Predicting infectious disease for biopreparedness and response: A systematic review of machine learning and deep learning approaches.

ABSTRACT: The complex, unpredictable nature of pathogen occurrence has required substantial efforts to accurately predict infectious diseases (IDs). With rising popularity of Machine Learning (ML) and Deep Learning (DL) techniques combined with their unique ability to uncover connections between large amounts of diverse data, we conducted a PRISMA systematic review to investigate advances in ID prediction for human and animal diseases using ML and DL. This review included the type of IDs modeled, ML and DL techniques utilized, geographical distribution, prediction tasks performed, input features utilized, spatial and temporal scales, error metrics used, computational efficiency, uncertainty quantification, and missing data handling methods. Among 237 relevant articles published between January 2001

SUBMITTER: Keshavamurthy R 

PROVIDER: S-EPMC9582566 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature