Misallocation of Vaccines Leads to 75,000 Additional US Deaths…At Least
As we prepare to roll out the vaccine across the United States, we are faced with an unparalleled opportunity. But there is also a danger of squandering the opportunity. States will distinguish themselves by the speed with which they get their populations to herd immunity, and by the degree to which they minimize the number of people who die during the period when the vaccine is provided.
The number of vaccines available per month is outside of your control. Therefore, as a decision maker, the lever for affecting outcomes lies in your decisions about what order (and which of) your citizens get the vaccine. Certain populations should clearly be at the front of the line (e.g. health care workers without SARS COV2 antibodies). But many questions remain: does the benefit of knowing who has antibodies justify the cost of an antibody test? Should you prioritize people more likely to spread COVID (front-line workers) or people more vulnerable to adverse outcomes (comorbid/elderly).
Vaccination Strategies for Minimizing Deaths
How vaccines are distributed can make a huge difference in the duration of economic effects, hospitalizations, and even deaths. Our analysis shows that nationwide an optimized distribution strategy is 90% likely to avoid more than 75,000 deaths over simpler distribution strategies. The same analysis shows that even for a medium sized state, improved vaccination strategies could reduce the duration of the pandemic by two months and could reduce the number of deaths due to COVID infection by more than 1,000 people.
This is accomplished by how we make tradeoffs among three guiding principles:
Don’t vaccinate people who have already had the infection.
Vaccinate people who are more likely to infect others.
Vaccinate vulnerable people (elderly and people with comorbidities are at higher risk of dying.)
There is extensive research indicating likelihood of reinfection is low and that infection with SARS-COV2 confers long term immunity[1][2][3][4]. One might therefore think the lowest hanging fruit would be to prioritize immunizations for the population that does not have antibodies or memory T cells for SARS-COV2. Unfortunately, only 10-20% of those who have had the illness are “visible” as indicated by a confirmed COVID test[5]. The remaining 80-90% of the already immune population is indistinguishable from the people who are not immune. So which tool should you use to reveal who is in this “not immune” population – antibody tests or statistical analysis? The answer to this question is pivotal and will probably vary from county to county and city to city. What is your current strategy to answer this question?
The other pivotal question is whether a strategy will save more lives by focusing on vaccinating socially active [6] or by focusing on vaccinating vulnerable populations? It is relatively straightforward to determine who is in the most vulnerable population (elderly and people with comorbidities) but knowing which broad strategy saves more lives is not as clear.
The charts below show how the virus may spread with an optimized strategy vs. a less effective strategy. In the less effective strategy, some vaccines are wasted on individuals who already have antibodies. In addition, the sub-optimal strategy would not attempt to identify recipients for the vaccine as a function of how likely they are to spread the virus to others.
The better strategy minimizes the use of vaccines on people who do not need them and focuses on individuals more likely to spread the virus to others. Because this strategy slows down the rate of spread, it also allows more time to vaccinate the population not already immune.
The costs and benefits are also not limited to directly saving lives. Since the optimal strategy would also end community transmission more quickly, there would be economic benefits as well. For a medium sized state like Wisconsin, economic benefits would run in the billions of dollars (think opening a convention center 45 days before your neighboring states are able to do so). For the same state, the cost of getting the order wrong is a thousand or more (preventable) COVID deaths and a society shut down for 1 month+ longer than need be. As a decision maker for vaccine distribution, you get to decide whether to be seen as the hero…or the villain. But to be the hero, you need the right tools.
How Hubbard Decision Research Can Help
For 20+ years, Hubbard Decision Research has been “spreading the gospel” about probabilistic methods across many areas of industry and government. Probabilistic forecasting has been shown to yield the best results across all the industries where it has been measured and studied. We have the experience and expertise to bring these methods into any organization and any challenge. This year, HDR has also built a reputation for accurate forecasting and predictions with COVID related issues for businesses and municipalities, as well as national forecasting. We have presented webinars for the GFOA, and worked on COVID related operational risk projects for school districts, insurance and reinsurance companies, and a variety of other industries. Our Applied Information Economics methodology has been applied across industries, government, and the military and focuses on improving decisions through probabilistic modeling.
Turn your vaccine distribution solution into an optimized quantitative solution. HDR offers a 20% discount on our rates for governments and nonprofits. Contact us to learn more.
Watch the interview with BSW that was previously aired on Tuesday, August 4th at 6:30pm CDT. Doug talks about his ground shaking exposé on the failure of popular cyber risk management methods, How To Measure Anything in Cybersecurity. This particular book is a Palo Alto Networks Cybersecurity Canon Award winner and the first of a series of spinoffs from his successful first book, How To Measure Anything: Finding the Value of “Intangibles” in Business. It is cited by the Center for Internet Security RAM Version 1.0 as a “thorough and practical guidance on using probability analysis for cybersecurity decision making.”
In the interview, Doug talks about his life’s work which is about building better “business impact” decision makers in any department of any sized organization and in any industry. He has sold over 150,000 copies of four different books in eight different languages. He offers powerful online training and consulting services revolving around his quantitative methodology, Applied Information Economics (AIE), for his global client base of Fortune 500 companies, federal and state governments, the United States military, and major non profits including the United Nations.
For this interview, Doug is particularly pleased with his shirt choice! Enjoy!
We heard you loud and clear and are happy to accommodate! We are extending our promotional offer on the NEW AIE Analyst Series through Friday, August 28th. Receive a dollar-for-dollar discount of all your previous webinar expenditures with HDR, up to 75% off the price of the new AIE Analyst Series, per person – if you book by Friday, August 28th.
The new AIE Analyst Series regular price is $1,950. This means you could take the entire series for as little as $487.50, if you have at least $1,462.50 in previous webinar expenditures. If you have more than $1,462.50 is previous webinar expenditures, invite a friend or colleague and (based on your remaining spend) they could also receive up to 75% off the series as well. This offer includes recordings of all courses in the series and access to online materials, even if you are unable to attend some or all of the live workshops.
Regular Prices on new courses included in the AIE Analyst Series:
Calibrated Probability Assessments – $580 (Pandemic Price: $325)
Creating Simulations in Excel: Basic – $375 (Pandemic Price: $310)
Creating Simulations in Excel: Intermediate – $375 (Pandemic Price: $310)
One Elective Course – $150 (Pandemic Price: $95)
(For Elective Course, Choose from: HTMA in Project Mgt, HTMA in Innovation, HTMA in Cybersecurity Risk, Failure of Risk Management)
Even courses that were part of the previous AIE Analyst series, such as Decisions Under Uncertainty and Empirical Measurements, have new methods and new spreadsheet tools. And now the new Computer Based Training (CBT) components mean that you can review hours of content online at your own pace and take the review quizzes online.
If you have any questions or if you are interested to take advantage of this offer, please contact us at info@hubbardresearch.com to verify your previous webinar expenditures and to receive your unique promo code in order to claim your personalized discount at checkout.
Doug Hubbard and his team’s work is mentioned in high regard often in articles, by peers and clients alike. Perhaps it’s because HDR utilizes native Excel to create custom automation models for each client’s specific needs – without any limitations of an existing software solution and without annual licensing or subscription fees that often come along with traditional software solutions.
This week an article in InfoSec 2020 was brought to our attention where Doug is mentioned specifically by the Union Pacific CISO, Rick Holmes. Union Pacific is the second largest railroad system in the United States and is one of the largest transportation companies in the world.
A portion of the article reads, “UP assesses and analyzes risk from four different perspectives – those of an insurance company or actuarial expert, a compliance auditor, a legal advisor and the mind of an attacker. Key to the process, however, is the risk probability modeling that the cyber risk assessment team developed in order to statistically convey to upper management the likelihood of a cyber event occurring and the calculable monetary loss that would result.
For this, UP recruited management consultant and author Douglas Hubbard, who helped devise a framework that analyzed and categorized UP’s computing environment into various asset classes.”
Hubbard Decision Research today announced the release of the second edition of the ground-breaking book on risk management, The Failure of Risk Management: What’s Broke and How to Fix It, published by Wiley and Sons.
The second edition expands upon the central theme of the first edition, which covered misused analysis methods and showed how some of the most popular “riskmanagement” methods are no better than astrology through examples from the 2008 credit crisis, natural disasters, outsourcing to China, engineering disasters, and other notable events where risk management failed. This edition includes new material on simple simulations in Excel, research about the performance of various methods, new survey results, expanded statistical methods, and more
The Failure of Risk Management can be purchased online here. We also have live, online training based on the principles covered in the book in a two-hour webinar that can be found here.
Risk management needs to change, and risk managers need to adopt scientifically-proven, quantitative methods like Applied Information Economics if they hope to get ahead of the curve and reduce risk with confidence instead of wishful thinking.
Learn how to identify flawed risk management methods you may be using and replace them with proven methods with our two-hour The Failure of Risk Management webinar. $150 – limited seating.
Rarely does an opportunity present itself where statistics can so immediately and with so much impact solve a pressing real world problem. Shortly before publishing this post, we reached out and talked to the Japanese Ministry of Health and they indicated that they planned to test everybody on board. Whether they go ahead with that plan or not, the point of the article remains valid – doing a random sample (it could have been done 6 days ago) would be an efficient (both in terms of time and the limitation on test kits) way to reduce uncertainty on the total population of positive COVID-19 cases.
Situation: There are 3,600 passengers and crew aboard the Diamond Princess cruise ship docked off the coast of Yokohama, Japan. A significant percentage of them are infected with coronavirus, but it is unknown how many. Currently, it would be difficult to quickly test all 3,600 passengers and crew for the coronavirus, especially since there is limited availability of test kits, according to Japanese authorities.
Solution: To get a much better estimate of total cases, only a fraction of the passengers need to be tested at first. This could lead to important time savings in crucial decisions facing the Japanese Ministry of Health and the Diamond Princess cruise ship.
Despite the total number of 3600 passengers and crew, you only need to test a couple hundred to narrow the range meaningfully on the number of total cases; the results of those tests could have immediate and important consequences. A crucial tenet of Applied Information Economics (AIE) is that measurements are generally more useful (or have a higher ROI) when they are connected to a decision. The important decisions in this situation aren’t going to be based so much on a small difference (e.g. whether there are 200 or 220 additional cases). The important decisions will be made based on whether there are 50 or 500 additional cases. This level of uncertainty reduction could be achieved quickly with a smaller random sample of the whole population.
A crucial point is that these tests need to be selected randomly – thus far the samples have been taken from suspected cases (people with symptoms or contact with known cases). However, a random selection of 200 would allow us to apply an inverse beta distribution to the results and produce a relatively tight 90% confidence interval on total cases in the remaining 3,400 people.
Recommendation 1: Randomly test 70 staff and 130 passengers, and then use a beta distribution to project the total in the remaining population. The reason to break out staff and passengers is that they are likely to have different proportions of infection. Because the staff on the Diamond Princess continues to commingle (i.e. eat and berth together), their rate of infection is likely higher.
Recommendation 2: Once the new range for total infections is obtained with the random sample, prepare local hospitals for case load. Among other things, the ministry will know if there are enough isolation units available, and if not make other arrangements (i.e. dedicate one hospital to COVID-19 patients).
Recommendation 3: If the infection level of the staff is greater than a threshold (15-20%), then other arrangements should be made for serving passengers. Identifying the healthy staff would become a priority in this case.
At this moment, there is a wide range for the 90% confidence interval of current level of infections (https://hubbardresearch.com/cruise-ship-coronavirus-infected-passengers-in-hundreds/). People on board are worried about if they are sick or not, regardless of whether they have symptoms. If test results come back that indicate that a very small percentage of asymptomatic people tested positive, this would reassure those on board who are asymptomatic. Additionally, the percentage of asymptomatic cases that test positive could have broad global implications for detection and planning for we could back out percentage of cases that test positive while asymptomatic.
This is a unique opportunity to understand the disease that could help the effort to contain it worldwide!
Learn how to start measuring variables the right way – and create better outcomes – with our two-hour Introduction to Applied Information Economics: The Need for Better Measurements webinar. $100 – limited seating.
THE TOTAL NUMBER OF CORONAVIRUS-INFECTED CRUISE SHIP PASSENGERS WILL BE IN THE HUNDREDS [UPDATE]
Authorities have revealed the results of the first round of COVID-19 tests. But, a more impactful question remains: How many passengers will ultimately become infected?
Any decision can be made better through better measurements – and as these three examples show, just five data points can tip the scales in a business decision.
