COVID-19 infection tends to be more lethal in older persons than in the young; death results from an overactive inflammatory response, leading to cytokine storm and organ failure. Here we describe immune regulation of the inflammatory response phenotype as emerging from a process that is analogous to machine-learning algorithms used in computers. We briefly describe some strategic similarities between immune learning and computer machine learning. We reason that a balanced response to COVID-19 infection might be induced by treating the elderly patient with a wellness repertoire of antibodies obtained from healthy young people. We propose that a beneficial training set of such antibodies might be administered in the form of intravenous immunoglobulin (IVIg).

中文翻译：

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免疫计算和COVID-19死亡率：IVIg的基本原理

老年人中COVID-19感染的致死率比年轻人高。炎症反应过度导致死亡，导致细胞因子风暴和器官衰竭。在这里，我们将炎症反应表型的免疫调节描述为一种过程，该过程类似于计算机中使用的机器学习算法。我们简要描述了免疫学习和计算机机器学习之间的一些战略相似之处。我们认为，通过从健康的年轻人那里获得抗体的健康目录来治疗老年患者，可以诱导对COVID-19感染的平衡反应。我们建议可以以静脉免疫球蛋白（IVIg）的形式给予此类抗体有益的培训。