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The impact of non-pharmaceutical interventions on the prevention and control of COVID-19 in New York City
Jiannan Yang; Qingpeng Zhang; zhidong cao; Jianxi gao; Dirk Pfeiffer; Lu Zhong; Daniel Dajun Zeng.
Affiliation
  • Jiannan Yang; City University of Hong Kong
  • Qingpeng Zhang; City University of Hong Kong
  • zhidong cao; Institute of Automation, Chinese Academy of Sciences
  • Jianxi gao; Rensselaer Polytechnic Institute
  • Dirk Pfeiffer; City University of Hong Kong
  • Lu Zhong; Rensselaer Polytechnic Institute
  • Daniel Dajun Zeng; Institute of Automation, Chinese Academy of Sciences
Preprint in English | medRxiv | ID: ppmedrxiv-20242347
Journal article
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ABSTRACT
The emergence of coronavirus disease 2019 (COVID-19) has infected more than 37 million people worldwide. The control responses varied across countries with different outcomes in terms of epidemic size and social disruption. In this study, we presented an age-specific susceptible-exposed-infected-recovery-death model that considers the unique characteristics of COVID-19 to examine the effectiveness of various non-pharmaceutical interventions (NPIs) in New York City (NYC). Numerical experiments from our model show that the control policies implemented in NYC reduced the number of infections by 72% (IQR 53-95), and the number of deceased cases by 76% (IQR 58-96) by the end of 2020, respectively. Among all the NPIs, social distancing for the entire population and the protection for the elderly in the public facilities is the most effective control measure in reducing severe infections and deceased cases. School closure policy may not work as effectively as one might expect in terms of reducing the number of deceased cases. Our simulation results provide novel insights into the city-specific implementation of NPIs with minimal social disruption considering the locations and population characteristics.
License
cc_by_nc_nd
Full text: Available Collection: Preprints Database: medRxiv Type of study: Experimental_studies / Observational study Language: English Year: 2020 Document type: Preprint
Full text: Available Collection: Preprints Database: medRxiv Type of study: Experimental_studies / Observational study Language: English Year: 2020 Document type: Preprint
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