Journal
CITIES
Volume 107, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.cities.2020.102869
Keywords
COVID-19; City lock-down; Counterfactual analysis; Deep learning; Network science; China
Categories
Funding
- Ministry of Education of China Project of Humanities and Social Sciences [20YJC790176]
- Fundamental Research Funds for the Central Universities [2242020S30024]
- National Science Foundation of USA [1937908]
- Office Of The Director
- Office of Integrative Activities [1937908] Funding Source: National Science Foundation
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The special epistemic characteristics of the COVID-19, such as the long incubation period and the infection through asymptomatic cases, put severe challenge to the containment of its outbreak. By the end of March 2020, China has successfully controlled the withinspreading of COVID-19 at a high cost of locking down most of its major cities, including the epicenter, Wuhan. Since the low accuracy of outbreak data before the mid of Feb. 2020 forms a major technical concern on those studies based on statistic inference from the early outbreak. We apply the supervised learning techniques to identify and train NP-Net-SIR model which turns out robust under poor data quality condition. By the trained model parameters, we analyze the connection between population flow and the cross-regional infection connection strength, based on which a set of counterfactual analysis is carried out to study the necessity of lock-down and substitutability between lock-down and the other containment measures. Our findings support the existence of non-lock-down-typed measures that can reach the same containment consequence as the lock-down, and provide useful guideline for the design of a more flexible containment strategy.
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