Digital Health Interventions in Dengue Surveillance to Detect and Predict Outbreak: A Scoping Review
Marko Ferdian Salim, Tri Baskoro Tunggul Satoto, Danardono Danardono, D. Daniel
Abstract
Background Dengue fever is a global concern, with half of the population at risk. Digital Health Interventions (DHIs) have been widely used in Dengue surveillance. Objective The objective of this review is to identify DHIs that have been used in Dengue surveillance. Methods A systematic literature search was performed on three primary databases: PubMed, Scopus, and Google Scholar. A total of 2637 studies, including duplicates, were found to be possibly pertinent to the study topic during the electronic search for the systematic literature review. After the screening of titles and abstracts, 51 studies remained eligible. Results The study analyzed 13 main categories of DHIs in Dengue surveillance, with Brazil, India, Sri Lanka, China, and Indonesia being the top five countries. Geographic Information System was the most used DHIs, followed by Machine Learning, Social Media, Mobile Applications, Google Trends, and Web Applications. DHIs were integrated, as evidenced by the deployment of many DHIs simultaneously in a single Dengue surveillance program. Conclusion Future research should concentrate on finding more efficient ways to combine all available data sources and approaches to improve data completeness and predictive model precision and identify Dengue outbreaks early.