d4t4

I wish there were more creativity in AI “thought leadership” than “replace all white collar workers.”

This study makes a comparison of the age distribution in reported Covid-19 cases vs. the distribution of ages in the Chinese population. I think this approach can definitely provide insight however, there’s one assumption that this study uses which will definitely bias the fatality rates being estimated.

> We chose to fix to 100% the reporting rate of infected individuals that have symptoms and are aged 80 and more, and estimate the reporting rates in other age groups relatively to that of older individuals. If further data, coming from a study in the general population, shows that this assumption is violated, this would lead to an overestimation of the CFR in our study.

I find it hard to believe that 100% of cases in individuals over 80 will be reported. It seems like any estimation of fatality rate at this time is going to be guesswork without more systematic sampling of the population.

I’m not trying to minimize the danger of Covid-19. It seems like caution to prevent further spread is absolutely necessary.

While I agree that collecting more data opens up the possibility for more complex, exciting analysis, gene expression data is no magic bullet. It is often noisy and still prone to experimental error because biological systems are so heterogeneous. Also, for a lot of investigations, there are only a handful of samples which means that the number of distinct data points for a specific transcript are very low. You typically end up with many columns but few rows in your dataset which makes modeling biological systems quite challenging.

I work in financial services as a statistical model developer and we usually go through extensive legal review before we can use a model for a specific application. We always avoid using less controversial demographics (e.g. customer tenure as a surrogate for age) before we build models for less legally risky endeavors like marketing because of Fair Lending laws. It concerns me that gender made it into a credit risk model which have an even higher standard for legal oversight where I'm at. Fingers crossed that this is only a rare oversight on Goldman Sachs part.