English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

The special epistemic characteristics of the COVID-19, such as the longincubation period and the infection through asymptomatic cases, put severechallenge to the containment of its outbreak. By the end of March 2020, Chinahas successfully controlled the within-spreading of COVID-19 at a high cost oflocking down most of its major cities, including the epicenter, Wuhan. Sincethe low accuracy of outbreak data before the mid of Feb. 2020 forms a majortechnical concern on those studies based on statistic inference from the earlyoutbreak. We apply the supervised learning techniques to identify and trainNP-Net-SIR model which turns out robust under poor data quality condition. Bythe trained model parameters, we analyze the connection between population flowand the cross-regional infection connection strength, based on which a set ofcounterfactual analysis is carried out to study the necessity of lock-down andsubstitutability between lock-down and the other containment measures. Ourfindings support the existence of non-lock-down-typed measures that can reachthe same containment consequence as the lock-down, and provide useful guidelinefor the design of a more flexible containment strategy.

Evaluating the effect of city lock-down on controlling COVID-19 propagation through deep learning and network science models