They modeled three scenarios, lifting restrictions partially at 70% vax with no further lifting, lifting restrictions in two stages at 70/80%, and waiting until 80% to lift the same restrictions. Additionally they modeled the effect of enhanced contact tracing in each case.
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NSW followed something like Scenario 3 (lifting restrictions at 70/80%, no enhanced contact tracing). Opening happened somewhat earlier than modeled, 11/18 Oct, rather than 18 Oct/5 Nov.
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OzSAGE predicted that 34 days after 80% re-opening, NSW ICUs would enter five weeks of "code black" w/626 covid patients + 300 other patients occupying all ICU beds, eventually peaking at 892 covid ICU patients.
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It's been 35 days since NSW's 80% re-opening. How are we doing?
Here's the actual hospitalization (red) against the predictions from @RealOzSAGE (purple is scenario followed):
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...and the actual ICU utilization (red) against the predictions from @RealOzSAGE (purple is the scenario followed):
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34 days after re-opening @RealOzSAGE predicted ICU utilization of 626 beds.
Observed utilization was 30 beds, more than 20X lower.
The largest difference between scenarios was only a factor of 3.4; the error is 6 times larger than the effects they are trying to model!
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This isn't terribly surprising; I noted at the time of release that the model was poorly calibrated, and the choice of parameters appeared arbitrary and not based on any objective inputs.
This doesn't mean that all modeling is garbage. There are some carefully calibrated models that are have become reasonably predictive especially in the short term.
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But anyone can cook up a model and write a press release for it. So we do need to consider the track record of the modelers. In this case they failed. So we should approach their future work with caution.
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Continuing to platform poor modeling is counter-productive. It erodes trust in science by the public, and can backfire, leading to the public ignoring *every* message from public health experts.
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The irony is that cases in Greater Sydney *are* showing the first signs of an increase now. This is worrying, and we should be debating how to respond. We need accurate, trusted, objective advice from experts.
...the NSW statewide data hide the fact that Greater Syd is showing R_eff > 1. (Here's a plot from @Chrisbilbo.) This is masked by a decline in cases outside Greater Syd.
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It is looking more and more like R_eff will come to rest just above 1 even as vaccination saturates at ~80% of total pop in Vic and NSW.
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I'm pro-vax, I'm pro-science, I don't like covid (who does?), and I don't want kids getting covid (who does?)
But vaccinating 5-11s is not a simple issue.
Here's the summary of the FDA's risk/benefit analysis is support of the EUA request for Pfizer for 5-11s in USA:
Thread 1/
The FDA's risk-benefit analysis looked at different scenarios of covid incidence, from the lowest point in June 2021 (about 35 cases/Mpop/day) to the delta peak in September (about 500 cases/M/day).
It's early days, but it seems that something quite unexpected is happening here. Many restrictions have been lifted in Vic and NSW, and by and large nothing has happened.
By "nothing", I mean that (apart from a bump in cases in NSW largely outside greater Sydney), the trend of the effective reproductive number (R_eff) vs. vaccination has followed the same track as during restrictions.
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The sloping lines on this plot are the expectations for vaccination effect on R_eff. Their y-intercepts are the R_eff expected with zero vaccination, which should vary with the level of public health and social measures (PHSMs).
- 1-dose vaccination is 2/3 as effective as 2-dose (consistent with estimates used in Doherty Institute report)
- Vaccination becomes effective after two weeks and does not wane
- All of the population (ages, regions, etc.) is equal in terms of transmission.
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- Vaccination is the only effect on R_eff which is changing in time during this period (9/8/2021 to 22/10/2021 in Vic, 26/6/2021 to 11/10/2021 in NSW).
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NSW and Vic show excellent evidence that vaccine effectiveness against onward transmission is high (>86%)!
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I fit the R_eff vs vaccination data for NSW and Vic to a linear relationship, to get two parameters, the R_eff at zero vax, and the vax effectiveness against onward transmission (VET). The result:
NSW: R_eff(0 vax) = 1.65; VET = 86.1%
Vic: R_eff(0 vax) = 2.27; VET = 86.4%
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Solid lines are the Doherty model, linearized:
Doherty uses a transmission matrix which effectively weights some ages more than others in relevance to transmission. I assume vax affects everyone equally. I take a weighted average of VET = 89.7% for AZ (86%) and Pfizer (93%).
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In a recent thread I looked at the performance of some low-covid countries against expectations from models of the expected R_eff achievable at different vaccination levels.
Today let’s examine how jurisdictions in Oceania are doing.
The effective reproductive number R_eff controls whether infections grow (R_eff > 1) or decay (R_eff < 1). We therefore need to achieve R_eff < 1 to have control over the epidemic with our public health and social measures (PHSMs).
2/🧵
The most important question then, is:
👉"Under what conditions of PHSM and vaccination can we achieve R_eff < 1?"👈
I’ll be plotting R_eff as a function of the effective vaccination expressed as a percentage of total population.