...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.
3/
What happens next?
- Additional lifting of restrictions in Vic, increased travel, etc. haven't been felt yet, so R_eff may go higher.
- Waning vax effectiveness is hurting, but eventual vax of 5-11s, and boosters, will help.
5/
But ultimately, it's not at all clear at this moment that R_eff won't be above 1 through the summer. If so, there are very few levers left to stop the outbreak. There is no political will to reimpose restrictions. There is little room for more gains in vaccination.
6/
Fortunately high vaccination and falling cases have already built in some cushion in the health care systems in NSW and Vic (though Vic is more worrisome).
7/
Sorry, no conclusions, just observations and predictions. I think we're heading toward "living with covid" in Australia. That's what I had assumed for quite some time, but recently had become more optimistic about keeping covid low with high vax. I'm less optimistic now.
8/
The vaccines are very effective at reducing the severe disease burden of covid, and we've done well to vaccinate a very high proportion of our population, and keep covid incidence low until we got there.
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P.S. The @TheDohertyInst report has held up very well. It implied that vaccination of 80% of total population, plus partial TTIQ, would be marginal to contain covid (achieve R_eff < 1). That appears correct.
10/10
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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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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.
2/🧵
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.
2/
- 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%)!
1/
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%
2/
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%).
3/
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.