Is the Fermi Paradox due to the Flaw of Averages?
Where is everybody? — Enrico Fermi The omnipresence of uncertainty is part of why making predictions and decisions is so …
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Adjusting the Santa Clara County COVID-19 Antibody testing study results for self-selection bias
The infection fatality rate (IFR) of COVID-19 is one of the most important parameters for mathematical models of the pandemic, …
The Suppression Triangle for COVID-19: How soon can we end the lockdown?
The COVID-19 pandemic has given many people a crash course in the frightening implications of rapid exponential growth in cases …
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A critique of the HealthData.org COVID-19 model, and how it works
(Posted on 15-Apr-2020) On 26-March-2020, researchers at the Institute for Health Metrics and Evaluation (IHME) at the University of Washington …
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Suppression strategy and updated forecast for US deaths from COVID-19 Coronavirus in 2020
(Posted 23-Mar-2020) Two weeks ago in the article “Estimating US Deaths from COVID-19 Coronavirus in 2020” [C20b], I developed a …
How social isolation impacts COVID-19 spread in the US: A Markov model approach
On Monday of this week, the Imperial College COVID-19 Response Team released an influential report that forecasts how social isolation …
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Estimating US Deaths from COVID-19 Coronavirus in 2020
We all are uncertain about how the future will unfold. This is a primary reason why decision making in business, public policy and our personal lives is so hard. As the COVID-19 Coronavirus pandemic continues to unfold [W20], we are all confronted numerous new decisions that hinge a lot on how bad this is going to get. And these are hard, because the uncertainty and the stakes are so big.
The mortality rate of the Wuhan coronavirus
It has only been about a month since the new strain of a coronavirus appeared in the city of Wuhan, …
Failure To Communicate: Why big data analytics projects fail and how to save them
If you are like most practitioners of analytics, you went into the field because you are good with numbers, and you want to help people improve the quality of decisions on important issues. But many of us spend too much time struggling to organize poorly structured data and debugging complex spreadsheets or code and too little time engaging directly with our clients to help them clarify their objectives and decisions, brainstorm better decision options, and explore, visualize and understand the results. Without this kind of interaction, the analysis may fail to address the issues they really care about. Even if it does, clients may not develop the confidence to rely on the insights, and you end up frustrated that your hard work fails to be properly appreciated.
Read the article at Orms Today Magazine, Dec 2019
What to do when your model results are ridiculous?
When an exhaustive scrutiny of the model code, the data and more sensitivity analyses don’t reveal why your model isn’t working, you must consider that perhaps it’s your intuition that is wrong. Learn why a normative framework like decision analysis can help vs treating human expertise and intuition as the “gold standard.”