The COVID-19 pandemic disrupted health systems and economies throughout the world during 2020 and was particularly devastating for the United States, which experienced the highest numbers of reported cases and deaths during 2020^1,2,3. Many of the epidemiological features responsible for observed rates of morbidity and mortality have been reported^4,5,6,7,8; however, the overall burden and characteristics of COVID-19 in the United States have not been comprehensively quantified. Here we use a data-driven model-inference approach to simulate the pandemic at county-scale in the United States during 2020 and estimate critical, time-varying epidemiological properties underpinning the dynamics of the virus. The pandemic in the United States during 2020 was characterized by national ascertainment rates that increased from 11.3% (95% credible interval (CI): 8.3–15.9%) in March to 24.5% (18.6–32.3%) during December. Population susceptibility at the end of the year was 69.0% (63.6–75.4%), indicating that about one third of the US population had been infected. Community infectious rates, the percentage of people harbouring a contagious infection, increased above 0.8% (0.6–1.0%) before the end of the year, and were as high as 2.4% in some major metropolitan areas. By contrast, the infection fatality rate fell to 0.3% by year’s end.

COVID-19 大流行在 2020 年扰乱了全世界的卫生系统和经济，对美国造成的破坏尤其严重，美国在 2020 年报告的病例和死亡人数最多^1,2,3。已报告了许多导致观察到的发病率和死亡率的流行病学特征^4,5,6,7,8; 但是，尚未对美国 COVID-19 的总体负担和特征进行全面量化。在这里，我们使用数据驱动的模型推理方法来模拟 2020 年美国县级的大流行，并估计支撑病毒动态的关键的、随时间变化的流行病学特性。2020 年美国大流行的特点是全国确诊率从 3 月的 11.3%（95% 可信区间 (CI)：8.3-15.9%）增加到 12 月的 24.5%（18.6-32.3%）。年底的人口易感率为 69.0%（63.6-75.4%），表明美国约有三分之一的人口受到感染。社区传染率，即具有传染性感染者的百分比，在年底前增加了 0.8%（0.6-1.0%）以上，在一些主要大都市地区，这一比例高达 2.4%。相比之下，到年底，感染死亡率下降到 0.3%。