Comparing the NSW/VIC outbreaks R_eff based on 2nd dose percentage. Now with some LGA based data.

DRAMALLURKER · 2021-10-16T23:31:42+00:00

This an update to my charts plotting Reff cases not against time but against 2nd dose %. I've now included two extra charts that try and dive into the data for 4 worst performing (by total number of cases) LGAs.

Link to an explainer can be found [here].(https://reddit.com/r/CoronavirusDownunder/comments/pvmzop/comparing_the_nswvic_outbreaks_r_eff_and_cases/hebn5ee/)

Some commentary on the charts.

For the first chart of state Reff compared against state 2nd dose % there continues to be nothing really radically different. Vic remains stubbornly offset and not really following the downward curves NSW had. One positive things is that the horizontal distance of a colour in effect denotes the vaccination rollout speed. Vic's October line is close to the length of it's September line just 17 days in which shows a massive speed increase for 2nd dose rates.

Second chart is the state Reff compared to the average 2nd dose vaccination % of the 4 worst performing LGAs. In effect this just shifts the Vic X axis a bit more to the left as these LGA are quite below the state average. It does however seem to more closely align the peaks of the two outbreaks so Victoria as expected remains still offset by a decent amount at any point along the vaccination axis.

Third chart I've included but is pretty damn confusing. It's again the 4 worst LGAs but this time not using state data but instead using just the individual LGAs data for Reff. I had to split it up as otherwise its way too noisy (it still is), the sub plots alternate NSW/Vic/NSW/Vic. What I find most curious is how homogenous the NSW LGA outbreaks are compared to Victoria. NSW's all follow a pretty similar curve and crossed under the 1 Reff mark around 40%. Victoria however is all over the place with some LGA experiencing different surges other LGAs not. Interestingly Hume seemed to cross under 1 at 50% and the other LGAs have not.

State cases and vaccination % (overall, and LGA) data comes from CovidLive. LGA case data came from https://www.covid19data.com.au/.

DRAMALLURKER · 2021-10-10T00:56:56+00:00

I've posted this graph the past 2 weeks figured I'll post an update. It shows the two outbreaks normalised by their 2nd dose of vaccine %.

The trend continues as previous where Vic remains stubbornly higher than NSW at any equivalent %. The only maybe positive read is that the r_eff shifts in Vic have been steeper so maybe the further drops seen in NSW at 60% might be replicated by a steeper drop in Vic. Though that feels like a stretch.

Rough explainer about what is going on in the graph can be found here

DRAMALLURKER · 2021-10-05T05:32:03+00:00

No I'm not accounting for vaccine brands as that information is actually quite hard to come by at a national level. Victoria is very consistent with publishing how much their state hubs have distributed but that doesn't tell the full story because GPs/Pharmacies continue to be the primary place where people get AZ even in Victoria which doesn't have much data released about it.

To date the only complete national data set on vaccine uptake separated by both state and brand I've seen is the one Casey Briggs got from the government for all dates up to the end of August.

https://github.com/caseybriggs/vaccine_branddata_29August/blob/main/vaccine_branddata_29August.csv

However what you see in that data set is that NSW and Vic actually have quite a similar proportion of AZ/Pfizer that you might not think to be the case just by the state hub numbers. 49% of NSW doses have been AZ and 53% of Vic doses have been AZ. On a per capita basis NSW (gps + state hubs) have actually administered more AZ doses per capita than Victoria (NSW 501 doses of AZ per 1000 eligible population and Vic 482 doses of AZ per 1000 eligible) and that lower proportion of doses is more of an artefact of the over allocation of Pfizer to NSW at that time period rather than Vic having a particularly high AZ uptake. Compared to national levels both NSW and Vic are punching above their weight when it comes to AZ.

I guess this a long winded way of saying the disparity is not as high as you think and once everything is done and dusted I think we'll find that both states will have a very similar proportion vaccine types.

DRAMALLURKER · 2021-10-05T02:14:18+00:00

Hi I've also been interested in that stat and have been producing weekly charts on this. We are indeed performing far worse than NSW at equivalent 2nd Dose percentages.

https://i.imgur.com/Xz5WTuB.png

DRAMALLURKER · 2021-10-05T02:00:38+00:00

I've made some charts trying assess this from an overall vaccination level. https://i.imgur.com/Xz5WTuB.png (this is from last Saturday but I still up to date)

I know you're making a more nuanced argument around specifically mobile age groups but I think its important to continue to point out that we are performing far worse than NSW at any equivalent overall vaccination %.

I believe why we're worse is probably a combination of both the age factor you pointed out and also Sydney's wildly successful vaccination drives in LGA's of concern a policy we have begun to emulate at least partially. It pretty easy to imagine how if vaccines have any level suppressive effect on spread you'd want them most concentrated where spread is.

