The COVID-19 pandemic emphasized the importance of epidemic forecasting for decision makers in multiple domains, ranging from public health to the economy. Forecasting epidemic progression is a non-trivial task due to multiple confounding factors, such as human behaviour, pathogen dynamics and environmental conditions. However, the surge in research interest and initiatives from public health and funding agencies has fuelled the availability of new data sources that capture previously unobservable aspects of disease spread, paving the way for a spate of ‘data-centred’ computational solutions that show promise for enhancing our forecasting capabilities. Here we discuss various methodological and practical advances and introduce a conceptual framework to navigate through them. First we list relevant datasets, such as symptomatic online surveys, retail and commerce, mobility and genomics data. Next we consider methods, focusing on recent data-driven statistical and deep learning-based methods, as well as hybrid models that combine domain knowledge of mechanistic models with the flexibility of statistical approaches. We also discuss experiences and challenges that arise in the real-world deployment of these forecasting systems, including decision-making informed by forecasts. Finally, we highlight some challenges and open problems found across the forecasting pipeline to enable robust future pandemic preparedness.

COVID-19 大流行强调了流行病预测对于从公共卫生到经济等多个领域决策者的重要性。由于人类行为、病原体动态和环境条件等多种混杂因素，预测流行病进展是一项艰巨的任务。然而，公共卫生和资助机构的研究兴趣和举措的激增，推动了新数据源的出现，这些数据源捕获了以前无法观察到的疾病传播方面，为大量“以数据为中心”的计算解决方案铺平了道路，这些解决方案有望增强我们的预测能力。在这里，我们讨论各种方法论和实践进展，并介绍一个概念框架来浏览它们。首先，我们列出相关数据集，例如症状在线调查、零售和商业、流动性和基因组数据。接下来我们考虑方法，重点关注最新的数据驱动的统计和基于深度学习的方法，以及将机械模型的领域知识与统计方法的灵活性相结合的混合模型。我们还讨论了这些预测系统在现实世界部署中出现的经验和挑战，包括根据预测制定决策。最后，我们强调了在预测过程中发现的一些挑战和未解决的问题，以便为未来的大流行病做好强有力的准备。