Epidemiological models for COVID-19 have been inaccurate.
Epidemiological models for COVID-19 have been inaccurate. Some possible reasons include:
- Lack of high stakes out of sample feedback, allowing mediocre approaches to survive in academia for too long.
- Limited transfer of techniques from applied mathematics, such as the use of mathematical techniques for addressing the impact of variation (mathematical homogenization, for example) or term structure modeling (for example from interest rate modeling)
- Failure to adopt open source best practices and reproducible research.
- Mostly individual modeling failing to take advantage of ways of tapping in to collective intelligence in the sourcing of accurate predictions.
The unmet need is a platform for transparent, efficient, collective prediction of disease spread. Contributions should be judged fairly and statistically (out of sample) and not by softer measures such as the reputations of those involved.
Jul 11, 2020
This is a great solution. There is indeed a need of an epidemiological model that can track the disease spread and calculate the right predictions.