Early warning of SARS-CoV-2 infection

The recent COVID-19 pandemic, caused by the SARS-CoV-2 virus, has been particularly severe due to a combination of mortality rate and rapid spreading within communities and countries. After the first impact, social mixing, relaxation of nonpharmaceutical measures, new variances, and other complex factors yielded additional outbreaks, and infections diffused. Monitoring and alerting systems have thus been put in place based on big data about epidemic spreading and dynamical models. Ideally, such alerting systems should provide early warnings in addition to being reactive to new outbreaks. This chapter revises the main modeling tools that have been used worldwide to raise early alerts about incoming epidemic waves. It focuses on methods from mathematical epidemiology, ranging from statistical to mechanistic models, highlighting their intersections with other multidisciplinary approaches for epidemic monitoring and forecasting, thus providing practical guidelines to leverage them against this and future pandemics.