Technische Universität Berlin, FG Verkehrssystemplanung und Verkehrstelematik, 10623, Berlin, Germany.
Zuse Institute Berlin, 14195, Berlin, Germany.
Sci Rep. 2024 Jun 24;14(1):14475. doi: 10.1038/s41598-024-64230-1.
Stay-at-home orders were introduced in many countries during the COVID-19 pandemic, limiting the time people spent outside their home and the attendance of gatherings. In this study, we argue from a theoretical model that in many cases the effect of such stay-at-home orders on incidence growth should be quadratic, and that this statement should also hold beyond COVID-19. That is, a reduction of the out-of-home duration to, say, 70% of its original value should reduce incidence growth and thus the effective R-value to of its original value. We then show that this hypothesis can be substantiated from data acquired during the COVID-19 pandemic by using a multiple regression model to fit a combination of the quadratic out-of-home duration and temperature to the COVID-19 growth multiplier. We finally demonstrate that many other models, when brought to the same scale, give similar reductions of the effective R-value, but that none of these models extend plausibly to an out-of-home duration of zero.
在 COVID-19 大流行期间,许多国家都发布了居家令,限制人们离家在外的时间和聚会的出席人数。在这项研究中,我们从理论模型出发认为,在许多情况下,这种居家令对发病率增长的影响应该是二次方的,而且这种说法应该也适用于 COVID-19 之外的情况。也就是说,将外出时间减少到原来的 70%,应该会降低发病率,从而将有效 R 值降低到原来的 0.49。然后,我们通过使用多元回归模型将二次方的外出时间和温度与 COVID-19 增长率相乘,从 COVID-19 大流行期间获得的数据中证明了这一假设。最后,我们证明了许多其他模型在相同的规模下也会给出类似的有效 R 值降低,但没有一个模型可以合理地扩展到零外出时间。