JMIR Publications

ABSTRACT

Background:

Policies recommending that individuals remain within their communities—including workplaces, schools, and conferences—were introduced globally to mitigate the spread of the COVID-19 pandemic. These policies meant to reduce the contacts of individuals from diverse communities: bubbling, where each should live within one's local community by reducing inter-community contacts, the Japanese policy “Stay with Your Community” (SWYC), where each sustain fewer contacts with individuals from unfamiliar communities than those in familiar ones, etc. These may be regarded as less stringent measures than Stay Home in that each can meet community members who are out of one’s family. However, these policies are inferred to be violated if individuals from various communities move to gather within close distances, which is a latent risk hard to detect in the normal conditions of society because people move complexly.

Objective:

Here, we aim to create a quantifiable physical index to assess the region's compliance with the SWYC guideline, serving as a reliable alert for the potential spread of SARS-CoV-2 in the context of a case study on infectious diseases.

Methods:

Moving Direction Entropy (MDE), which quantifies the diversity of moving directions of individuals in each local region, is proposed as an index to evaluate a region’s risk of violating the policy above due to the diversity of communities from which people come to meet. This index was computed for each local region using mobility data collected from smartphones.

Results:

First, the MDEs for local regions showed significant invariance between different periods according to Spearman's rank correlation coefficient (>0.9). Second, MDE was found to be significantly correlated with the rate of infection cases of COVID-19 over local populations in the 53 inland regions of Tokyo: 0.76 in average during the periods of expansion. The density of restaurants had a similar correlation with COVID-19. The densities of schools and listed companies were correlated with Influenza and STDs, respectively. Third, the spread of COVID-19 infection was accelerated in regions with high-rank MDEs than in those with high-rank densities of restaurants during and after the period governmental declarations of emergency (p < .001). This finding is explained as due to policymakers’ overlooking of MDE. Fourth, MDEs tended to be high and increased during the COVID-19 period in regions where influx or daytime movements were active.

Conclusions:

We propose monitoring the regional values of MDE to reduce the risk of infection spread. To aid this monitoring, we show a method to create a heat map of the MDE values, thereby drawing public attention to the hazards of behaviors that facilitate contact between communities during a highly infectious disease pandemic. Clinical Trial: We are not registered in a WHO-accredited trial registry. because this study used mobility data from smartphones and other available data without trials.