The LA Times COVID pages show vaccinations by zip-code. Looking at the zip codes in some of the more vaccinated counties is still disappointing.
94022 is much of Los Altos & LAH. Pop 19,378. First dose: 13,955=72%. If 20% are too young, that leaves nearly 1550 that haven't gotten a shot. Why? I suppose that number might represent children listed as residents but off at school or in the military.
94024 is mostly Los Altos & LAH as well. Pop. 23.961. First dose: 15,644=65%. Around 3500 eligible but not yet gotten the first shot.
(Cumulative cases in Los Altos & LAH is about 2.5% of the population.)
94031 is Palo Alto. Pop. 17,191. First dose: 11,500=67%. Around 2,250 eligible but not vaccinated.
94305 includes Stanford. Pop 15,730. First dose: 7,792=50%.
One of the least vaccinated regions in the Bay Area is between Alameda and San Leandro.
94601 Pop. 52,299. 1st dose: 23,397=45%
94621 Pop. 35,261. 1st dose: 12,034=34%
94695 Pop. 43,112. 1st dose: 19,557=45%
94603 Pop. 34,593. 1st dose: 12,359=36%
On the other end, the 3 zip codes around Compton (pop. 139K) average only 30% first-dose vaccination.
(Cumulative cases in Compton represent about 17% of the population.)
This is in the middle of a large L.A. region of zip codes all less than 50% vaccination.
I'm less concerned about the rural areas where vaccination rates are low. It is the urban areas that are liable to light up quickly. Perhaps part of the issue is that people who had COVID are not getting vaccinated. (Perhaps because they think they are already "immune" or perhaps they never would get vaccinated anyway.)
While the vaccination rate is about 30% in Compton, the data shows Compton had about 17% cumulative cases of COVID (1700 per 10K). Ignoring the issue of children contributing to the case rate, that suggests maybe a 47% "immunity" rate for that area. That's a lot more encouraging than 30%. In my opinion, it likely better represents the vulnerability status of Compton.
The CDC recommends those that had COVID should get vaccinated, but people may hesitate to get the shot if they had the disease.
When looking at measuring the chance of COVID outbreaks in the future, it may be worth looking at a "resistance" map which combines vaccination rate and cumulative infection rate. I don't like the term "immunity" as it sounds 100% effective and permanent. "COVID-resistance" may be a better term.
You could even get a little fancier and multiply case rate and vaccination rate by an effectiveness factor that decreases over time.
The best numbers I've seen for effectiveness against getting COVID:
90% effectiveness of both doses of mRNA vaccines (CDC study of health care workers)
80% effectiveness of one dose of mRNA vaccines (CDC study of health care workers)
67% effectiveness of Janssen vaccine (clinical trial results)
80% effectiveness of having had COVID (from a Dutch? study)
The effectiveness will decrease over time but those numbers should be good for 6 months after the vaccine or illness.
(And, of course, you may adjust them if the effectiveness of the vaccines differs for the prevailing variant as the infections evolve in the area.)
For instance, if in 10,000 people,
2,000 have 2 mRNA doses, they constitute (100-90% = 10%) vulnerable: 200
1,000 have 1 mRNA dose, they constitute (100-80%= 20%) vulnerable: 200
2,000 have Janssen, they constitute (100-67% = 33%) vulnerable: 600
1,500 have had COVID (if we presume none got vaccinated), they constitute (20%) vulnerable: 300
3,500 have no infection or shot, they are 100% vulnerable: 3500
Total vulnerable: 4,800 = 48%
If each of the 10,000 were exposed today to the virus, then about 48% would get sick.
I think a map per zip code or community showing either the simple sum of vaccination rate plus infection rate
or this vulnerability measure, would help clarify how vulnerable communities are to COVID outbreaks.
------------
You could go one step further and calculate a weekly transmission rate. If you figure a community has 4800 vulnerable per 10K, and gets 48 cases per 100K in a week (4.8 per 10K), then the "transmission rate" is 4.8/4800 = 0.1%. This would be a measure that is the result of two factors
(1) how many people are infectious and are exposing others
(2) how effective the community is against spreading the disease (by vaccine, hygiene, or restrictions on activities) by those walking around while infectious.
Using the 0.1% "transmission rate", in the next week, the estimate for the 10,000 people,
2,000 * 0.1% * 10% = 0.2 with 2 mRNA will get COVID
1,000 * 0.1% * 20% = 0.2 with 1 mRNA will get COVID
2,000 * 0.1% * 33% = 0.6 with Janssen will get COVID
1,500 * 0.1% * 20% = 0.3 with previous COVID will get COVID
3,500 * 0.1% * 100% = 3.5 with no vaccine or previous COVID will get COVID
Total 4.8 will get COVID
This is related to R (where 1 infection will cause R infections). Both are inversely proportional to the 2nd factor. R is usually used to describe the (positive or negative) exponential growth of an epidemic, and is usually thought of as primarily a parameter of the infectiousness of the virus.
This transmission rate is more clearly focused on how many are vulnerable and how many have the disease now, and so can be used to measure the risk to an individual, or the effectiveness of ALL the controls (including vaccination) in one community versus another.
