Abstract
How data became one of the most powerful tools to fight an epidemic is a question that a recent (10 June 2020) The New York Times article poses in its title. Indeed, the spread of COVID-19 involves dynamically evolving hidden data (for example, the number of infected people, the number of asymptomatic people) that must be deduced from noisy and partially observed data (for example, the number of daily deaths, the number of daily hospitalizations, and the number of daily positive tests). The underlying mathematics for posing and solving this and several other partially observed dynamic problems is familiar to control theorists.
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