Information and signal processing tools are crucial for interpreting coronavirus disease 2019 (COVID-19) pandemic data. These tools allow us to extract, synthesize, and interpret pandemic information, thus providing valuable support to the decision-making authorities. This paper presents an overview of recent advances in information processing methodologies to combat the COVID-19 pandemic. First, we describe the quickest detection procedure designed to detect an exponential growth of positive cases with a mean delay of only a few days and a low risk of erroneously declaring an outbreak. Second, we present a Bayesian approach designed to estimate some features of the pandemic, e.g., the infection rate, and reliably forecast the evolution of the contagion.