I think Xmas hosed some things. I know our health system stopped vaccinations on Xmas Eve and don't think they resumed until today but I could be wrong there. They might have picked up over the weekend.
[Reply]

I'm talking more about the rollout and allotments.

We had a good idea a while ago about this and that these would work like they do. We should have ramped production and got this shit over with.
[Reply]

Ah, yeah I am not real sure on what the entire plan was even to begin with. Needless to say we are relying on government and the only thing they do reliably is f **** up everything, make it cost more than it should and take longer than it should.
[Reply]

I just got word of the same thing up here in NW Missouri. It's very possible we're looking at late January for our staff and clients even though they are in the 1a category.

Sucks.
[Reply]

Then again everything seems to change on a day to day / week to week basis so who knows.
[Reply]

Are they saying why? Or is the same reason O. City said, care facilities and such?
[Reply]

Effects of non-pharmaceutical interventions on COVID-19: A Tale of Three Models
Vincent Chin, John P.A. Ioannidis, Martin A. Tanner, Sally Cripps
doi: https://doi.org/10.1101/2020.07.22.20160341

4 Discussion
We demonstrate that effects of NPIs are non-robust and highly sensitive to model specification, assumptions and data employed to fit models. We obtained very different inferences regarding the effectiveness of lockdown measures in terms of curbing the epidemic wave and reducing fatalities. Lockdown appeared the most effective measure to save lives in the original analysis of 11 European countries performed by the Imperial College team through model 1. This analysis was published in Nature and has probably had a major impact to maintain a mentality among policy makers that lockdown should be used during the advent of second waves in many countries in the Fall of 2020. However, model 2 (which was also originally developed by the same team), suggests that these impacts were highly exaggerated, with little or no benefit from lockdown in most of the same countries.

Importantly, model 2 typically outperformed model 1 in data fit. Consideration of longer follow-up that included also the lifting of many measures still suggested that the originally1 claimed effects of lockdown are grossly overstated. Fitting yet a third model, resulted in yet further variant conclusions, with only mobility and event ban having regression coefficients with 95%CIs that did not contain 0 for the period until May 5th.

The different results and inferences of these models may be partly explained by the highly correlated structure of NPIs and mobility data, as well as the dense time clustering of the different NPIs being applied typically in close sequence. NPIs largely reduce Rt by reducing contact among individuals. An indirect measure of the reduction in individual contact is the mobility data, and so these data will be highly correlated with NPIs, making any inference difficult by default. Moreover, as different NPIs are typically introduced in close sequence, their exact time lag before impact is difficult to model. Interaction effects between different NPIs may also exist. The effectiveness of different NPIs may also vary across locations and across time based on adherence, acceptability, and enforcement. Any collateral harms may also affect acceptability and adherence.

Given that the inference around the effectiveness of various NPIs is highly model dependent and that more aggressive NPIs have more adverse effects on other aspects of health, society, and economy 7;8;9;10;11;12;13;14;15;16;17;18;19;20;21;22;23;24;25, it is ill-advised to ignore the substantial model uncertainty. Failing to report this uncertainty may ultimately undermine the public’s trust in the value of policy decisions based on statistical modeling. Flaxman et al. 1 made the statement “We find that, across 11 countries, since the beginning of the epidemic, 3,100,000 [2,800,000 - 3,500,000] deaths have been averted due to intervention”. Both the provided estimate and the accompanying limited uncertainty are highly misleading. When results vary widely based on model specification, strong inferences should be avoided.

We are concerned that Flaxman et al. 1 selectively reported on only model 1, even though the Google mobility data was available from early April and the Imperial College team had obviously been using this data and both models 1 and 2, as evidenced by several of their reports2;26;27, before their Nature publication. The results included in the Nature paper seem to suffer from serious selective reporting, providing the most favorable estimates for lockdown benefits, while model 2 would have led to more nuanced, if not different, conclusions. Also the three European countries excluded from the Nature publication had among the least favorable results for lockdown.

Given that modeling studies are typically not pre-registered, multiple analytical approaches and model specifications may be used on the same data28, and data and results may be filtered by modelers according to whether they fit their prior beliefs. This bias can have devastating implication if it leads to adoption of harmful measures.

