I was hoping this time around, the ones that blew it, and took all the bailouts last time would have to pay their fair share.
I gave 10% of salary last time, plus work furloughs. Which haven’t been recovered yet. Although I’m sure I will give again.
Yes I am a sworn civil servant. So according to the order passed down yesterday it appears I am an “essential infrastructure” employee. So I will be out in the public, until further notice. Luckily wife, and kids get to stay home. Hopefully I don’t bring it home. Unfortunately my benefit package doesn’t give me time off during an emergency.
There now I feel better. Just needed to whine a little.
Looks like that is going to work. They’re getting her out in the nick of time! Whew!
I very much enjoyed reading your post # 64 which was a response to sand puppy. Sharing your thoughts and experiences about working in the medical field is interesting. I hope you will post again soon.
AKGranny
I know there’s a link to this very helpful paper somewhere up this thread, but I can’t find it. Read it carefully as it gives much insight into which policies might be most helpful going forward. Note in particular, this quote from the top of page 7:
So total deaths could be higher by perhaps a factor or 2 or 3 than the numbers cited judging from Italy's experience. Here is a brief summary. The first three points below are from the paper, the last two are mine:In total, in an unmitigated epidemic, we would predict approximately 510,000 deaths in GB and 2.2 million in the US, not accounting for the potential negative effects of health systems being overwhelmed on mortality. (Empasis mine)
- If it's too late to suppress the pandemic, choose just the right combination of NPIs to keep ICU demand at or below capacity.
- Choose the most effective NPIs with that are easiest to maintain over time in terms of minimal disruption and damage to the economy. Otherwise, people will stop cooperating or it will become necessary to loosen restrictions to prevent catastrophic economic consequences that could be more deadly than the virus.
- Toggling NPIs on and off based on weekly ICU admissions could be helpful for calibrating the response.
- Ramp up ICU capacity as quickly as possible in order to allow gradual relaxation of NPIs in order to minimize economic damage.
- Strong emphasis on better treatment to minimize duration of hospital stays and need for ICU beds would have a similar benefit.
I just want to announce that the vote button works again. Whew! I was really missing it all these weeks.
Please use it liberally. Anything you find useful in some way, give that person’s comment a thumbs up.
Great conversations everyone. I find it useful - captivating even - to read your stories, ideas, and experiences.
Keep them coming!
Any research about MERV13 cloth traditionally used in furnace filters? Better or worse than vacuum cleaner bags? Or even higher MERV levels?
Hi Dave,
Have been fiddling around a bit but am getting to my limits. What I ultimately am looking for is, at local level, being able to predict how many new cases would be generated per pay, how many beds are needed for suspected cases/ confirmed cases, and getting a better view of how this epidemic could develop over time (I’m preparing an open letter for the local ‘authorities’ to show what we might be facing, and how to better prepare for it.
I see 3 approaches to getting to a curve that can be used for local scenario planning for the epidemic curve:
- A formula is developed that provides the form of the curve. As the curve is only exponential in the beginning, it quickly gets beyond my 'kitchen sink' maths. I also wonder if this approach allows for easy adapting (e.g. change R0, different treatment protocols). Possibility to tweak while data comes in.
- Using Excel, calculate day-by-day, the situation, and let the curve form itself by this. We have quite some data to inform us in that. I think this form is easiest (for me at least) to tweak parametres to form different scenarios (e.g. attack rates, R0), but not possible to change the form of the graph on-the-fly. . One part will push the number of cases higher (existing infections that transmit using an R0 of around 2-3 (though this will change with types of measures taken), one part will suppress (a reducing pool of people who are susceptible to infection). There are some shortcuts taken here (e.g. you're just looking at number of new cases, not total present in a population) and it is based on assumptions, but you have to start somewhere...
- The last option is just take a paper with a raster, overlay a curve that starts off with a doubling every 6 days, and counts number of squares under the curve. More intuitive, but no ability to easily tweak with new data that comes in.