Abstract
Identifying effective treatments and policies early in a pandemic is challenging because only limited and noisy data are available and biological processes are unknown or uncertain. Consequently, classical statistical procedures may not work or require strong structural assumptions. We present an information-theoretic approach that can overcome these problems and identify effective treatments and policies. The efficacy of this approach is illustrated using a study conducted at the beginning of the COVID-19 pandemic. We applied this approach with and without prior information to the limited international data available in the second month (24 April 2020) of the COVID-19 pandemic. To check if our results were plausible, we conducted a second statistical analysis using an international sample with millions of observations available at the end of the pandemic's pre-vaccination period (mid-December 2020). Even with limited data, the information-theoretic estimates from the original study performed well in identifying influential factors and helped explain why death rates varied across nations. Later experiments and statistical analyses based on more recent, richer data confirm that these factors contribute to survival. Overall, the proposed information-theoretic statistical technique is a robust method that can overcome the challenges of under-identified estimation problems in the early stages of medical emergencies. It can easily incorporate prior information from theory, logic, or previously observed emergencies.