We estimate that the combination of changes in behavior to slow the spread of COVID-19 and the delivery of vaccines to a substantial majority of the American population by mid-2021 saved close to 800,000 American lives relative to what would have occurred had vaccines not been developed. We argue that the duration and magnitude of this behavioral response—and thus its overall success in delaying infections—came as a surprise, relative to both our historical experience with pandemic influenza and to model-based projections based on that experience. Thus, we take from our experience with COVID-19 over the past four years the important public health lesson that behavior change can be a powerful force for slowing the spread of a dangerous infectious respiratory disease for a long time. At the same time, these behavioral changes to slow the spread of COVID-19 came at a tremendous economic, social, and human cost. To avoid similar pain from mitigation in the next pandemic, we argue that we need to make investments now not only in vaccine development, but also in data infrastructure so that we can precisely target behavior-oriented mitigation efforts to minimize their economic and social impacts in the next pandemic.

我们估计，与没有开发疫苗的情况相比，到 2021 年年中，为减缓 COVID-19 传播而改变行为并与向绝大多数美国人口提供疫苗相结合，挽救了近 800,000 名美国人的生命。我们认为，相对于我们在大流行性流感方面的历史经验和基于该经验的基于模型的预测，这种行为反应的持续时间和幅度——以及它在延迟感染方面的总体成功——都令人惊讶。因此，我们从过去四年的 COVID-19 经验中吸取了重要的公共卫生教训，即行为改变可以成为长期减缓危险传染性呼吸道疾病传播的强大力量。与此同时，这些减缓 COVID-19 传播的行为改变付出了巨大的经济、社会和人力成本。为了避免在下一次大流行中因缓解而产生类似的痛苦，我们认为我们现在不仅需要投资于疫苗开发，还需要投资于数据基础设施，以便我们能够精确地针对以行为为导向的缓解工作，以最大限度地减少它们在下一次大流行中的经济和社会影响。