Here are the final observations for R_eff vs. effective vaccination rate during Vic and NSW lockdowns.
Vaccine effectiveness against transmission (VET) with standard errors:
NSW: VET = (86.1 ± 6.5)%
Vic: VET = (90.1 ± 16.2)%
Methods/notes/assumptions below.
1/
Vaccine effectiveness against transmission (VET) with standard errors:
NSW: VET = (86.1 ± 6.5)%
Vic: VET = (90.1 ± 16.2)%
Methods/notes/assumptions below.
1/
https://twitter.com/MichaelSFuhrer/status/1448795988122955790
I assume:
- 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/
- 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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3/
Methods:
- R_eff is calculated from a linear regression of ln(new cases) over 8 days (4 days in past to 3 days in future of date of R_eff.
- Case and vaccination data from @covidliveau.
- VET is the inverse of the x-intercept of a linear regression of R_eff(vax).
4/
- R_eff is calculated from a linear regression of ln(new cases) over 8 days (4 days in past to 3 days in future of date of R_eff.
- Case and vaccination data from @covidliveau.
- VET is the inverse of the x-intercept of a linear regression of R_eff(vax).
4/
Discussion:
- Both vaccination and transmission are inhomogeneously distributed across the population which is likely important! Targeting vaccination to high-transmission groups (age groups, or regions) will cause VET to be overestimated.
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- Both vaccination and transmission are inhomogeneously distributed across the population which is likely important! Targeting vaccination to high-transmission groups (age groups, or regions) will cause VET to be overestimated.
5/
- The age-based roll-out means working-age adults (likely more highly transmitting) were vaccinated later, and this would cause VET to be overestimated.
- Likewise, children <12 (likely less transmitting) were not vaccinated; this also exaggerates VET.
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- Likewise, children <12 (likely less transmitting) were not vaccinated; this also exaggerates VET.
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- "lockdown fatigue" could increase R_eff w/time, leading to an underestimate of VET.
- other changes could be important (e.g. changing weather/seasons, or small changes in public health measures).
- Waning vax effectiveness means VET may be lower than peak after vax.
7/
- other changes could be important (e.g. changing weather/seasons, or small changes in public health measures).
- Waning vax effectiveness means VET may be lower than peak after vax.
7/
- VET is consistent with 86%-93% assumed for AZ/Pfizer by Doherty Institute.
- HOWEVER, due to effects discussed above, I think my VET is more likely an overestimate! Still, I think the observations provide good evidence that vax is effective at reducing transmission.
8/
- HOWEVER, due to effects discussed above, I think my VET is more likely an overestimate! Still, I think the observations provide good evidence that vax is effective at reducing transmission.
8/
Interesting observations:
- Both NSW and Vic appear to show a higher VET (higher slope) just before crossing R_eff = 1. This was noted at the time (e.g. @Chrisbilbo) in that it caused models to overestimate cases, and overestimate date when peak cases would occur.
9/
- Both NSW and Vic appear to show a higher VET (higher slope) just before crossing R_eff = 1. This was noted at the time (e.g. @Chrisbilbo) in that it caused models to overestimate cases, and overestimate date when peak cases would occur.
9/
- This apparent higher slope just before R_eff = 1, if real, happens at different point in the vaccination rollout in Vic and NSW, and hence is unlikely to be attributed to e.g. vaccination of certain groups, or AZ vs. Pfizer.
10/
10/
- Vic has consistently shown higher R_eff at a given vax level than NSW.
The cause is not known, but it is NOT a function of vaccination rollout, which is corrected for in this plot! Possible differences are climate, "lockdown fatigue", or LGA-specific settings in NSW.
11/
The cause is not known, but it is NOT a function of vaccination rollout, which is corrected for in this plot! Possible differences are climate, "lockdown fatigue", or LGA-specific settings in NSW.
11/
- Vic data are also "noisier" (progressing less smoothly in time) leading to larger error in VET estimate for Vic.
12/
12/
Conclusions:
Vax effectiveness against transmission (VET) is evident in the statistically significant decrease in R_eff with vaccination during lockdown in NSW and Vic.
A simple model gives high VET approx 80-90%, however this is likely to be modestly overestimated.
13/13
Vax effectiveness against transmission (VET) is evident in the statistically significant decrease in R_eff with vaccination during lockdown in NSW and Vic.
A simple model gives high VET approx 80-90%, however this is likely to be modestly overestimated.
13/13
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