Innovating Health Prevention Models in Detecting Infectious Disease Outbreaks Through Social Media Data: an Umbrella Review of the Evidence

Overview

Journal Front Public Health

Specialty Public Health

Date 2024 Dec 9

PMID 39651472

Authors

Monica Giancotti

Milena Lopreite

Marianna Mauro

Michelangelo Puliga

Affiliations

Soon will be listed here.

Abstract

Introduction And Objective: The number of literature reviews examining the use of social media in detecting emerging infectious diseases has recently experienced an unprecedented growth. Yet, a higher-level integration of the evidence is still lacking. This study aimed to synthesize existing systematic literature reviews published on this topic, offering an overview that can help policymakers and public health authorities to select appropriate policies and guidelines.

Methods: We conducted an umbrella review: a review of systematic reviews published between 2011 and 2023 following the PRISMA statement guidelines. The review protocol was registered in the PROSPERO database (CRD42021254568). As part of the search strategy, three database searches were conducted, specifically in PubMed, Web of Science, and Google Scholar. The quality of the included reviews was determined using A Measurement Tool to Assess Systematic Reviews 2.

Results: Synthesis included 32 systematic reviews and 3,704 primary studies that investigated how the social media listening could improve the healthcare system's efficiency in terms of a timely response to treat epidemic situations. Most of the included systematic reviews concluded showing positive outcomes when using social media data for infectious disease surveillance.

Conclusion: Systematic reviews showed the important role of social media in predicting and detecting disease outbreaks, potentially reducing morbidity and mortality through swift public health action. The policy interventions strongly benefit from the continued use of online data in public health surveillance systems because they can help in recognizing important patterns for disease surveillance and significantly improve the disease prediction abilities of the traditional surveillance systems.

Systematic Review Registration: http://www.crd.york.ac.uk/PROSPERO, identifier [CRD42021254568].

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