Khan Atikur R, Chowdhury Abdul Hannan, Imon Rahmatullah
Department of Management, North South University, Dhaka, Bangladesh.
Department of Mathematical Sciences, Ball State University, Muncie, USA.
Intell Syst Appl. 2023 May;18:200234. doi: 10.1016/j.iswa.2023.200234. Epub 2023 May 13.
Growth of an epidemic is influenced by the natural variation in climatic conditions and enforcement variation in government stringency policies. Though these variations do not prompt an instant change in the growth of an epidemic, effects of climatic conditions and stringency policies become apparent over time. Time-lagged relationships and functional dynamic connectivity among meteorological covariates and stringency levels generate many lagged features deemed to be important for prediction of reproduction rate, a measure for growth of an epidemic. This empirical study examines the importance scores of lagged features and implements distributed lag inspired feature selection with back testing for model selection and forecasting. A verification forecasting scheme is developed for continuous monitoring of the growth of an epidemic. We have demonstrated the monitoring process by computing a week ahead expected target of the reproduction rate and then by computing a one day ahead verification forecast to evaluate the progress towards the expected target. This evaluation procedure will aid the analysts with a decision making tool for any early adjustment of control options to suppress the transmission.
流行病的传播受到气候条件自然变化以及政府严格政策执行差异的影响。尽管这些变化不会立即促使流行病传播发生改变，但气候条件和严格政策的影响会随着时间推移而显现。气象协变量与严格程度之间的时间滞后关系和功能动态连通性产生了许多滞后特征，这些特征被认为对预测繁殖率（一种衡量流行病传播的指标）很重要。本实证研究考察了滞后特征的重要性得分，并实施了受分布式滞后启发的特征选择，并通过回测进行模型选择和预测。开发了一种验证预测方案，用于持续监测流行病的传播。我们通过计算繁殖率提前一周的预期目标，然后计算提前一天的验证预测来评估朝着预期目标的进展，展示了监测过程。这种评估程序将为分析师提供一种决策工具，以便对控制措施进行任何早期调整以抑制传播。
