Wildlife Analysis GmbH, Oetlisbergstrasse 38, 8053, Zurich, Switzerland.
Department of Integrative Biology, University of Wisconsin-Madison, Madison, WI, 53706, USA.
Sci Rep. 2023 Aug 31;13(1):14277. doi: 10.1038/s41598-023-40990-0.
During the COVID-19 pandemic, many quantitative approaches were employed to predict the course of disease spread. However, forecasting faces the challenge of inherently unpredictable spread dynamics, setting a limit to the accuracy of all models. Here, we analyze COVID-19 data from the USA to explain variation among jurisdictions in disease spread predictability (that is, the extent to which predictions are possible), using a combination of statistical and simulation models. We show that for half the counties and states the spread rate of COVID-19, r(t), was predictable at most 9 weeks and 8 weeks ahead, respectively, corresponding to at most 40% and 35% of an average cycle length of 23 weeks and 26 weeks. High predictability was associated with high cyclicity of r(t) and negatively associated with R values from the pandemic's onset. Our statistical evidence suggests the following explanation: jurisdictions with a severe initial outbreak, and where individuals and authorities took strong and sustained protective measures against COVID-19, successfully curbed subsequent waves of disease spread, but at the same time unintentionally decreased its predictability. Decreased predictability of disease spread should be viewed as a by-product of positive and sustained steps that people take to protect themselves and others.
在 COVID-19 大流行期间，许多定量方法被用于预测疾病传播的过程。然而，预测面临着传播动态本质上不可预测的挑战，这限制了所有模型的准确性。在这里，我们使用统计和模拟模型的组合，分析来自美国的 COVID-19 数据，以解释疾病传播可预测性（即预测的可能性）在司法管辖区之间的差异。我们表明，对于一半的县和州，COVID-19 的传播率 r(t) 最多可在 9 周和 8 周前预测，分别对应于平均周期长度 23 周和 26 周的 40%和 35%。高可预测性与 r(t) 的高周期性相关，与大流行开始时的 R 值呈负相关。我们的统计证据表明了以下解释：在最初爆发严重的司法管辖区，个人和当局采取了强有力和持续的 COVID-19 保护措施，成功遏制了随后的疾病传播浪潮，但同时也无意中降低了其可预测性。疾病传播可预测性的降低应被视为人们为保护自己和他人而采取的积极和持续措施的副产品。
