Authors : Esha Madamalla

Volume/Issue : Volume 10 - 2025, Issue 7 - July

Google Scholar : https://tinyurl.com/y6taw66p

Scribd : https://tinyurl.com/4kdmezwj

DOI : https://doi.org/10.38124/ijisrt/25jul887

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Abstract : This review evaluates how social media and internet-based platforms can enhance infectious disease surveillance by supplementing traditional epidemiological methods. Drawing from 15 studies published between 2015 and 2023, the paper examines platforms such as Twitter, Facebook, Google Trends, Reddit, Wikipedia, Baidu, and Sina Weibo in the context of disease outbreaks like COVID-19, Influenza, Zika, and Ebola across countries including the U.S., China, Brazil, and Saudi Arabia. Findings show that spikes in user activity, such as tweets, search queries, and online discussions, often precede official case reporting by several days to weeks, offering valuable lead time for public health response. Twitter excelled in real-time detection, Google Trends in population-level awareness, and Reddit and Facebook in sentiment and misinformation tracking. Multi-platform AI models demonstrated improved accuracy over single-platform approaches. However, challenges such as demographic bias, language limitations, and misinformation remain. The study concludes that digital platforms are most effective when integrated into hybrid systems that combine social, clinical, and environmental data for more timely and adaptive disease monitoring.

Keywords : Computational Biolgy and Bioinformatics; Computational Epidemiology; Digital Epidemiology; Social Media Surveillance; Infectious Disease Monitoring.

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