Why No COVID-19 Models Have Been Accurate, And How To Fix That

1. While these models can sometimes provide decision makers useful information, the decisions that are being made during this crisis are far too important and complex to be based on such imprecise data.
2. For COVID-19, we likely need a set of models for medical and economic decisions that augment final decision-support models that help the decision makers weigh their options.
3. For COVID-19, we likely need a set of models for medical and economic decisions that augment final decision-support models that help the decision makers weigh their options.
4. The key is participation from a diverse set of subject-matter experts from interdisciplinary backgrounds working together to build scenario models that help decision makers assess the decision options in terms of probability of the possible outcomes.
5. With the COVID-19 models, the so-called “news” appears to be using either the confidence interval from one model or actual estimated values (i.e., means) from different models as a way of reporting a range of the “predicted” number of people who may contract or die from the disease (e.g., 60,000 to 2 million).
6. First of all, in terms of helping decision makers make quality decisions, statistical hypothesis testing and data analysis is just one tool in a large tool box.
7. Either way, these types of results are an indication of bias in the data, which can come from many sources (such as not enough data, measurement error, reporting error, using too many variables, etc.). For the COVID-19 models, most of the data appears to come from large population centers like New York.
8. In fact, for predicting outcomes within complex and adaptive and dynamic systems, where controlled experiments are not possible, data is lacking, and large amounts of uncertainty exist, the reductionists’ tool is not useful.

https://thefederalist.com/2020/04/27/why-no-covid-19-models-have-been-accurate-and-how-to-fix-that/