Dr. Tam, whose expertise include infectious disease, would allow this model to be released.