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COVID-19 infection has been reported to be caused by droplet and contact infection. This paper proposes a model that visualizes the risk of contact infection to family members when viruses spread to various items at home. Behavior data after returning home are extracted from a questionnaire-based survey of home behavior to design the agent-based model. The data tables of contact behavior are created, including the room-to-room transfer probability table, the conditional probability table, and the contact probability table. The material transfer efficiency table is also created by measuring the virus transmission rate after contact with droplets in a virus experiment laboratory. In the experiment, the synthetic agent created from the acquired data probabilistically performs movement and contact behavior after returning home and reproduces the state in which the virus attached to the hand or belongings, when going out, propagates to objects at home. Next, we examine the risk of a second family member returning home. As a result, virus-attached contacts within around 30 minutes after returning home are widely confirmed around the entrance and kitchen, suggesting the effectiveness of early hand-washing behavior. And the experiment shows that even if the first person returning home disinfects their hands inside the entrance, the virus remains in a part of the entrance, and the virus is spread inside the room by the second person returning home.

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The objective of this research is to investigate the features of an inter-firm trade structure model. We have developed this model through agent-based simulation. In order to adapt to rapid changes in the business market, each firm must deal with changes in requirements and the required volume changes. Thus, the model has two combined functions. The first function allows a firm to freely trade with other firms as a decentralized trade structure. The second function allows a firm to mediate other firms as a centralized trade structure. The combined model has worked well in various test cases so far. However, other trading environments have not been fully investigated. Therefore, this study focuses on the detailed environmental conditions: (1) the number of similar firms and (2) the number of procurement items, and deals with intensive experiments and detailed analysis of the two functions of the model in the agent-based simulations. The simulation results suggest that the two functions in the combined model work particularly well under the conditions of a trading environment in which there exist both (1) a large number of similar business firms and (2) firms with a certain number of procurement items, compared to the decentralized trade structure, where each firm’s size is neither particularly large nor small.

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