Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review.

ABSTRACT:

Background

Traditionally, dengue surveillance is based on case reporting to a central health agency. However, the delay between a case and its notification can limit the system responsiveness. Machine learning methods have been developed to reduce the reporting delays and to predict outbreaks, based on non-traditional and non-clinical data sources. The aim of this systematic review was to identify studies that used real-world data, Big Data and/or machine learning methods to monitor and predict dengue-related outcomes.

Methodology/principal findings

We performed a search in PubMed, Scopus, Web of Science and grey literature between January 1, 2000 and August 31, 2020. The review (ID: CRD42020172472) focused on data-driven studies. Reviews, randomized control trials and descr

SUBMITTER: Sylvestre E 

PROVIDER: S-EPMC8740963 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature