Integrating Internet multisource big data to predict the occurrence and development of COVID-19 cryptic transmission.

ABSTRACT: 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 (ICH

SUBMITTER: Gao C 

PROVIDER: S-EPMC9614204 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature