Containing COVID-19 among 627,386 persons in contact with the diamond princess cruise ship passengers who disembarked in Taiwan: Big data analytics

Chi Mai Chen, Hong Wei Jyan, Shih Chieh Chien, Hsiao Hsuan Jen, Chen Yang Hsu, Po Chang Lee, Chun Fu Lee, Yi Ting Yang, Meng Yu Chen, Li Sheng Chen, Hsiu Hsi Chen, Chang Chuan Chan

Research output: Contribution to journalArticlepeer-review

118 Citations (Scopus)


Background: Low infection and case-fatality rates have been thus far observed in Taiwan. One of the reasons for this major success is better use of big data analytics in efficient contact tracing and management and surveillance of those who require quarantine and isolation. Objective: We present here a unique application of big data analytics among Taiwanese people who had contact with more than 3000 passengers that disembarked at Keelung harbor in Taiwan for a 1-day tour on January 31, 2020, 5 days before the outbreak of coronavirus disease (COVID-19) on the Diamond Princess cruise ship on February 5, 2020, after an index case was identified on January 20, 2020. Methods: The smart contact tracing-based mobile sensor data, cross-validated by other big sensor surveillance data, were analyzed by the mobile geopositioning method and rapid analysis to identify 627,386 potential contact-persons. Information on self-monitoring and self-quarantine was provided via SMS, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) tests were offered for symptomatic contacts. National Health Insurance claims big data were linked, to follow-up on the outcome related to COVID-19 among those who were hospitalized due to pneumonia and advised to undergo screening for SARS-CoV-2. Results: As of February 29, a total of 67 contacts who were tested by reverse transcription-polymerase chain reaction were all negative and no confirmed COVID-19 cases were found. Less cases of respiratory syndrome and pneumonia were found after the follow-up of the contact population compared with the general population until March 10, 2020. Conclusions: Big data analytics with smart contact tracing, automated alert messaging for self-restriction, and follow-up of the outcome related to COVID-19 using health insurance data could curtail the resources required for conventional epidemiological contact tracing.

Original languageEnglish
Article numbere19540
JournalJournal of Medical Internet Research
Issue number5
Publication statusPublished - May 2020


  • Big data
  • Contact tracing
  • COVID-19
  • Digital contact tracking
  • Mobile geopositioning
  • Precision public health
  • Proximity tracing
  • Public health
  • Surveillance
  • Virus

ASJC Scopus subject areas

  • Health Informatics


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