COVID-19 Has You Working From Home? Here’s 50% Off Online Quantitative Training through April

If you haven’t already been sent home to work in the midst of the spreading COVID-19 pandemic, you may very well find yourself there soon. The government is urging anyone who can work remotely to do so for up to eight weeks (or even longer) as the nation tries to flatten the curve of the pandemic’s growth and keep things under control.

To help our fellow exiles, we are offering a special deal: from now until the end of April 2020, get 50% OFF all online training from Hubbard Decision Research. That includes any webinar scheduled from now until the end of April. The list of all eligible webinars is below:

introduction to applied information economics - 1 hour

Wednesday, March 18 3:00pm – 4:00pm CDT

Monday, March 30 9:00am – 10:00am CDT

Tuesday, April 14 9:00am – 10:00am CDT

$100 $50

In this one-hour session, you will get an executive overview of methods that show independently, scientifically measured improvements to management forecasts and decisions. This webinar is an excellent means to learn about the key tools and methods of Applied Information Economics, so you can start applying these trusted practices today to grow your organization’s success. Visit the Checkout Page

calibration training - quantify your uncertainty - 3 hours

Thursday, March 19 3:00pm – 6:00pm CDT

Wednesday, April 15 9:00am – 12:00pm CDT

$580 $290

In this 3-hour Calibration webinar, you will learn the techniques behind subjectively assessing the probability of uncertain events and the ranges of uncertain quantities. This is an essential skill for anyone who needs to consider chance in decisions. Participants will see their skills measurably improve during the training with a series of “calibration exams.”  Visit the Checkout Page

basic simulations in excel - 3 hours

Monday, March 23 12:00pm – 3:00pm CDT

Wednesday, April 22 3:00pm – 6:00pm CDT

$375 $187.50

Simulations have been shown to measurably improve estimates, but many decision models currently lack this critical element. Learn how to create simulations in native Microsoft Excel that can lead to better decisions in any field. Visit the Checkout Page

calibration facilitator training - 1.5 hours

Tuesday, March 24 3:00pm – 4:30pm CDT

$995 $497.50

The Calibration Facilitator Training webinar and follow-up is for already calibrated people and includes everything that somebody needs to run their own calibration session, including a private follow-up observation occurs when the purchaser gives their first live calibration training in their organization or elsewhere. Note: Calibration Training is a prerequisiteVisit the Checkout Page

intermediate simulations in excel - 3 hours

Thursday, March 26 9:00am – 12:00pm CDT

$375 $187.50

Simulations have been shown to measurably improve estimates, but many decision models currently lack this critical element. Learn how to improve on basic simulations in native Microsoft Excel that can lead to better decisions in any field. Note: Basic Simulations in Excel is highly recommended prior to taking this courseVisit the Checkout Page

the failure of risk management - 2 hours

Friday, March 27 9:00am – 11:00am CDT

$150 $75

The biggest risk to an organization is a failed risk management system. In this 2-hour webinar, based on Doug Hubbard’s ground-breaking book The Failure of Risk Management: Why It’s Broken and How to Fix It (now in its second edition), you’ll learn how risk management today is broken, and how organizations can fix their processes and do a better job protecting themselves from risk through proven quantitative methods.  Visit the Checkout Page

applied information economics (aie) analyst training - 9 hours

Monday, March 30 9:00am – 10:00am CDT

Tuesday, March 31 9:00am – 11:00am CDT

Wednesday, April 1 9:00am – 11:00am CDT

Thursday, April 2 9:00am – 11:00am CDT

Friday, April 3 9:00am – 11:00am CDT

$1,450 $725

This series of webinars give the participants hands-on training in the use of Applied Information Economics (AIE), a proven and powerful quantitative analysis method used by Fortune 500 companies, federal and state governments, the U.S. military, and leading multi-national corporations across the globe. It consists of a total of 9 hours of training delivered in five separate modules and teaches participants how to measure any “intangible,” think of risk like an actuary, and look at any portfolio from a risk/return point-of-view. Visit the Checkout Page

how to measure anything in project management - 2 hours

Tuesday, March 31 2:00pm – 4:00pm CDT

Friday, April 17 9:00am – 11:00am CDT

$150 $75

In this two-hour, introductory webinar session, you will get an executive overview of what is wrong with current methods in measurement and risk assessment in project management. We will outline real solutions that are based on real quantitative methods which have scientific evidence of improving decisions. Visit the Checkout Page

how to measure anything in cybersecurity risk - 2 hours

Wednesday, April 1 2:00pm – 4:00pm CDT

Thursday, April 16 9:00am – 11:00am CDT

$150 $75

Do current risk assessment methods in cybersecurity work? Recent big security breaches have forced business and government to question their validity. Is there a way to fix them? How can risk even be assessed in cybersecurity? This two-hour webinar will change how you view cybersecurity and give you the tools to begin finding these critical answers – and better protecting your organization. Visit the Checkout Page

how to measure anything in innovation - 2 hours

Thursday, April 9 2:00pm – 4:00pm CDT

$150 $75

What do we mean by innovation? Can we measure it? And if we can measure it, can we get better at innovating? This two-hour webinar will explain how better measurements can lead to better innovative results. Visit the Checkout Page

If you have already purchased any of the above webinars in the March 17 – April 30 timeframe, you can receive one webinar of equal value (of the original price) at a 100% discount. Contact us to let us know and we’ll make the arrangements. Note: the special doesn’t not include group discounts on training, which have their discounted rates.

In any crisis, there’s opportunity. Now is your opportunity to receive proven, industry-leading quantitative training at a discount and gain the skills you need to measurably improve your performance – whether you’re working from the couch or the cubicle.


Get a more granular, tailored, and accurate estimate of the spread of the pandemic for your organization with our customizable COVID-19 Coronavirus Operational Risk Report. Click below to learn more. 


The CDC Needs a Better Way to Communicate Coronavirus Risk

The COVID-19 coronavirus pandemic is continuing to grow, and the Centers for Disease Control (CDC) is ramping up testing to gather more data on the spread of the virus in the U.S.

Gathering data is a must, but unfortunately, the CDC is running into a very common – and very problematic – risk management problem: using qualitative and pseudo-quantitative methods to calculate and communicate risk.

As you can see in the image above, the CDC is still using “High, Medium, Low” methods for communicating risk to the general public. They are using advanced epidemiological simulations that produce probabilistic results, but, unlike what you’ve seen with hurricane forecasts from the National Oceanic and Atmospheric Administration’s National Hurricane Center, any quantitative analysis is reduced to an ordinal scale for public consumption. This is not actionable for most organizations.


For starters, an ordinal scale like the one above doesn’t rank magnitude, or the degree to which one thing is “more” than another. In this case, exactly how much more exposure or risk does “High” represent over “Medium?” Put another way, if one location has a “High” rating versus a location with a “Medium” rating, how much more likely are you to become infected if you’re in that first location – 5%, 20%, 80%? There’s no proper context.

Another flaw that flows from these kind of ordinal ranking systems is that you can’t perform math with them. It’s easy to see how you can’t exactly add “High” to “Medium” to get any kind of insight. If we used a 5-point scale instead, and one was a 4 and the other was a 2, is the first location twice as risky as the second location? Is a 3 location three times as risky as a 1 location?

Finally, using a “High, Medium, Low” method doesn’t give you what you need the most: the ability to make informed decisions via quantitative analysis. If we wanted to create a model to forecast the spread of the virus and calculate infection rates, we need the actual data – the numbers of potential cases, confirmed cases, deaths, recoveries; the demographics of the patients and of the location as a whole; observed transmission rates, etc. The CDC would be performing a more valuable service if it made that information readily available to the public so that anyone can use it, but unfortunately, they either are restricting what they publish to the public, or the lack of testing to this point (as of Friday, March 13, only roughly 16,600 tests had been performed, or 0.005% of the population ; South Korea, by contrast, has tested 0.45% of its population, or 90 times the per capita amount in the US).

