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

Xiaoqi Zhang, Zheng Ji, Yanqiao Zheng, Xinyue Ye, Dong Li

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

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 within- spreading 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.

Original languageEnglish (US)
Article number102869
JournalCities
Volume107
DOIs
StatePublished - Dec 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Development
  • Sociology and Political Science
  • Urban Studies
  • Tourism, Leisure and Hospitality Management

Keywords

  • COVID-19
  • China
  • City lock-down
  • Counterfactual analysis
  • Deep learning
  • Network science

Fingerprint

Dive into the research topics of 'Evaluating the effect of city lock-down on controlling COVID-19 propagation through deep learning and network science models'. Together they form a unique fingerprint.

Cite this