Tag Archives: COVID

COVID Vaccine Effectiveness

Oddly, there is still a lot of vaccine hesitancy and conspiracy theories even though that has all been debunked. What’s even more puzzling are the number of people who will claim that it hasn’t been tested adequately (it’s been tested far more than any other vaccine at launch), but then they will go on to take drugs like hydroxychloroquine and ivermectin, that hadn’t been tested much for COVID initially, and after they have been tested and found not to provide any benefit, they still insist that it’s a cover up. Another study was published in JAMA this week indicating that ivermectin is no better than placebo.

So are vaccines better at preventing COVID infection, hospitalizations, and death? The data paints a very cleart picture.


This graphs represents cases by vaccination status per 100,000 people.

At first glance, the green line at the bottom right doesn’t seem to any benefit from the bivalent boosters. However, this is due to the big surge of cases at the start of 2022, which changes the scale of the y-axis. This is a view of just the part of the graph when the bivalent data became available.

Of course, there were legitimate concerns about the safety of the vaccine in adolescents and children. Views of the data can be found in the link in the sources section below. This is what the case data looks like for the <5 year old cohort, suggesting efficacy among this age group as well


This is hospitalizations per 100,000 among the >18 year old population by vaccination status.

Hospitalization data isn’t available in their visualization tool for the <5 age group, but this is how hospitalizations look for 5-11 year olds by vaccination status.


This represents deaths per 100,000 among those over 18 by vaccination status.

Again, it’s useful to zoom in to see the imapact of the bivalent boosters.

Data Sources

Rates of laboratory-confirmed COVID-19 hospitalizations by vaccination status

Rates of COVID-19 Cases and Deaths by Vaccination Status

The Republican-Democrat COVID Divide

(Graphs will continue to be updated)

Misinformation and outright lies has been a major part of the problem of the COVID response in the US. This seems to be a particular problem related to the right and right-leaning media.


2020 election outcome data for the presidential race was used to group voters by county into six strata:

  • 80-100% Republican
  • 60-80% Republican
  • 50-60% Republican
  • 50-60% Democrat
  • 60-80% Democrat
  • 80-100% Democrat

Each of these strata were pooled for both 2010 population per the US Census Bureau and the incidence of COVID Cases. Due to the geographical election structure not aligning with county structure in Alaska, Alaska was excluded from this analysis.

The pooled COVID cases for each strata were then calculated as an incidence rate per 100,000 per calendar day. A 7-day moving average was then applied to each strata to remove some of the normal weekday variations and each line on the graph was colored to represent the degree of dominance of a political party (dark red represents >80% Republican, dark blue represents >80% Democrat) by 2020 presidential vote.

The hypothesis was that the more strongly a county voted Republican, the more likely it was to listen to and be influenced by misinformation and disinformation channels.


There is a clear gradient in case rates depending on the political leanings of counties. This has remained consistent over time with two exceptions.

When the pandemic first started, a large proportion of the spread was in New York City. This was before there was a good understanding of the mode of transmission and in a very dense population area. As the virus spread into less populous area of the country and more knowledge was gained about transmission, other factors became more important in spread, such as messaging and beliefs.

The other anomaly in this pattern is in the spring of this year. This could be an effect of college students traveling during spring break, who are at an age where they are eager to return to normal and more likely to be a source of asymptomatic community transmission.

For better clarity in just how big the differences are in the extremes, the next graph only shows the >80% wins in the election. Except during the rapid case declines that were due to the vaccination efforts that reduced spread and the previously noted exceptions, the rates of new cases in strongly republican counties were almost four times as high as those in strongly democratic.


This becomes much more important as it related to vaccination efforts. As of this writing, the vaccination rates by state correlate well with voting. A current view of this map can be found here.

The majority of lies and disinformation seems to spread mostly in right wing media and is instigated by twelve different people. This is causing irreparable harm to health and to the economy. Sadly, as new variants come to dominate cases, the spread will be most obvious among those who have been fooled by these sources. What remains to be seen is whether the media will be held responsible for the damage they have caused.

Do your part. Get vaccinated. Encourage others to do so. As B.1.617.2 (the delta variant) gains a foothold, your health and life may depend on it.

An Epilogue

There is a similar pattern for deaths when stratified the same way, which is not surprising at all. The two big jumps for about a month in May 2020 are due to states catching up on death reports and submitting them all at once.

8/21/21 Addendum. Fox “News” carries some responsibility for what is happening by allowing lies like this.

8/26/21 Others are picking up on this source of the problem as well.

12/4/21 The rapid drop in the 80%+ Republican counties the past few weeks has been puzzling. The data hasn’t been tracking with that of the other strata. One possible explanation might have something to do with those who move south for the winter. However, the data this week painted a darker picture. While it may be easy to skew case data by not reporting it, it’s much harder to do so with death data. While this certainly doesn’t draw any specific conclusions, it certainly supports evidence that some governors have been trying to hide the impact in their states.

12/9/21 It appears that data from a very red state has been suppressed for about a month.

State Impacts

Image result for us states

Yesterday I showed the impacts that could be expected in the US by age overall. I’ve taken US census data on a deeper dive and broken that down to the state level so people could see what that could mean for each state if drastic measures are not in place.

Something interesting jumped out at me as I looked at the table. It’s pretty obvious that states that are thought of as retirement destinations are going to have proportionally bigger problems.

These numbers are using the assumptions of an attack rate of 20% and the case fatality rates for age groups reported by the China CDC.

*Please note that there is insufficient data for children <10