Regional Workshop for Policy Implementation Based on the Stay with Your Community Principles.

A co-author of this paper had previously presented the principle of "Stay with Your Community" as a method of countermeasures against COVID-19 infection spread and have been working on its social implementation. This case study paper presents an example of activities to spread the Stay with Your Community principle to citizens and visitors in Shimoda City, Shizuoka Prefecture, in order to control the spread of COVID-19 infection. As a result, the infection cluster was successfully controlled. The authors discuss the effect of the regional workshop as a key to open the way to Organizational Citizenship Behavior of participants.

Explanatory Change Detection in Financial Markets by Graph-Based Entropy and Inter-Domain Linkage.

In this study, we analyzed structural changes in financial markets under COVID-19 to support investors' investment decisions. Because an explanation of these changes is necessary to respond appropriately to said changes and prepare for similar major changes in the future, we visualized the financial market as a graph. The hypothesis was based on expertise in the financial market, and the graph was analyzed from a detailed perspective by dividing the graph into domains. We also designed an original change-detection indicator based on the structure of the graph. The results showed that the original indicator was more effective than the comparison method in terms of both the speed of response and accuracy. Explanatory change detection of this method using graphs and domains allowed investors to consider specific strategies.

Actual conditions of person-to-object contact and a proposal for prevention measures during the COVID-19 pandemic.

This study focused on human contact behavior with objects and discussed countermeasures during the COVID-19 pandemic across 15 location types. Reducing contact with objects and disinfecting items can be implemented at a relatively low cost. We created a protocol for organizing the objects, and 1260 subjects who went outside during a day between December 3-7, 2020 in Tokyo and Kanagawa, Japan were surveyed. The participants touched 7317 objects in total; the most common objects were doors, chairs, baskets, elevator equipment, and cash. One-way analysis of variance and Scheffé's multiple comparison test showed that supermarkets had the lowest mean and median values despite having the highest number of users, contact objects, and object types. Conversely, the values for hotels were the highest, significantly higher than that for other places, excluding amusement parks, workplaces, and schools and universities. Furthermore, the long-tailed frequency distribution of the number of objects suggests that the objects touched by many individuals are limited; thus, it is important to determine the objects to be prioritized for disinfection at each location. The data and protocol could inform infection countermeasures that properly address the contact realities as they pertain to people's behavior and objects.

Stay with your community: Bridges between clusters trigger expansion of COVID-19.

In this study, the spread of virus infection was simulated using artificial human networks. Here, real-space urban life was modeled as a modified scale-free network with constraints. To date, the scale-free network has been adopted for modeling online communities in several studies. However, in the present study, it has been modified to represent the social behaviors of people where the generated communities are restricted and reflect spatiotemporal constraints in real life. Furthermore, the networks have been extended by introducing multiple cliques in the initial step of network construction and enabling people to contact hidden (zero-degree) as well as popular (large-degree) people. Consequently, four findings and a policy proposal were obtained. First, "second waves" were observed in some cases of the simulations even without external influence or constraints on people's social contacts or the releasing of the constraints. These waves tend to be lower than the first wave and occur in "fresh" clusters, that is, via the infection of people who are connected in the network but have not been infected previously. This implies that the bridge between infected and fresh clusters may trigger a new spread of the virus. Second, if the network changes its structure on the way of infection spread or after its suppression, a second wave larger than the first can occur. Third, the peak height in the time series of the number of infected cases depends on the difference between the upper bound of the number of people each member actually meets and the number of people they choose to meet during the period of infection spread. This tendency is observed for the two kinds of artificial networks introduced here and implies the impact of bridges between communities on the virus spreading. Fourth, the release of a previously imposed constraint may trigger a second wave higher than the peak of the time series without introducing any constraint so far previously, if the release is introduced at a time close to the peak. Thus, overall, both the government and individuals should be careful in returning to society where people enjoy free inter-community contact.

Net-TF-SW: Event Popularity Quantification with Network Structure.

Event popularity quantification is essential in the determination of current trends in events on social media and the internet. Particularly, it is important during a crisis to ensure appropriate information transmission and prevention of false-rumor diffusion. Here, we propose Net-TF-SW - a noise-robust and explainable topic popularity analysis method. This method is applied to tweets related to COVID-19 and the Fukushima Daiichi Nuclear Disaster, which are two significant crises that have caused significant anxiety and confusion among Japanese citizens. The proposed method is compared to existing methods, and it is verified to be more robust with respect to noise.