Early SNS-Based Monitoring System for the COVID-19 Outbreak in Japan: A Population-Level Observational Study

  • Yoneoka Daisuke
    Department of Health Policy and Management, School of Medicine, Keio University Graduate School of Public Health, St. Luke’s International University Department of Global Health Policy, Graduate School of Medicine, The University of Tokyo
  • Kawashima Takayuki
    Department of Health Policy and Management, School of Medicine, Keio University Department of Mathematical and Computing Science, Tokyo Institute of Technology
  • Tanoue Yuta
    Department of Health Policy and Management, School of Medicine, Keio University Institute for Business and Finance, Waseda University
  • Nomura Shuhei
    Department of Health Policy and Management, School of Medicine, Keio University Department of Global Health Policy, Graduate School of Medicine, The University of Tokyo
  • Ejima Keisuke
    Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington
  • Shi Shoi
    Department of Systems Pharmacology, Graduate School of Medicine, The University of Tokyo Laboratory for Synthetic Biology, RIKEN Center for Biosystems Dynamics Research
  • Eguchi Akifumi
    Department of Sustainable Health Science, Center for Preventive Medical Sciences, Chiba University
  • Taniguchi Toshibumi
    Department of Infectious Diseases, Chiba University
  • Sakamoto Haruka
    Department of Health Policy and Management, School of Medicine, Keio University Department of Global Health Policy, Graduate School of Medicine, The University of Tokyo
  • Kunishima Hiroyuki
    Department of Infectious Diseases, St. Marianna University
  • Gilmour Stuart
    Graduate School of Public Health, St. Luke’s International University
  • Nishiura Hiroshi
    Graduate School of Medicine, Hokkaido University
  • Miyata Hiroaki
    Department of Health Policy and Management, School of Medicine, Keio University

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  • Early SNS-based screening system for the COVID-19 in Japan: a population-level observational study

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<p>Background: The World Health Organization declared the novel coronavirus outbreak (COVID-19) to be a pandemic on March 11, 2020. Large-scale monitoring for capturing the current epidemiological situation of COVID-19 in Japan would improve preparation for and prevention of a massive outbreak.</p><p>Methods: A chatbot-based healthcare system named COOPERA (COvid-19: Operation for Personalized Empowerment to Render smart prevention And care seeking) was developed using the LINE app to evaluate the current Japanese epidemiological situation. LINE users could participate in the system either though a QR code page in the prefectures’ websites or a banner at the top of the LINE app screen. COOPERA asked participants questions regarding personal information, preventive actions, and non-specific symptoms related to COVID-19 and their duration. We calculated daily cross correlation functions between the reported number of infected cases confirmed using polymerase chain reaction and the symptom-positive group captured by COOPERA.</p><p>Results: We analyzed 206,218 participants from three prefectures reported between March 5 and 30, 2020. The mean age of participants was 44.2 (standard deviation, 13.2) years. No symptoms were reported by 96.93% of participants, but there was a significantly positive correlation between the reported number of COVID-19 cases and self-reported fevers, suggesting that massive monitoring of fever might help to estimate the scale of the COVID-19 epidemic in real time.</p><p>Conclusions: COOPERA is the first real-time system being used to monitor trends in COVID-19 in Japan and provides useful insights to assist political decisions to tackle the epidemic.</p>

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