Response to COVID-19 in Taiwan: Big Data Analytics, New Technology, and Proactive Testing.
By C. J. Wang, C. Y. Ng, R. H. Brook. Journal of the American Medical Association (JAMA), 2020
1,335 citations recorded by the source corpus.
COVID-19 occurred just before the Lunar New Year during which time millions of Chinese and Taiwanese were expected to travel for the holidays Taiwan quickly mobilized and instituted specific approaches for case identification, containment, and resource allocation to protect the public health Taiwan enhanced COVID-19 case finding by proactively seeking out patients with severe respiratory symptoms (based on information from the National Health Insurance [NHI] database) who had tested negative for influenza and retested them for COVID-19;1 was found of 113 cases The toll-free number 1922 served as a hotline for citizens to report suspicious symptoms or cases in themselves or others;as the disease progressed, this hotline has reached full capacity, so each major city was asked to create its own hotline as an alternative It is not known how often this hotline has been used The government addressed the issue of disease stigma and compassion for those affected by providing food, frequent health checks, and encouragement for those under quarantine This rapid response included hundreds of action items Taiwan's government learned from its 2003 SARS experience and established a public health response mechanism for enabling rapid actions for the next crisis Well-trained and experienced teams of officials were quick to recognize the crisis and activated emergency management structures to address the emerging outbreak In a crisis, governments often make difficult decisions under uncertainty and time constraints These decisions must be both culturally appropriate and sensitive to the population Through early recognition of the crisis, daily briefings to the public, and simple health messaging, the government was able to reassure the public by delivering timely, accurate, and transparent information regarding the evolving epidemic Taiwan is an example of how a society can respond quickly to a crisis and protect the interests of its citizens
Subfields: Oncology
View Paper: https://www.semanticscholar.org/paper/d983427574a41bf485e31ab983acc1c742e9e24c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
Citations in the corpus
- Contact Tracing Mobile Apps for COVID-19: Privacy Considerations and Related Trade-offs (2020, background): “In Taiwan, medical institutions were given access to patients travel histories [7], and authorities track phone location data for anyone under quarantine [8].”
- COVI White Paper (2020, background): “Analysis of this data can provide a more precise understanding of the progression of the pandemic, which is critical to the creation of effective public health policy [38].”
- Using an extended Technology Acceptance Model to understand students’ use of e-learning during Covid-19: Indonesian sport science education context (2020, positive): “The necessary to use technologies for educational purposes in education during the pandemic is inescapable; some current studies have addressed this issue (Mailizar et al., 2020; Kerres, 2020; Wang et al., 2020).”
- A review of modern technologies for tackling COVID-19 pandemic (2020, background): “Countries like Taiwan infused the national medical insurance database with inputs from the immigration and customs database, hence confronting the coronavirus patients on the basis of their travel history and symptoms [4,5].”
- Multimodal biomedical AI (2022, background): “…the need for effective infectious disease surveillance at national and state levels 57 , with some countries successfully integrating multimodal data from migration maps, mobile phone utilization and health delivery data to forecast the spread of the outbreak and identify potential cases 58,59 .”
- Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model (2021, neutral): “In their study, the performance of 9 different prediction models based on three evaluation metrics is compared.”