Response to the Coronavirus Disease-2019 Pandemic
Lessons Learned from the Taiwan Model
Bibliographic Data
| ID | 21828581 |
|---|---|
| Authors | Charles Chong-Han Wu (0000-0003-0898-377X), Charles Wu, Catherine Wu (0000-0002-0506-9774), Catherine Z Wu (National United University), Kun Wu (0000-0002-2357-3288), Kun Chan Wu |
| Year | 2020 |
| Volume | 16 |
| Issue | 10 |
| Pages | 16 |
| Publication date | 2020-09-24 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Asian Social Science (JOURNAL) |
| Journal identifiers | ISSN: 1911-2017 • E-ISSN: 1911-2025 |
| Publisher | Canadian Center of Science and Education (PUBLISHER) |
| DOI | 10.5539/ass.v16n10p16 |
| OpenAlex | W3088032912 |
| Language | EN |
| Citations received | 2 |
| References cited | 2 |
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), or coronavirus disease-2019 (COVID-19), emerged in December 2019 in Wuhan, China and has since then spurred a global pandemic (Lai et al., 2020). Taiwan and China, separated only by 130 km across the Taiwan Strait, have frequent cross-strait interactions with each other; millions of people travel to and from between the two countries (Wang & Lin, 2020). Considering these facts, Lauren Gardner, an associate professor at the Johns Hopkins University, even predicted that Taiwan will have the second highest number of COVID-19 cases among the world using a metapopulation model (Gardner et al., 2020). However, with a population of 23.7 million people, Taiwan leads one of the least COVID-19 cases worldwide. With the help of technology, swift reactions, advanced deployment of resources, and complete transparency, the Taiwan model has made its success. By analyzing the actions taken and how they functioned in Taiwan in preventing a nationwide epidemic, other countries may benefit in understanding how to design better models for the prevention of future epidemics and pandemics
China · Coronavirus · Development economics · Disease · Economic growth · Economics · Geography · Outbreak · Pandemic · Political science · Population · Socioeconomics · Sociology · COVID-19 and Mental Health · COVID-19 epidemiological studies · COVID-19 Pandemic Impacts · Demography · Medicine · Virology
| Citations per year | 0.33 |
|---|---|
| Citation span | 2020 - 2022 (3) |
| Highly cited | No |