To those who say that the “High, Medium, Low” approach is the best way to inform a large group of people who probably don’t have experience in statistics, we call foul. The National Hurricane Center, as mentioned above, conducts the same probabilistic analysis as the CDC when forecasting the intensity and track of a hurricane. But it doesn’t shy away from numbers; in fact, it produces images like the one below (Figure 1):

Figure 1: National Hurricane Center’s Projection for Hurricane Isaac, August 26, 2012

Everyone can understand this chart. It doesn’t tell you exactly what will happen, but it doesn’t have to. Instead, someone can look at the chart and see what is most likely to happen, when it’s most likely to occur, and how intense the storm will most likely be. All of those conclusions were drawn from analyzing troves of data about wind speed, humidity, internal pressure measurements, water temperature, and the like, so it’s not as if tracking a hurricane is child’s play compared to calculating infection spread. If the NHC can do it, so can the CDC – and they should.

If government agencies want to adequately convey risk to the public – and to their own internal and external decision-makers – then using qualitative and pseudo-quantitative methods like risk matrices, heat maps, and weighted scoring is insufficient at best and dangerous at worst. If we are all to make better decisions regarding the coronavirus pandemic, then we need better efforts from those who we have entrusted with our safety.

Note: The above concepts are explained more completely in The Failure of Risk Management, which can be purchased online here.


Get a more granular, tailored, and accurate estimate of the spread of the pandemic for your organization with our customizable COVID-19 Coronavirus Operational Risk Report. Click below to learn more. 


Doug Hubbard’s The Failure of Risk Management Second Edition Is Now Available

 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 “risk management” 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.


HDR Launches New Customizable COVID-19 Coronavirus Operational Risk Report

 customizable coronavirus (covid-19) operational risk REPORT

Providing daily, weekly, or Twice-weekly Probabilistic Forecasts tailored to inform your operational decisions for mitigating a rapidly-changing threat

,Hubbard Research


The COVID-19 coronavirus pandemic that is currently spreading across the globe threatens not just the health of every person, but the health of every organization. From lower productivity to higher insurance rates, slower and more irregular supply chains, and other potential disruptions, the threat of critical disruption to key operations is real. And as the number of cases grows, so does the operational risk for an organization.

This is the coronavirus curve, and an organization needs to know where the curve is so they can stay ahead of it.

With our innovative Coronavirus Operational Risk Report, we provide the means for organizations to:

Understand and quantify specifically how the pandemic poses a threat
Forecast the spread of coronavirus cases beyond what is currently available to the public
Provide timely interpretation of breaking news as it pertains to operational risk
Make strategic decisions about mitigating operational risk

Reports can be customized to your organization’s specific needs, including: timeframe, delivery method, scope, subject matter, and more. The insight we deliver can be easily incorporated into your current decision-making process, even if you don’t already use statistical modeling. And, we don’t use risk matrices, “high-medium-low” ratings, weighted scoring, heat maps, or other pseudo-quantitative methods that introduce more error than they eliminate.


how our customizable reports deliver the insight you need

HDR has conducted probabilistic analysis on the spread of this virus using our industry-leading Applied Information Economics (AIE) methodology that incorporates probabilistic modeling to create actionable projections. The projections so far have tracked well with subsequently observed outcomes.

For example, we forecasted a 90% confidence interval for the number of total cases on the quarantined Diamond Princess cruise ship prior to the tests being conducted.  The figure below shows the forecast made 12 days before the tests were given and the outcome after test results were known. Note that the actual result was well within the forecasted range, and this result was repeated on multiple forecasts.

Reports are customizable by:

  • Frequency: Receive updates daily, weekly, or twice-weekly
  • Location: Choose the domestic or international locations you want to monitor and forecast
  • Timeframe: Determine how far out from the present you want to forecast (i.e. 7 days, 14 days, 30 days)
  • Delivery method: Tailor the content and method of the deliverables to what your decision-makers prefer
  • Optional consulting: Work with us to develop a more complete probabilistic model that can measure more variables and make additional forecasts to further quantify and mitigate operational risk

The result: actionable intelligence beyond what organizations have now through more accurate and more timely forecasts.

Only measuring the right things in the right way, and building a decision model to help inform better decisions, can move your organization ahead of the coronavirus curve.

Contact Us

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Diamond Princess: A Data Science Solution To the COVID-19 Test Kit Shortage

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 ( 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.




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?


The Diamond Princess cruise ship is currently under quarantine due to COVID-19. Here, we share lessons learned from applying our quantitative method.

Five Data Points Can Clinch a Business Case


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.

The Total Number of Coronavirus-Infected Cruise Ship Passengers Will Be in the Hundreds

Update: A new batch of test results was released on 2/15/2020, bringing the total number so far to 355 confirmed infections. The cruise ship has another week left in quarantine, and officials expect more cases to come – in line with our estimates (see below).

The Diamond Princess, a cruise ship carrying 3,700 passengers and crew, has been in quarantine ever since a passenger from Hong Kong fell ill with the 2019 novel coronavirus (2019 n-CoV). 

On 2/6/2020, we estimated that the total number of positive test results from the first 273 samples would be between 35 and 66. Today, 2/7/2020, the testing was completed and reported – and the number of samples that tested positive (61) fell within our estimated range (see Figure 1 below).

But, a more impactful question remains: How many passengers will ultimately become infected?

Today, 2/7/2020, we estimate the total number of coronavirus infections on the Diamond Princess as 150 to 850. We’ll explain the methodology we used to make this estimate, but the salient point is this: If the “hidden” population of the virus is large as we predict it holds important implications for policy makers (and the general public).

First, we’ll revisit our estimate from earlier this week. This estimate was made using a Beta distribution on the first 102 tests and applying it to the remaining 171 tests. (Figure 1).

Using a probabilistic model, we now give a 90% confidence interval for the total number of coronavirus infections on the Diamond Princess on 2/3/2020 as 150 to 550. Because infections could continue to occur between people in the same cabins after the quarantine, our 90% confidence interval for total infections before the quarantine is released is 150 to 850.

It is virtually impossible that the initial 61 are the only 2019 n-CoV cases on board the Diamond Princess. Why?

The answer is found in our article from 2/3/2020 – this disease has a long incubation period when people are asymptomatic but potentially infectious. This means there is likely a large group of people who are asymptomatic and not in the group of 273 but who do have the virus (Figure 2).

Figure 2: Projected date of first symptoms of 2019 n-CoV on Diamond Princess, given no infections after the 2/3/2020 quarantine. (No y-axis value is given since a wide range of total infections is possible).

Put another way, if one or two individuals could start a chain reaction on 1/21/2020 that created 61 symptomatic passengers by 2/2 – 12 days later – how many people would those symptomatic passengers have infected in the days before the quarantine? The answer to this question lies in knowing the average and distribution of the incubation period.

Using the Incubation Period to Estimate Asymptomatic Cases

The mean incubation period has been estimated in the New England Journal of Medicine as 5.2 days, with the 95th percentile of the distribution at 12.5 days. We are slightly troubled taking the values from these early cases as canonical (the same case study indicated a doubling time of 7.4 days which is clearly inconsistent with cases multiplying from 41 on 1/1/2020 to 32,000+ on 2/6/2020).

However, this is the best evidence based estimate we have, and it seems consistent with other (more recent) anecdotal cases where exposure and first symptom times are well known (such as cases 19-22 in Singapore). The NEJM incubation estimate is well described by a lognormal function with a mean of 1.425 and a standard deviation of 0.67 (Figure 3).

