Epidemic Forecasting with a Data-Centric Lens

The recent COVID-19 pandemic has reinforced the importance of epidemic forecasting to equip decision makers in multiple domains, ranging from public health to economics. However, forecasting the epidemic progression remains a non-trivial task as the spread of diseases is subject to multiple confounding factors spanning human behavior, pathogen dynamics and environmental conditions, etc. Research interest has been fueled by the increased availability of rich data sources capturing previously unseen facets of the epidemic spread and initiatives from government public health and funding agencies like forecasting challenges and funding calls. This has resulted in recent works covering many aspects of epidemic forecasting. Data-centered solutions have specifically shown potential by leveraging non-traditional data sources as well as recent innovations in AI and machine learning. This tutorial will explore various data-driven methodological and practical advancements. First, we will enumerate epidemiological datasets and novel data streams capturing various factors like symptomatic online surveys, retail and commerce, mobility and genomics data. Next, we discuss methods and modeling paradigms with a focus on the recent data-driven statistical and deep-learning based methods as well as novel class of hybrid models that combine domain knowledge of mechanistic models with the effectiveness and flexibility of statistical approaches. We also discuss experiences and challenges that arise in real-world deployment of these forecasting systems including decision-making informed by forecasts. Finally, we highlight some open problems found across the forecasting pipeline.