Department of Biology, University of Vermont, Burlington, VT, United States of America.
Gund Institute for Environment, University of Vermont, Burlington, VT, United States of America.
PLoS One. 2020 Oct 13;15(10):e0240648. doi: 10.1371/journal.pone.0240648. eCollection 2020.
During an epidemic, metrics such as R0, doubling time, and case fatality rates are important in understanding and predicting the course of an epidemic. However, if collected over country or regional scales, these metrics hide important smaller-scale, local dynamics. We examine how commonly used epidemiological metrics differ for each individual state within the United States during the initial COVID-19 outbreak. We found that the detected case number and trajectory of early detected cases differ considerably between states. We then test for correlations with testing protocols, interventions and population characteristics. We find that epidemic dynamics were most strongly associated with non-pharmaceutical government actions during the early phase of the epidemic. In particular, early social distancing restrictions, particularly on restaurant operations, was correlated with increased doubling times. Interestingly, we also found that states with little tolerance for deviance from enforced rules saw faster early epidemic growth. Together with other correlates such as population density, our results highlight the different factors involved in the heterogeneity in the early spread of COVID-19 throughout the United States. Although individual states are clearly not independent, they can serve as small, natural experiments in how different demographic patterns and government responses can impact the course of an epidemic.
在疫情期间，R0、倍增时间和病死率等指标对于理解和预测疫情的进程非常重要。然而，如果从国家或地区层面进行收集，这些指标就会掩盖重要的、较小规模的本地动态。我们研究了在 COVID-19 疫情初期，美国每个州的常用流行病学指标有何不同。我们发现，各州的检测病例数量和早期检测病例的轨迹差异很大。然后，我们测试了与检测方案、干预措施和人口特征的相关性。我们发现，在疫情早期，非药物性政府措施与疫情动态的相关性最强。特别是，早期的社交距离限制，特别是对餐馆经营的限制，与倍增时间的增加有关。有趣的是，我们还发现，对违反强制规定的容忍度低的州，早期疫情增长更快。结合其他相关因素，如人口密度，我们的结果突出了 COVID-19 在全美早期传播中涉及的不同因素。尽管各州显然不是相互独立的，但它们可以作为小型的自然实验，研究不同的人口模式和政府反应如何影响疫情的进程。
