Selected article for: "bayesian model and time series"

Author: Varghese, Chris; Xu, William
Title: Quantifying what could have been – the impact of the Australian and New Zealand governments’ response to COVID-19
  • Cord-id: hzpbujye
  • Document date: 2020_5_27
  • ID: hzpbujye
    Snippet: The Australian and New Zealand governments both initiated strict social distancing measures in response to the COVID-19 pandemic in late March. It remains difficult to quantify the impact this had in reducing the spread of the virus. Bayesian structural time series model provide a model to quantify the scenario in which these government-level interventions were not placed. Our models predict these strict social distancing measures caused a 79% and 61% reduction in the daily cases of COVID-19 acr
    Document: The Australian and New Zealand governments both initiated strict social distancing measures in response to the COVID-19 pandemic in late March. It remains difficult to quantify the impact this had in reducing the spread of the virus. Bayesian structural time series model provide a model to quantify the scenario in which these government-level interventions were not placed. Our models predict these strict social distancing measures caused a 79% and 61% reduction in the daily cases of COVID-19 across Australia and New Zealand respectively. This provides both evidence and impetus for governments considering similar measures in response to COVID-19 and other pandemics.

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