Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, P.R. China.
Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing, P.R. China.
Epidemiol Infect. 2020 Oct 5;148:e236. doi: 10.1017/S095026882000237X.
Forecasting the epidemics of the diseases is very valuable in planning and supplying resources effectively. This study aims to estimate the epidemiological trends of the coronavirus disease 2019 (COVID-19) prevalence and mortality using the advanced α-Sutte Indicator, and its prediction accuracy level was compared with the most frequently adopted autoregressive integrated moving average (ARIMA) method. Time-series analysis was performed based on the total confirmed cases and deaths of COVID-19 in the world, Brazil, Peru, Canada and Chile between 27 February 2020 and 30 June 2020. By comparing the prediction reliability indices, including the root mean square error, mean absolute error, mean error rate, mean absolute percentage error and root mean square percentage error, the α-Sutte Indicator was found to produce lower forecasting error rates than the ARIMA model in all data apart from the prevalence testing set globally. The α-Sutte Indicator can be recommended as a useful tool to nowcast and forecast the COVID-19 prevalence and mortality of these regions except for the prevalence around the globe in the near future, which will help policymakers to plan and prepare health resources effectively. Also, the findings of our study may have managerial implications for the outbreak in other countries.
预测疾病的流行情况对于有效规划和供应资源非常有价值。本研究旨在使用先进的 α-Sutte 指标来估计 2019 年冠状病毒病（COVID-19）流行率和死亡率的流行病学趋势，并将其预测准确性水平与最常采用的自回归综合移动平均（ARIMA）方法进行比较。基于 2020 年 2 月 27 日至 6 月 30 日期间全球、巴西、秘鲁、加拿大和智利的 COVID-19 确诊病例和死亡总数，进行了时间序列分析。通过比较预测可靠性指标，包括均方根误差、平均绝对误差、平均误差率、平均绝对百分比误差和均方根百分比误差，发现 α-Sutte 指标在所有数据中，除了全球流行率测试集之外，均比 ARIMA 模型产生更低的预测误差率。α-Sutte 指标可以作为一种有用的工具，用于预测这些地区的 COVID-19 流行率和死亡率，除了近期全球流行率之外，这将有助于政策制定者有效规划和准备卫生资源。此外，我们的研究结果可能对其他国家的疫情爆发具有管理意义。