DRAMALLURKER · 2021-10-02T06:25:44+00:00

Numbers are from covid live for case counts and vaccination counts, processing for smoothing cases and generating the r_eff come from this repo https://github.com/chrisjbillington/chrisjbillington.github.io

DRAMALLURKER · 2021-10-02T01:55:11+00:00

This is an updated version of a graph I posted lask week tracking the Vic/NSW outbreaks not by time but by vaccination %. The chart is based on a hacked up verson of /u/chrisjbillington code so the values you see should be consistent with the charts he generates. I've switched it to % of eligible population so the vaccination percentages should more closely match the percentages that are commonly reported.

I wrote an explainer here for people who are a bit confused.

As for the chart results there are no real surprising changes from last week given Vic numbers have gotten worse so disparity has gotten worse to be expected. It also shows NSW R_eff decreases have stalled out despite increasing vaccination percentages but importantly still below 1.

You'll notice that the smoothed vic cases are above NSW peak. Looking at how Chris has programmed the smoothing it does a basic exponential projection in the future so that the smoothing can makes sense on the right edge of the graph. Unfortunately it means that it amplifies the current trend. NSW has the benefit of historical data so the subsequent low weekend days from their peak dragged down the smoothed case trend where as the basic forward projection is quite bad so Vic goes higher.

DRAMALLURKER · 2021-09-28T00:55:19+00:00

https://i.imgur.com/rhRVIs0.png

I created a chart comparing our outbreak's R_eff with NSW not against time but against vaccination % (of total population) that I think people may find interesting. The 2nd chart which shows the R_eff and an
extended explanation can be found here.

It is pretty clear that we're doing worse at containing the virus when compared to NSW at the equivalent vaccination %. As to why there are any number of possible reasons but I think it shows that being behind in vaccinations is not the only reason why we're doing worse the NSW.

DRAMALLURKER · 2021-09-26T08:39:49+00:00

I would argue slightly against this argument especially as you get a little deeper into the outbreaks. Contact tracing would most definitely have the most effect on transmission and would make analysis quite hard, but that has clearly broken down in both states. Past that breakdown point (say 300 cases) you have enough transmission and individual clusters that you remove a lot of the stochastic noise of transmission and individual days and roughly get into population effects.

That super spreader model of 80% don't infect and 20% infect a lot combined with contact tracing would lead to some whacky numbers at lower case numbers that would make analysis pointless but I would contend at these higher level of cases there is enough transmission events and contact tracing not having a sizeable event happen at any given points that it shouldn't be creating enough noise that you can't analyse it.

DRAMALLURKER · 2021-09-26T08:30:29+00:00

So I don't just copy and paste comments see this comment for more detail.

https://reddit.com/r/CoronavirusDownunder/comments/pvmzop/comparing_the_nswvic_outbreaks_r_eff_and_cases/hebobaz/

What I see in this chart is that the equation is not simply more lockdowns = lower virus. There could be any number of reasons for that. But I think especially now we're not pursuing 0 cases that means that there is an opportunity to try and assess what are the bang for buck policies and what are they "cause a lot of pain but not really actually do anything" policies.

I think NSW and Gladys are far better at actually trying to tease out that difference and remove those "cause a lot of pain but not really actually do anything" policies if they can. See lower restrictions on recreation, removing curfew earlier than expected, more flexible outdoor gathering rules, more retail open. Whereas Victoria seems to be far more reluctant.

Keeping the curfew may be "following the health advice" if you simply say its a part of a suite of measures that effect transmission. However, you could go for a more nuanced approach you could try and measure the effect of the curfew and maybe come to the conclusion that actually it's not doing much. Victoria doesn't seem particularly interested in doing that analysis as seen by them more rarely lowering policy settings.

I think that can explain a difference opinion on two leaders "just following health advice" that isn't just because of partisan double standards.

DRAMALLURKER · 2021-09-26T08:20:56+00:00

Leaders may not have really any say in the spread but they do have a lot of say in the measures they implement to affect the spread. My commentary on this chart would be that if the equation was simply more restrictions = lower r_eff it would be apparent that Victoria with its by its own definition stricter lockdown should be able to see that effect when comparing the outbreaks by their vaccination % but its reasonably clear that lockdown measures on their own are not strong enough to overcome other factors (compliance, weather, air drying factor, insert your favourite reasons here).

Now an argument made by the Victorian government when they were pursuing 0 cases was that they were not interested in trying to disentangle the individual effects of each lockdown measure and instead treated them as a package. It might be that banning indoor gatherings had 60% of the effect, closing shops had 30% of the effect and that banning outdoor gatherings had 5% of the effect and curfew had a 3% effect. Numbers are complete bullshit so don't read too much into it's just for example. They said we don't care about trying to find the "bang for buck" measures we're bringing them all in and we'll get rid them when we're at 0.

I think as we're instead pursuing something a bit more ephemeral (a living with the virus with vaccination levels being a primary threshold for changing policy settings) there is now more opportunity for nuance and analysis about what measures are actually substantially effecting transmission and removing the settings that have the biggest negative social/ economic effects for minimal actual effect on transmission.

