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原文連結
論文資訊
- 類型：已發表論文
- 日期：2024-11-26
摘要
Identifying effective treatments and policies early in a pandemic is challenging because only limited and noisy data are available and 生物 processes are unknown or uncertain. Consequently, classical 統計 procedures may not work or require strong structural assumptions. We present an 資訊-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 資訊 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 統計 analysis using an international sample with millions of observations available at
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