Digital Epidemiology: Harnessing Big Data and Artificial Intelligence for Enhanced Disease Surveillance

Abstract

Background: The emergence of SARS-CoV-2 in late 2019 highlighted the critical need for advanced disease surveillance systems. Digital epidemiology, leveraging Big Data and artificial intelligence (AI), offers promising solutions for effective monitoring and management of infectious diseases like COVID-19.

Methods: This review explores the integration of Big Data analytics and AI in enhancing disease surveillance. It examines various data types, including real-time epidemiological data, social media trends, and population mobility patterns. Case studies, such as the AI-driven initiatives by Blue Dot and Johns Hopkins University, illustrate the application of these technologies in early outbreak detection and trend forecasting.

Results: The findings indicate that AI and Big Data significantly improve the efficiency of pandemic response. For instance, AI algorithms successfully predicted COVID-19 case surges based on social media search trends and population mobility data. Additionally, novel diagnostic tools, including salivary tests and AI-enhanced imaging platforms, have emerged to facilitate rapid and accurate COVID-19 diagnosis.

Conclusion: The integration of Big Data and AI into public health strategies enhances the ability to monitor disease spread, predict outbreaks, and inform decision-making. These technologies not only improve responsiveness to current pandemics but also pave the way for more resilient healthcare systems in the future. Continued investment in digital epidemiology is essential for efficient disease surveillance and management, ensuring preparedness for future health crises.