Figure 3: The incubation time described by the recent NEJM article is well modeled by a lognormal curve (mu=1.425 and sigma = 0.67

The Utility of Probabilistic Models

Given that 61 people were symptomatic with the coronavirus by 2/2 or 2/3, how do we calculate the total number infected before the quarantine? We do this with a Monte Carlo model – using the incubation time described above and solve for what the doubling interval would have to be to produce 56 to 61 symptomatic people by 2/2 or 2/3. It matters a great deal if the original group of 273 was selected on 2/2 to be tested or 2/3. This can be seen visually in Figure 2 (the proportion of the curve up to 2/2 is much smaller than to 2/3).

The best fit if the group was selected on 2/2 would be a doubling time of 1.33 days, implying 530 people would have been infected by the quarantine. If the group was selected on 2/3, the best fit doubling time would drop to 1.48 – implying a best guess of 275 people infected prior to the quarantine.

Additional Sources of Uncertainty

Note that 275 to 530 does not represent our 90% confidence interval. We would place about a 60% chance of certainty on that range. However, there are significant additional sources of uncertainty:

  1. Most importantly, the incubation could be significantly different than what the early evidence indicated. This could make the range go up or down.
  2. We don’t know the passenger from Hong Kong was the only one who had 2019 n-CoV. Other passengers could have had it or gotten infected early on in other ports. If so, this would make the range go down.
  3. If passengers on board are sharing rooms, there is the possibility for further infections after the quarantine started. If that occurs, this would make the final infection number go up.

The best-case scenario is that the tested group was symptomatic on 2/3/2020, that there was another source of coronavirus on board by 1/21/2020, that the true incubation is shorter, and that there were relatively few infections after the quarantine. In this case, the best estimate of infections would be around 150.

The worst-case scenario is if the tested group was symptomatic by 2/2/2020, there were no other cases of 2019 n-CoV on the ship on 1/21/2020, the true incubation period is longer, and there were significant infections of family members after the quarantine. In this case, the best estimate of infections would be around 850.

Therefore, our 90% confidence interval for all infected passengers before the ship is released, assuming the quarantine is maintained, is between 150 and 850.

Drawing the Right Conclusions

Here, it’s important to provide a non-quantitative side-note. The people who are sick or quarantined on the ship are not numbers on a page – they are real people who are facing a difficult trial. My thoughts go out to these people, and my gratitude to the cruise operators and all the medical and support staff that are helping in this situation. May they be given strength and not fear their situation; may the sick passengers be comforted by the knowledge that Japan has great medical services and there are hopeful signs regarding treatment.

Additionally, it is important that other passengers draw the correct conclusions if additional cases are found in the coming days. Panic is not the best response. In fact, part of the motivation to write this article is to point out that we should all assume there will be reports of additional cases of people who were infected before the quarantine. This is crucial, and my hope is that this message can find its way to the passengers to prevent further unnecessary worry.

Macro Implications

There are potentially valuable implications that come out of this extremely unfortunate situation. This may provide a stark illustration of the hidden population of 2019 n-CoV – although only 61 people were symptomatic on 2/3/2020, there were likely hundreds already infected. This estimate, if correct, is vital to the understanding of policymakers and how the outbreak needs to be handled.

Also, I believe that we should avoid condemning the quarantine as ineffective and should not unnecessarily scapegoat the Diamond Princess staff or Carnival policies. In no way do I intend this to be “apologist” – rather let’s draw the correct conclusions from the data and not give in to emotional but incorrect reasoning.

More importantly, if the “hidden” population of asymptomatic infected people is significantly larger than the population of known cases, then the idea of “best practice” currently adopted in countries other than China may need to change. At this point, we aren’t willing to speculate what those changes might be, but it is worth starting to think about. We will learn a lot by the rate of infection revealed on the cruise ship over the next 7-10 days. Roughly 90% of those infected on or before 2/2/2020 should be symptomatic by 2/10/2020, so we should have a good idea of the total number within a week.


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.




Measuring the wrong variables is a Trojan horse that infiltrates virtually every organization. This phenomenon has a real cost that can be measured – and avoided.


A measurement isn’t useless if the sample size is small. You can actually use small sample sizes to learn something useful about anything – and use that insight to make better decisions.

Five Data Points Can Clinch a Business Case

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.


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