Chengcheng Gao: Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, 400038, China. ORCID
Rui Zhang: Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, 400038, China.
Xicheng Chen: Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, 400038, China.
Tianhua Yao: Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, 400038, China.
Qiuyue Song: Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, 400038, China.
Wei Ye: Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, 400038, China.
PengPeng Li: Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, 400038, China.
Zhenyan Wang: Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, 400038, China.
Dong Yi: Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, 400038, China. yd_house@hotmail.com.
Yazhou Wu: Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, 400038, China. asiawu@tmmu.edu.cn. ORCID
With the recent prevalence of COVID-19, cryptic transmission is worthy of attention and research. Early perception of the occurrence and development risk of cryptic transmission is an important part of controlling the spread of COVID-19. Previous relevant studies have limited data sources, and no effective analysis has been carried out on the occurrence and development of cryptic transmission. Hence, we collect Internet multisource big data (including retrieval, migration, and media data) and propose comprehensive and relative application strategies to eliminate the impact of national and media data. We use statistical classification and regression to construct an early warning model for occurrence and development. Under the guidance of the improved coronavirus herd immunity optimizer (ICHIO), we construct a "sampling-feature-hyperparameter-weight" synchronous optimization strategy. In occurrence warning, we propose an undersampling synchronous evolutionary ensemble (USEE); in development warning, we propose a bootstrap-sampling synchronous evolutionary ensemble (BSEE). Regarding the internal training data (Heilongjiang Province), the ROC-AUC of USEE3 incorporating multisource data is 0.9553, the PR-AUC is 0.8327, and the R of BSEE2 fused by the "nonlinear + linear" method is 0.8698. Regarding the external validation data (Shaanxi Province), the ROC-AUC and PR-AUC values of USEE3 were 0.9680 and 0.9548, respectively, and the R of BSEE2 was 0.8255. Our method has good accuracy and generalization and can be flexibly used in the prediction of cryptic transmission in various regions. We propose strategy research that integrates multiple early warning tasks based on multisource Internet big data and combines multiple ensemble models. It is an extension of the research in the field of traditional infectious disease monitoring and has important practical significance and innovative theoretical value.
