ABSTRACT
As people move around using public transportation networks, such as train and airplanes, it is expected that emerging infectious diseases will spread on the network. The scan statistics approach has been frequently applied to identify high-risk locations, and the results are widely used for making a clinical decisions in a timely manner. However, they are not optimally designed for modeling the spread and might not effectively work under the emergency situation where computational time is essentially important. We propose a new scan statistics approach for the public transportation network, called PTNS (Public Transportation Network Scan). PTNS utilizes the available network structure to construct potential candidates of clusters, and thus it can work well especially in situations where public transportation is the main medium of the infection spread. Further, it is designed for rapid surveillance. Lastly, PTNS is generalized to detect space-time clusters by customizing the iteration for potential clusters creation. Using the simulation data generated with a real railway network, we showed that, PTNS outperformed the conventional methods, including Circular- and Flex-scan approaches in terms of the detection performance, while the computational time is feasible.
ABSTRACT
OBJECTIVES: The Japanese prime minister declared a state of emergency on April 7 2020 to combat the outbreak of coronavirus disease 2019 (COVID-19). This declaration was unique in the sense that it was essentially driven by the voluntary restraint of the residents. We examined the change of the infection route by investigating contact experiences with COVID-19-positive cases. STUDY DESIGN: This study is a population-level questionnaire-based study using a social networking service (SNS). METHODS: To assess the impact of the declaration, this study used population-level questionnaire data collected from an SNS with 121,375 respondents (between March 27 and May 5) to assess the change in transmission routes over the study period, which was measured by investigating the association between COVID-19-related symptoms and (self-reported) contact with COVID-19-infected individuals. RESULTS: The results of this study show that the declaration prevented infections in the workplace, but increased domestic infections as people stayed at home. However, after April 24, workplace infections started to increase again, driven by the increase in community-acquired infections. CONCLUSIONS: While careful interpretation is necessary because our data are self-reported from voluntary SNS users, these findings indicate the impact of the declaration on the change in transmission routes of COVID-19 over time in Japan.