Rapid and affordable methods of summarizing the multitude of data relating to the pandemic can be useful to health authorities and policy makers who are dealing with the COVID-19 pandemic at various levels in the territories affected by SARSCoV-2. This is the goal of the Synthetic COVID Index, an index based on an ensemble of Unsupervised Machine Learning techniques which focuses on the identification of a latent variable present in data that contains measurement errors. This estimated latent variable can be interpreted as "the strength of the pandemic". An application to the Italian case shows how the index is able to provide a concise representation of the situation.

翻译：在受SARSCOV-2影响的地区各级处理COVID-19流行病的卫生当局和决策者,可以采用快速和负担得起的方法,总结与该流行病有关的大量数据。这是合成COVID指数的目标,该指数基于一套不受监督的机器学习技术,重点是查明含有测量错误的数据中存在的潜在变量。这一估计的潜在变量可以被解释为“该流行病的强度”。意大利案例的一个应用表明该指数如何能够对情况提供简要的描述。