TEXT:

Although measuring national policy containing the health care, social, economic, and public security environment is difficult, the degree of success in the health policy or strategy against the COVID-19 pandemic can be simply assessed by the number of COVID-19 deaths.

Cheng C. et al. provided the useful dataset composed of over 13,000 such policy announcements across more than 195 countries for evaluating the effect of individual policies¹. Liang, L et al. indicated that the government policy or strategy plays a key role in suppressing the number of COVID-19 deaths². Ferrante, L et al. emphasized that the effective public health policy should be immediately executed for mitigating the COVID-19 pandemic³. Therefore, the dataset plays a key role in investigating the effect of individual policies^1,2,3.

The degree of success in individual policies can be simply examined by the death toll from the worldometer. COVID-19 data from the worldometer obviously shows that the most successful public health policy against COVID-19 has been implemented by Taiwan among many countries in the world.

Although Taiwan (23.78 million population) is not included in G20, Taiwan has a total population of 23.8 million with only 7 COVID-19 cumulative deaths as of Oct. 10 in 2020.

The worldometer as of Oct. 10 in 2020 shows the cumulative death toll among G20: 218,637 in the United States (328.2 million), 42,679 in UK(66.65 million), 32,583 in France (66.99 million), 23,225 in Argentina(44.49 million), 897 in Australia (24.99 million), 149,692 in Brazil(209.5 million), 9,585 in Canada (37.59 million), 4,634 in China (1.44 billion), 229,543 in Europe (747.76 million), 9,687 in Germany (83.8 million), 107,450 in India (1.38 billion), 11,677 in Indonesia (274.3 million), 36,111 in Italy (60.4 million), 1,616 in Japan (126.4 million), 83,096 in Mexico (129.2 million), 22,257 in Russia (145.9 million), 996 in Saudi Arabia (34.9 million), 17,547 in South Africa (59.5 million), 428 in South Korea (51.2 million), and 8,722 in Turkey (84.5 million) respectively.

With no vaccine or no effective treatment against the COVID-19, the complete prevention by isolating all at-risk is one and only one policy recommended by Hsiao-HuiTsou et al.⁴.

The problem of the dataset¹ provided by Cheng C. et al. lies in missing parameters on swiftness of policy action on isolation. Swiftness parameters on isolating all at-risk really play a key role in investigating the effect of health policy and mitigating the pandemic. Taiwan’s consequence using the real-time isolating-all-at-risk strategy shows that the swiftness parameters on isolation should be included or added in the dataset. In Taiwan, the real-time digital health system always functions in detecting and tracking infected persons, early isolation and border control of those, proactive case finding and containment, caring patients, resource allocation respectively for implementing policy and strategy. Taiwan is tracking 55,000 people under home quarantine in real time⁵. The real-time digital health system in Taiwan is implemented on a single-payer national health insurance (NHI) scheme that covers more than 99% of the population, and emergency funding has been approved to support COVID-19 prevention efforts and affected industries⁶.

CONCLUSION

The real-time surveillance in digital health plays a key role in mitigating the COVID-19 pandemic because we have an only isolating-all-at-risk strategy with no vaccine and no effective treatment. Real-time digital health system should be integrated for detecting and tracking infected persons, early isolation and border control of those, proactive case finding and containment, caring patients, resource allocation for implementing policy and strategy in order to mitigate the pandemic.

This research did not receive any specific funding. The authors declare no conflict of interest. The author has read the manuscript and has approved this submission. Ethical statement is not applicable.

References:

1.Cheng, C., Barceló, J., Hartnett, A.S. et al. COVID-19 Government Response Event Dataset (CoronaNet v.1.0). Nat Hum Behav 4, 756–768 (2020).https://doi.org/10.1038/s41562-020-0909-7

2. Liang, L., Tseng, C., Ho, H.J. et al. Covid-19 mortality is negatively associated with test number and government effectiveness.Sci Rep 10, 12567 (2020).https://doi.org/10.1038/s41598-020-68862-x

3. Ferrante, L., Steinmetz, W.A., Almeida, A.C.L. et al. Brazil’s policies condemn Amazonia to a second wave of COVID-19. Nat Med26, 1315 (2020).https://doi.org/10.1038/s41591-020-1026-x

4. Hsiao-HuiTsou et al., The effect of preventing subclinical transmission on the containment of COVID-19: Mathematical modeling and experience in Taiwan, Contemporary Clinical Trials, Vol 96, 2020https://doi.org/10.1016/j.cct.2020.106101

5. https://www.loc.gov/law/help/coronavirus-apps/taiwan.php

6. Linda Hsieh, What coronavirus success of Taiwan and Iceland has in common, June 29, 2020https://theconversation.com/what-coronavirus-success-of-taiwan-and-iceland-has-in-common-140455