We do not claim that lockdown measures definitely had no impact in the first wave of COVID-19. Indeed model 2 showed that Rt was still above 1 in some countries and thus it is possible that in these locations it may have some impact on the course of the epidemic wave. Other investigators using a different analytical approach have suggested also some benefits from lockdown; however, these benefits were of a smaller magnitude (e.g. 13% relative risk reduction29). Small benefits of such modest size would be less likely to match complete lockdown-induced harms in a careful decision analysis. Another modeling approach has found that benefits can be reaped by simple self-imposed interventions such as washing hands, wearing masks, and some social distancing30.

Some limitations of our work should be acknowledged. Besides model fit and parsimony metrics, theoretical and subjective considerations, as well as experience from other countries should be considered in model choice. However, given the observational nature of the data and the dynamic course of epidemic waves, one should avoid strong priors about effectiveness of different NPIs. Similarly, our results should not be interpreted with a nihilistic lens, i.e. that NPIs are totally ineffective. Decreasing exposures makes sense as a way to reduce epidemic wave propagation and eventually fatalities. However, if exposures can be reduced with less aggressive measures and fewer or no harms, this would be optimal. Finally, we did not examine very long-term time horizons. In theory, even effective measures may achieve only temporary mitigation and epidemic waves may surge again, when measures are relieved. We did observe this for the uplifting of measures in the July 12th analyses and empirical data from the emergence of second waves in many European countries and the USA in the fall of 2020 validate this hypothesis31. Availability of effective and safe vaccines may also affect risk-benefit ratios of NPI measures of different aggressiveness and different duration of implementation.

Overall, observational data that feed into complex epidemic models should be dissected very carefully and substantial uncertainty may remain despite the best efforts of modelers28;32. While there has been resistance to testing NPIs with randomized trials, such trials are feasible, and more thought and effort should be devoted on how to complement the available, tenuous observational data33. Regardless, causal interpretations from non-robust models should be avoided. In any decision analysis the accurate quantification of the size, not just the existence, of the impact of lockdown on Rt is also critical. This is difficult task when one considers all the confounds between NPIs and mobility, as well as the several behavioral changes such as hand washing and wearing masks. This is an interesting area for research, and crucial for the management of future pandemics.

https://www.medrxiv.org/content/10.1...41v3.full-text
[Reply]

Any chance Donger/Pete can get their own Covid-19 thread?
[Reply]

Most any and every thing.
[Reply]

Sorry, but I think inaccuracies should be challenged and corrected.
[Reply]

Admiral Brett Giroir, assistant secretary for health at the US Department of Health and Human Services said on Good Morning America that when it comes to the number of Covid vaccines that the US Centers for Disease Control and Prevention have reported being administered, “the two million number is probably an underestimate.”

On Saturday, the CDC Covid Data Tracker said that 9,547,925 vaccine doses had been distributed and 1,944,585 had been administered.

Giroir said that 10.8 million doses have been distributed to the states, “that two million number is delayed three to seven days, so we certainly expect that to be a multiple of two million.”

Giroir said that another four point seven million doses would be distributed this week, so by the end of the week there would be over 15 and a half million doses “in the hands of the states.”

There will be another allocation Tuesday, he said, saying “that’s the rhythm,” with states being told what they will get the following week.

“So, 20 million doses will be distributed to the States by the first week in January, that’s where we are, probably another 30 million doses in January, another 50 million doses in February. That seems to be a very good estimate given what we know right now,” said Giroir. “So it’s moving along, it’s cranking, the end of the pandemic is in sight, but we have a lot of work to do and literally thousands of lives depend on how well we follow the simple public health measures until the vaccine can be widely distributed.”
[Reply]

:-)

Your definitions of accuracy are pretty damn fluid.
[Reply]

Sounds like a distribution issue within the states which is embarrassing knowing they’ve had so long to prepare.

But 100 million people vaccinated by the end of February would be fantastic.
[Reply]

Sigh. Fauci didn't claim the 90% figure attributed to him, so I corrected it. Nothing fluid about it.
[Reply]

With Pfizer and Moderna, 100 million doses equals 50 million people vaccinated. Prime and boost. But yes, very cool.
[Reply]