Weeks-Ahead Epidemiological Predictions of Varicella Cases From Univariate Time Series Data Applying Artificial Intelligence  

作　　者：David A.Wood 

机构地区：[1]DWA Energy Limited,Lincoln LN59JP,United Kingdom

出　　处：《Infectious Diseases & Immunity》2024年第1期25-34,共10页感染性疾病与免疫（英文）

摘　　要：Background:"Chickenpox"is a highly infectious disease caused by the varicella-zoster virus,influenced by seasonal and spatial factors.Dealing with varicella-zoster epidemics can be a substantial drain on health-authority resources.Methods that improve the ability to locally predict case numbers from time-series data sets every week are therefore worth developing.Methods:Simple-to-extract trend attributes from published univariate weekly case-number univariate data sets were used to generate multivariate data for Hungary covering 10 years.That attribute-enhanced data set was assessed by machine learning(ML)and deep learning(DL)models to generate weekly case forecasts from next week(t0)to 12 weeks forward(t+12).The ML and DL predictions were compared with those generated by multilinear regression and univariate prediction methods.Results:Support vector regression generates the best predictions for weeks t0 and t+1,whereas extreme gradient boosting generates the best predictions for weeks t+3 to t+12.Long-short-term memory only provides comparable prediction accuracy to the ML models for week t+12.Multi-K-fold cross validation reveals that overall the lowest prediction uncertainty is associated with the tree-ensemble ML models.Conclusion:The novel trend-attribute method offers the potential to reduce prediction errors and improve transparency for chickenpox timeseries.

关 键 词：Varicella zoster virus infection Disease-case weekly predictions Weeks-ahead forecasting Univariate time-series enhancements Tree-ensemble machine learning Time-series attribute extraction 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] R511.5[自动化与计算机技术—控制科学与工程]