Understanding the outbreak dynamics of COVID-19 through the lens of mathematical models is an elusive but significant goal. Within only half a year, the COVID-19 pandemic has resulted in more than 19 million reported cases across 188 countries with more than 700,000 deaths worldwide. Unlike any other disease in history, COVID-19 has generated an unprecedented volume of data, well documented, continuously updated, and broadly available to the general public. Yet, the precise role of mathematical modeling in providing quantitative insight into the COVID-19 pandemic remains a topic of ongoing debate. Here we discuss the lessons learned from six month of modeling COVID-19. We highlight the early success of classical models for infectious diseases and show why these models fail to predict the current outbreak dynamics of COVID-19. We illustrate how data-driven modeling can integrate classical epidemiology modeling and machine learning to infer critical disease parameters—in real time—from reported case data to make informed predictions and guide political decision making. We critically discuss questions that these models can and cannot answer and showcase controversial decisions around the early outbreak dynamics, outbreak control, and exit strategies. We anticipate that this summary will stimulate discussion within the modeling community and help provide guidelines for robust mathematical models to understand and manage the COVID-19 pandemic. EML webinar speakers, videos, and overviews are updated at https://imechanica.org/node/24098.

通过数学模型的视角了解 COVID-19 的爆发动态是一个难以实现但意义重大的目标。在短短半年内，COVID-19 大流行导致 188 个国家/地区报告了超过 1900 万例病例，全球死亡人数超过 70 万人。与历史上的任何其他疾病不同，COVID-19 产生了前所未有的数据量，记录完备，不断更新，并广泛提供给公众。然而，数学建模在提供对 COVID-19 大流行的定量洞察方面的确切作用仍然是一个持续争论的话题。在这里，我们讨论从六个月的 COVID-19 建模中吸取的教训。我们强调了传染病经典模型的早期成功，并说明了为什么这些模型无法预测 COVID-19 当前的爆发动态。我们说明了数据驱动建模如何将经典流行病学建模和机器学习结合起来，以实时从报告的病例数据中推断出关键疾病参数，从而做出明智的预测并指导政治决策制定。我们批判性地讨论这些模型可以回答和不能回答的问题，并展示围绕早期爆发动态、爆发控制和退出策略的有争议的决定。我们预计该摘要将激发建模社区内的讨论，并有助于为强大的数学模型提供指导，以理解和管理 COVID-19 大流行。EML 网络研讨会演讲者、视频和概述在 https://imechanica.org/node/24098 上更新。