在新冠肺炎期間，數學與統計模型被用來預測疫情趨勢以及量化控制政策的有效性。整合移動平均自迴歸模型(Automatic Regressive Integrated Moving Average, ARIMA)已被使用於預測時間序列。儘管保護行為與接種疫苗已被證實可以控制疫情，卻很少ARIMA模型將其納入考慮。此研究加入了從新冠肺炎行為追蹤器(COVID-19 Behavior Tracker)收集的預測子(戴口罩、避免外出與打疫苗)發展出新的ARIMA模型，以增加預測每周病例增長率的準確性。此模型分別捕捉了加拿大、法國、義大利與以色列在2021年1月至2022年3月疫情的趨勢，並量化了保護行為與打疫苗的影響。在Alpha與Delta時期(2021年11月前)，加入了戴口罩與施打疫苗的模型預測能力最好；在Omicron時期(2021年12月後)，無額外預測子的模型表現最好。模型也顯示病例增長率隨著保護行為和疫苗接種有效降低(係數:戴口罩，-0.807 ~ -0.003；避免外出，-0.542 ~ -0.207)，在Omicron時期，追加劑也十分重要(係數: -0.120 ~ -0.027)。此模型易於理解且可使用在即時監測的防疫計畫中，幫助制定能有效控制疫情的政策。

During the COVID-19 pandemic, mathematical models are used to predict trends in epidemic spread and determine the effectiveness of control measures. The Automatic Regressive Integrated Moving Average (ARIMA) model has been used for time series forecasting. Despite reports that protective behavior and vaccinations are effective in controlling pandemics, previous ARIMA models did not take them into account. To improve the accuracy of predictions, this study applied a newly developed ARIMA model with predictors (mask-wearing, avoiding going out, and vaccination) collected from the COVID-19 Behavior Tracker to predict the weekly growth rates of COVID-19 cases. Our model was used to capture the trend of COVID-19 from January 2021 to March 2022 in Canada, France, Italy, and Israel and quantify the impacts of protective behavior and vaccine. During the Alpha and Delta periods (prior to November 2021), the model with mask-wearing and vaccination improved the performance of prediction. During the Omicron periods (after December 2021), the ARIMA model without any additional predictors had the best performance. The models demonstrated that COVID-19 case growth rates effectively decreased with protective behaviors and vaccination (coefficients: mask wearing, –0.807 to –0.003; avoiding going out, –0.542 to –0.207), with booster vaccine coverage playing a particularly vital role during the Omicron period (coefficient: –0.120 to –0.027). The models we developed are simple to understand and can be embedded in a “real-time” schedule, with weekly data updates, and therefore can help make timely policy decisions to control dynamically changing epidemics.

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