| Autor: |
Song, Kuan, Jiao, Shiqi, Zhu, Qiang, Wu, Huitao |
| Zdroj: |
IEEE Engineering Management Review; Sep2020, Vol. 48 Issue 3, p63-71, 9p |
| Abstrakt: |
To reopen the economy safely during the COVID-19 pandemic, governments need the capability to proactively identify new and often asymptomatic infections, as well as contact tracing. Policymakers and public health professionals need a sampling-testing method that can achieve broad population coverage without overwhelming medical workers. We observe that COVID-19 high-risk groups are located in the hubs and cliques of our geo-social network, formed by the close encounters of people during daily life. These individuals are the de facto “canary in a coal mine”. We propose that nations offer free and anonymous testing service to them. With open-source computer algorithms and datasets, only a small fraction of the population selected for COVID-19 testing can cover the majority of high-exposure-risk individuals. A 0.3% sampled testing for a megacity covers 3/4 of its entire population. A 3% sampled testing for a rural town covers 3/4 of its entire population. With government oversight and public consent, this approach can serve each province/state or city/township for decentralized daily testing planning. However, to protect privacy, we recommend constructing the geo-social network of anonymized cellphones, not named individuals. This infrastructure should be dismantled once the pandemic is largely over. This can be achieved by policymakers, health workers, and engineers together in solidarity. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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