94022 is much of Los Altos & LAH. Pop 19,378. First dose: 13,955=72%. If 20% are too young, that leaves nearly 1550 that haven't gotten a shot. Why? I suppose that number might represent children listed as residents but off at school or in the military.
94024 is mostly Los Altos & LAH as well. Pop. 23.961. First dose: 15,644=65%. Around 3500 eligible but not yet gotten the first shot.
(Cumulative cases in Los Altos & LAH is about 2.5% of the population.)
94031 is Palo Alto. Pop. 17,191. First dose: 11,500=67%. Around 2,250 eligible but not vaccinated.
94305 includes Stanford. Pop 15,730. First dose: 7,792=50%.
One of the least vaccinated regions in the Bay Area is between Alameda and San Leandro.
94601 Pop. 52,299. 1st dose: 23,397=45%
94621 Pop. 35,261. 1st dose: 12,034=34%
94695 Pop. 43,112. 1st dose: 19,557=45%
94603 Pop. 34,593. 1st dose: 12,359=36%
On the other end, the 3 zip codes around Compton (pop. 139K) average only 30% first-dose vaccination.
(Cumulative cases in Compton represent about 17% of the population.)
This is in the middle of a large L.A. region of zip codes all less than 50% vaccination.
I'm less concerned about the rural areas where vaccination rates are low. It is the urban areas that are liable to light up quickly. Perhaps part of the issue is that people who had COVID are not getting vaccinated. (Perhaps because they think they are already "immune" or perhaps they never would get vaccinated anyway.)
While the vaccination rate is about 30% in Compton, the data shows Compton had about 17% cumulative cases of COVID (1700 per 10K). Ignoring the issue of children contributing to the case rate, that suggests maybe a 47% "immunity" rate for that area. That's a lot more encouraging than 30%. In my opinion, it likely better represents the vulnerability status of Compton.
The CDC recommends those that had COVID should get vaccinated, but people may hesitate to get the shot if they had the disease.
When looking at measuring the chance of COVID outbreaks in the future, it may be worth looking at a "resistance" map which combines vaccination rate and cumulative infection rate. I don't like the term "immunity" as it sounds 100% effective and permanent. "COVID-resistance" may be a better term.
You could even get a little fancier and multiply case rate and vaccination rate by an effectiveness factor that decreases over time.
The best numbers I've seen for effectiveness against getting COVID:
90% effectiveness of both doses of mRNA vaccines (CDC study of health care workers)
80% effectiveness of one dose of mRNA vaccines (CDC study of health care workers)
67% effectiveness of Janssen vaccine (clinical trial results)
80% effectiveness of having had COVID (from a Dutch? study)
The effectiveness will decrease over time but those numbers should be good for 6 months after the vaccine or illness.
(And, of course, you may adjust them if the effectiveness of the vaccines differs for the prevailing variant as the infections evolve in the area.)
For instance, if in 10,000 people,
2,000 have 2 mRNA doses, they constitute (100-90% = 10%) vulnerable: 200
1,000 have 1 mRNA dose, they constitute (100-80%= 20%) vulnerable: 200
2,000 have Janssen, they constitute (100-67% = 33%) vulnerable: 600
1,500 have had COVID (if we presume none got vaccinated), they constitute (20%) vulnerable: 300
3,500 have no infection or shot, they are 100% vulnerable: 3500
Total vulnerable: 4,800 = 48%
If each of the 10,000 were exposed today to the virus, then about 48% would get sick.
I think a map per zip code or community showing either the simple sum of vaccination rate plus infection rate
or this vulnerability measure, would help clarify how vulnerable communities are to COVID outbreaks.
------------
You could go one step further and calculate a weekly transmission rate. If you figure a community has 4800 vulnerable per 10K, and gets 48 cases per 100K in a week (4.8 per 10K), then the "transmission rate" is 4.8/4800 = 0.1%. This would be a measure that is the result of two factors
(1) how many people are infectious and are exposing others
(2) how effective the community is against spreading the disease (by vaccine, hygiene, or restrictions on activities) by those walking around while infectious.
Using the 0.1% "transmission rate", in the next week, the estimate for the 10,000 people,
2,000 * 0.1% * 10% = 0.2 with 2 mRNA will get COVID
1,000 * 0.1% * 20% = 0.2 with 1 mRNA will get COVID
2,000 * 0.1% * 33% = 0.6 with Janssen will get COVID
1,500 * 0.1% * 20% = 0.3 with previous COVID will get COVID
3,500 * 0.1% * 100% = 3.5 with no vaccine or previous COVID will get COVID
Total 4.8 will get COVID
This is related to R (where 1 infection will cause R infections). Both are inversely proportional to the 2nd factor. R is usually used to describe the (positive or negative) exponential growth of an epidemic, and is usually thought of as primarily a parameter of the infectiousness of the virus.
This transmission rate is more clearly focused on how many are vulnerable and how many have the disease now, and so can be used to measure the risk to an individual, or the effectiveness of ALL the controls (including vaccination) in one community versus another.