Abstract Our vulnerability to emerging infectious diseases has been illustrated with the devastating impact of the COVID-19 pandemic. Forecasting epidemic trajectories (such as future incidence over the next four weeks) gives policymakers a valuable input for designing effective healthcare policies and optimizing supply chain decisions; however, this is a non-trivial task with multiple open questions. In this workshop, we will go through current research and practice in epidemic forecasting, from recent machine learning innovations to real-time forecasting challenges, with an emphasis in data-driven computational methods. Research topics include (but not limited to) leveraging heterogenous and multimodal data, handling data quality issues, spatio-temporal modeling, auto-regressive time-series models, topic models, uncertainty quantification, mechanistic models, and neural epidemic models. We will also share practical insights from our applied research experience in real-time forecasting for the US Centers of Disease Control and Prevention in influenza and COVID-19, and how forecasts may be used to inform decision-making.
Date: Wednesday 01 December 2021
Time: 09.00-13.00 Eastern Time, 14.00-18.00 (London)