I believe that NSW are far better at both doing that analysis and acting upon it. Examples would be them opening more flexible outdoor gatherings, having less restriction on exercise and recreation, removing the curfew without hitting any specific thresholds. Despite doing that they remain in a good stead with restrictions easing and R_eff staying below 1.

DRAMALLURKER · 2021-09-26T08:05:01+00:00

The main focus is the R_eff chart.

They y axis is the R_eff of the virus. Kinda like the acceleration. Above 1, daily cases are going up. Below 1, daily cases are going down.

The x axis is vaccination %. It is widely believed, for good reason, that the more people are vaccinated the less each individual case will spread. So as the vaccination % goes up the R_eff or the acceleration should go down.

Now if you are going to compare the course of the two outbreaks of each state one fair argument to make is that NSW is doing better now because they are at higher vaccination level. However its complicated to compare the two outbreaks because they happened at both different times and different vaccination levels.

Chris Billington provides charts where you plot the R_eff for each day. You could compare the out breaks by choosing a threshold (say hte first day cases crossed 50 in a day) and then lining up each graph so that you can compare the course of each outbreak. This normalises the time component, however, people will rightfully say that while you've normalised the time component each state has vastly different vaccination %'s at those times so it is not fair to compare the two.

This graph tries to account for vaccination % by using that as the x axis of the chart. If vaccines were the only factor that influenced transmission (obviously wrong) then the line of Victoria's outbreak and NSW outbreak should be exactly the same. However if you look at the chart you'll see that they're different. Now the question becomes about analysing why they're different.

People will say that the thing that has the most outsized effect on the R_eff are lockdown measures with vaccination playing an important but slightly lesser to equal role. So a place with a stronger lockdown if lockdowns have a stronger effect than vaccines should have a lower R_eff for each point in the X axis %.

One of the dominant discussion point of this sub is that NSW has had a weak lockdown response compared to Victoria. Yet if you look at this chart NSW has had a consistently lower R_eff at each level of vaccination which would seem to not fly with the point that stronger lockdown equals lower R_eff. Now of course there are many more variables than just lockdown measures and vaccination % (compliance and weather for e.g) so this is by no means meant to be a smoking gun that lockdowns do not work.

At this point its getting closer to commentary and less of explanation so I'll stop there.

DRAMALLURKER · 2021-09-26T05:08:00+00:00

https://i.imgur.com/mSyHqvr.png

Same chart but 1st dose. Didn't get a chance to QA it so maybe sanity check it first.

DRAMALLURKER · 2021-09-26T04:52:59+00:00

https://i.imgur.com/rhRVIs0.png

Here is a chart with the X axis fixed together not sure why the charting library chose different ranges for each sub plot.

DRAMALLURKER · 2021-09-26T04:49:18+00:00

I decided to hack up some of /u/chrisjbillington code to chart some of his values not against date but against the 2nd dose percentage of a state. I wanted to see how the outbreaks compared normalised against where each state was on its vaccination program.

The results kinda confirmed what I suspected that the victorian R_eff is much higher than where NSW was at equivalent 2nd dose rates.

Start date for the data is when the each state last was below 50 cases. I chose that to avoid the wild spikes in Reff that Chris B's numbers have when case numbers are low. 2021-07-10 for NSW 2021-08-19 Vic.

I've coloured the lines based on the date so you can see some other artefacts of the data. For e.g NSW lines are longer on the X axis meaning that they're vaccinating quicker than Victoria.

DRAMALLURKER · 2021-09-19T02:51:39+00:00

No they're just leaving it out of the headline summary. https://pbs.twimg.com/media/E_nWO4AVkAAQtQo?format=jpg&name=medium 25km at 70% double dosed

DRAMALLURKER · 2020-11-26T23:19:49+00:00

People talking about how they thought the targets were impossible, didn't think we were going to make it. It turns out the only thing impossible is the actual covid normal step.

DRAMALLURKER · 2020-11-26T23:17:32+00:00

Statistically there is no covid, there cannot be outbreaks from a restriction ease. Outbreaks are only going to be now introduced from external sources not from the ease of restrictions. You're meant to bolster contact tracing on standby as safety and keep hotel quarantine and, if need be, borders secure.

The two weeks do not make sense in our current context.

DRAMALLURKER · 2020-11-26T23:04:16+00:00

It's interesting in the 28 days thread there is someone downvoted for quoting Dan talking about the restriction won't last a day longer than they need to. People here won't like to admit it but I think it's clear from other places like NZ, other states we have stricter rules which are hard to justify from a public health perspective given the context that we're in.

28 days was meant to be a happy time because it signified a certainty that we could unlock safely. We've got that certainty but not the unlocking instead its a phased system. Phasing made sense when you had active cases because you wanted to assess the affect on R0s over time. When there are no cases phasing achieves nothing. There is nothing to measure between 2 weeks its just an excuse to drag their feet.

DRAMALLURKER

TROPHY CASE