Given that real-world infection-spread scenarios pose many uncertainties, and predictions and simulations may differ from reality, this study explores factors essential for more realistically describing an infection situation. It furnishes three approaches to the argument that human mobility can create an acceleration of the spread of COVID-19 infection and its cyclicality under the simultaneous relationship. First, the study presents a dynamic model comprising the infection–mobility trade-off and mobility demand, where an increase in human mobility can cause infection explosion and where, conversely, an increase in new infections can be made temporary by suppressing mobility. Second, using time-series data for Japan, it presents empirical evidence for a stochastic trend and cycle in new infection cases. Third, it employs macroeconometrics to ascertain the feasibility of our model’s predictions. Accordingly, from March 2020 to May 2021, the sources of COVID-19 infection spread in Japan varied significantly over time, and each change in the trend and cycle of new infection cases explained approximately half the respective variation.

鉴于现实世界的感染传播场景存在许多不确定性，并且预测和模拟可能与现实不同，本研究探讨了更真实地描述感染情况所必需的因素。它提供了三种方法来论证人类流动性可以加速 COVID-19 感染的传播及其在同时关系下的周期性。首先，该研究提出了一个包含感染-流动性权衡和流动性需求的动态模型，其中人类流动性的增加可能导致感染爆炸，相反，通过抑制流动性可以暂时使新感染的增加。其次，使用日本的时间序列数据，它为新感染病例的随机趋势和周期提供了经验证据。第三，它采用宏观计量经济学来确定我们模型预测的可行性。因此，从 2020 年 3 月到 2021 年 5 月，在日本传播的 COVID-19 感染源随时间变化很大，新感染病例的趋势和周期的每一次变化都解释了各自变化的大约一半。