PEAK: Policy Event Assessment of COVID-19 Cases at the Start of the Pandemic in New York City

Amit Hiremath, Ziqian Dong, Roberto Rojas-Cessa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The impact of events and associated public health announcements on COVID-19 incidence remains an interesting and open question for future response and prevention. To address this issue, we propose a policy event impact assessment framework (PEAK) that quantifies the impact of policies and events on COVID-19 incidence in this paper. PEAK uses timeseries change point detection to estimate how health policies and events affected COVID-19 incidence during the most difficult period of the pandemic experienced in New York City and uses the long short-term memory for impact analysis at each change point. We analyze 26 public announcements on COVID19 and events that occurred in New York City from March 2020 to February 2021. The results show the top 10 largest-impact change points identified by PEAK and the events that caused such impacts.

Original languageEnglish (US)
Title of host publicationProceedings - 2023 IEEE 35th International Conference on Tools with Artificial Intelligence, ICTAI 2023
PublisherIEEE Computer Society
Pages507-512
Number of pages6
ISBN (Electronic)9798350342734
DOIs
StatePublished - 2023
Event35th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2023 - Atlanta, United States
Duration: Nov 6 2023Nov 8 2023

Publication series

NameProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
ISSN (Print)1082-3409

Conference

Conference35th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2023
Country/TerritoryUnited States
CityAtlanta
Period11/6/2311/8/23

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence
  • Computer Science Applications

Keywords

  • change point detection
  • correlation
  • COVID-19
  • impact analysis
  • LSTM
  • policy announcement
  • time series

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