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).
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The x-intercept is the inverse of the vaccine effectiveness against transmission (VET); a VET of 90% gives x-intercept of 111%.
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The different solid lines are expectations from the Doherty Institute modeling for re-opening. "High" PHSM is roughly Stage 4 lockdown, "Medium" is Stage 3, and "low" is roughly Stage 2 (non-lockdown).
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It seems Vic and NSW are meeting expectations for "high" PHSM and partial test/trace/isolate/quarantine (TTIQ; "partial" expected for high cases), but without lockdown.
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Many possible reasons for this, all interesting:
1) There's something wrong with the data. I can't rule this out. One possible factor which is skewing the data is that cases in vaxxed are more likely to be asymptomatic and not reported.
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This is a linear effect on cases, and R_eff measures the exponential slope of cases vs. time. However, when the exponential slope is small (near R_eff = 1) corrections could matter a lot.
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But, if (1) is the *entire* reason we see an apparent R_eff<1 when it is really >1, that means cases are growing (exponentially!) and we don't know. Seems unlikely, especially as vax is saturating and the ratio of asymptomatic/symptomatic stops changing.
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2) Lockdowns are simply *much* less effective than thought.
This seems also seems unlikely; it is counter to the experience of Vic 2020 and many jurisdictions.
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3) Restrictions can be removed on vaxxed with little consequence. (I.e. lockdowns are effective for unvaxxed, but don't matter for vaxxed.)
Surprising if true. VET is accounted for in the plot, and isn't perfect; why does removing restrictions on vaxxed not matter?
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4) PHSMs and vaccination aren't independent multiplicative factors. (Similar to #3.)
Again, it's hard to understand exactly how this works. We think we roughly trust the endpoints R_eff(0 vax) and VET, implying that something odd happens in the middle.
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It's a riddle.
Covid is full of surprises.
The Doherty Institute model predicted well (particularly for NSW) the point at which vaccination would bring R_eff = 1 in lockdown. How can it predict so poorly the effect of lifting restrictions?
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- 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).
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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.
First, comparing the Punaha Matatini and Doherty models is easy. They use very similar methodology. The contact matrix is the same, taken from this paper: journals.plos.org/ploscompbiol/a…
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What that means is that in both models children and the elderly contribute relatively little to transmission, which is driven more by working-age people.
The model from @TonyBlakely_PI of the Population Interventions Unit, released yesterday, comes to some surprising conclusions, for example that Stage 4 lockdowns would continue to be necessary even if 95% 16+ are vaccinated.
I’ve attempted to summarize the differences between the model released yesterday by Melbourne Uni’s Population Interventions Unit (PIU) and the modelling by the Doherty Institute for the National Plan.
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PIU provide a very nice web interface that allows the user to explore the effect of different scenarios on the model outcomes. I encourage you to have a look!
Today we’ll look around the world at countries which have had success at suppressing covid, the delta strain in particular, and see what lessons there might be for Australia.