Golan Amos, Mumladze Tinatin, Perloff Jeffery M, Wilson Danielle
Department of Economics, American University, Washington, DC 20016, USA.
Santa Fe Institute, Santa Fe, NM 87501, USA.
Entropy (Basel). 2024 Nov 26;26(12):1021. doi: 10.3390/e26121021.
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.
在大流行早期识别有效的治疗方法和政策具有挑战性，因为可获得的数据有限且有噪声，生物过程也未知或不确定。因此，经典统计程序可能不起作用或需要很强的结构假设。我们提出一种信息论方法，该方法可以克服这些问题并识别有效的治疗方法和政策。通过一项在新冠疫情初期进行的研究说明了这种方法的有效性。我们将这种有无先验信息的方法应用于新冠疫情第二个月（2020年4月24日）可获得的有限国际数据。为了检验我们的结果是否合理，我们使用了在疫情疫苗接种前阶段结束时（2020年12月中旬）可获得数百万观测值的国际样本进行了第二次统计分析。即使数据有限，原始研究中的信息论估计在识别影响因素方面表现良好，并有助于解释各国死亡率为何不同。后来基于更新的、更丰富数据进行的实验和统计分析证实，这些因素有助于提高生存率。总体而言，所提出的信息论统计技术是一种稳健的方法，可以克服医疗紧急情况早期识别不足的估计问题带来的挑战。它可以轻松纳入来自理论、逻辑或先前观察到的紧急情况的先验信息。
