Enhancing Data Linkage to Break the Chain of COVID-19 Spread: The Taiwan Experience.

Digital technology has been widely used in health care systems and disease management, as well as in controlling the spread of COVID-19. As one of the most successful countries in combating the COVID-19 pandemic, Taiwan has successfully used digital technology to strengthen its efforts in controlling the COVID-19 pandemic. Taiwan has a well-established National Health Insurance System (NHIS), which provides a great opportunity to develop a nationwide data linkage model in an agile manner. Here we provide an overview of the application of data linkage models for strategies in combating COVID-19 in Taiwan, including NHIS centralized data linkage systems and "from border to community" information-driven data linkage systems during the COVID-19 pandemic. Furthermore, we discuss the dual role of digital technologies in being an "enabler" and a "driver" in early disease prevention. Lastly, Taiwan's experience in applying digital technology to enhance the control of COVID-19 potentially highlights lessons learned and opportunities for other countries to handle the COVID-19 situation better.

Containing COVID-19 Among 627,386 Persons in Contact With the Diamond Princess Cruise Ship Passengers Who Disembarked in Taiwan: Big Data Analytics.

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.