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Identify successful restrictions in suppressing the early outbreak of COVID-19 in Arizona, United States: Interrupted time series analysis

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  • Ali Hadianfar
  • Milad Delavary
  • Martin Lavallière
  • Amir Nejatian
  • Omid Mehrpour

Abstract

COVID-19 was responsible for many deaths and economic losses around the globe since its first case report. Governments implemented a variety of policies to combat the pandemic in order to protect their citizens and save lives. Early in 2020, the first cases were reported in Arizona State and continued to rise until the discovery of the vaccine in 2021. A variety of strategies and interventions to stop or decelerate the spread of the pandemic has been considered. It is recommended to define which strategy was successful for disease propagation prevention and could be used in further similar situations. This study aimed to evaluate the effect of people’s contact interventions strategies which were implemented in Arizona State and their effect on reducing the daily new COVID-19 cases and deaths. Their effect on daily COVID-19 cases and deaths were evaluated using an interrupted time series analysis during the pandemic’s first peaks to better understand the onward situation. Canceling the order of staying at home (95% CI, 1718.52 to 6218.79; p

Suggested Citation

  • Ali Hadianfar & Milad Delavary & Martin Lavallière & Amir Nejatian & Omid Mehrpour, 2023. "Identify successful restrictions in suppressing the early outbreak of COVID-19 in Arizona, United States: Interrupted time series analysis," PLOS ONE, Public Library of Science, vol. 18(11), pages 1-11, November.
    • Handle: RePEc:plo:pone00:0291205
    DOI: 10.1371/journal.pone.0291205
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    References listed on IDEAS

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    1. Ali Hadianfar & Razieh Yousefi & Milad Delavary & Vahid Fakoor & Mohammad Taghi Shakeri & Martin Lavallière, 2021. "Effects of government policies and the Nowruz holidays on confirmed COVID-19 cases in Iran: An intervention time series analysis," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-11, August.
    2. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
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