NSW R_eff as of December 16th with daily cases and restrictions. Latest estimate: R_eff = 3.24 ± 0.63

Cases shown on both a linear scale (first image) and log scale (second image).

More info chrisbillington.net/COVID_NSW.html

#covid19nsw #covidsydney ImageImage
R_eff in Greater Sydney vs rest of New South Wales:

Greater Sydney: R_eff = 1.45 ± 0.13
NSW excluding Greater Sydney: R_eff = 1.81 ± 0.32

(Cases shown on a log scale)

(note region-specific data is several days old, does not include today's cases)

#covid19nsw #covidsydney ImageImage
R_eff in LGAs of concern* vs the rest of NSW (*all of Penrith included):

LGAs of concern: R_eff = 1.38 ± 0.14
Rest of NSW: R_eff = 1.65 ± 0.17

(Cases shown on a log scale)

(note LGA data is several days old, does not include today's cases)

#covid19nsw #covidsydney ImageImage
The Hunter region: R_eff = 3.02 ± 1.40

Illawarra region: R_eff = 1.27 ± 0.38

Western New South Wales: R_eff = 1.07 ± 0.50

(Cases shown on a log scale)

(note region-specific data is several days old, does not include today's cases)

#covid19nsw #covidsydney ImageImageImage
Expected numbers if the current trend continues:

day cases 68% range
Fri: 3484 2197—5044
Sat: 5027 2940—7831
Sun: 7265 3919—12189
Mon: 10497 5210—18896
Tue: 15080 6956—29336
Wed: 21675 9245—45465
Thu: 31063 12228—69972

Doubling time is 2.9 days.
Note that there was no LGA dataset released yesterday, so the regional breakdowns are more out of date than usual - three days behind the statewide figures.

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More from @Chrisbilbo

18 Dec
Figured out why my Hunter region R_eff calculation had zero uncertainty. I was literally clipping R_eff values to the "sane" assumption that it would be less than 10 in the uncertainty calculation. Some other clipping for the sake of "sanity" was also limiting the central value.
What used to be insane now isn't. Here's what the Hunter looks like with none of this clipping. Image
The clipping was there for a reason, sometimes small numbers and bad luck does produce stupid R_eff estimates. My code might now say stupid things in those contexts instead. But won't have to worry about that until numbers are smaller.
Read 4 tweets
17 Dec
They're the experts, but I think ATAGI is making the wrong call. There is a persistent bias in health regulation toward inaction in uncertain times, as if regulators are somehow less responsible for the consequences of inaction than of action. 1/🧵
Much in the article emphasises uncertainty. But we need to make judgement call with the information available. Just because of uncertainty doesn't mean the best option is doing nothing. There's no reason to privilege that course of action over any other. 2/🧵
Just as there are considerable uncertainties about the negative consequences of bringing forward vaccines, there are considerable uncertainties about the negative consequences of not doing so as well. 3/🧵
Read 4 tweets
17 Dec
NSW R_eff as of December 18th with daily cases and restrictions. Latest estimate: R_eff = 3.35 ± 0.25

Cases shown on both a linear scale (first image) and log scale (second image).

More info chrisbillington.net/COVID_NSW.html

#covid19nsw #covidsydney ImageImage
R_eff in Greater Sydney vs rest of New South Wales:

Greater Sydney: R_eff = 2.37 ± 0.27
NSW excluding Greater Sydney: R_eff = 8.55 ± 0.78

(Cases shown on a log scale)

(note region-specific data is several days old, does not include today's cases)

#covid19nsw #covidsydney ImageImage
R_eff in LGAs of concern* vs the rest of NSW (*all of Penrith included):

LGAs of concern: R_eff = 1.92 ± 0.22
Rest of NSW: R_eff = 5.57 ± 0.50

(Cases shown on a log scale)

(note LGA data is several days old, does not include today's cases)

#covid19nsw #covidsydney ImageImage
Read 7 tweets
16 Dec
NSW R_eff as of December 17th with daily cases and restrictions. Latest estimate: R_eff = 3.52 ± 0.33

Cases shown on both a linear scale (first image) and log scale (second image).

More info chrisbillington.net/COVID_NSW.html

#covid19nsw #covidsydney ImageImage
R_eff in Greater Sydney vs rest of New South Wales:

Greater Sydney: R_eff = 1.76 ± 0.17
NSW excluding Greater Sydney: R_eff = 7.22 ± 1.01

(Cases shown on a log scale)

(note region-specific data is several days old, does not include today's cases)

#covid19nsw #covidsydney ImageImage
R_eff in LGAs of concern* vs the rest of NSW (*all of Penrith included):

LGAs of concern: R_eff = 1.51 ± 0.15
Rest of NSW: R_eff = 4.16 ± 0.56

(Cases shown on a log scale)

(note LGA data is several days old, does not include today's cases)

#covid19nsw #covidsydney ImageImage
Read 7 tweets
16 Dec
Hi all,

You might have noticed my short-term projections for the NSW outbreak yesterday were pretty nuts. It turns out this projection was unrealistic, due to some approximations in my code that are no longer valid now that growth rates are higher. 1/🧵
So first, apologies. These issues are fixed now and today's projection ought to be more realistic. Secondly, gory details below. There were two problems. 2/🧵
Problem 1. smoothing. Many have noticed my "smoothed cases" line didn't actually go through the middle of the data. This is not incorrect per se, this is what correct smoothing with a symmetric smoothing window looks like when the data has a nonzero second derivative. 3/🧵 Image
Read 16 tweets
1 Oct
VIC R_eff as of October 2nd with daily cases and restrictions. Latest estimate: R_eff = 1.53 ± 0.09

Plus projected effect of vaccination rollout.

Cases shown on a linear scale (log scale in next tweet).

More info chrisbillington.net/COVID_VIC_2021…

#COVID19Vic
VIC R_eff as of October 2nd with daily cases and restrictions. Latest estimate: R_eff = 1.53 ± 0.09

Plus projected effect of vaccination rollout.

(Cases shown on a log scale)

More info chrisbillington.net/COVID_VIC_2021…

#COVID19Vic
Expected numbers if the current trend continues:

day cases 68% range
Sun: 1591 1327—1863
Mon: 1739 1409—2100
Tue: 1896 1485—2367
Wed: 2068 1558—2644
Thu: 2246 1635—2952
Fri: 2433 1706—3288
Sat: 2631 1770—3655

Doubling time is 8.2 days.

#COVID19Vic
Read 7 tweets

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