Global Prevention of Future Disease Threats: The Role of Technological Solutions in Epidemiology
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Artificial Intelligence, Public Health, Technological Solutions
Abstract
The global landscape of infectious diseases continues to evolve, posing new challenges for public health systems worldwide. With the rise of emerging and re-emerging diseases, it becomes imperative to leverage technological solutions in epidemiology to prevent future health crises. This study investigates the role of advanced technologies such as artificial intelligence (AI), big data analytics, machine learning, and digital health tools in shaping effective disease prevention strategies. The objective is to evaluate the potential of these innovations in forecasting, monitoring, and managing public health threats. Using a systematic review methodology, data were collected from 100 peer-reviewed articles and reports on the application of technological tools in epidemiology. The findings reveal that technological advancements have significantly enhanced the accuracy of disease predictions, real-time monitoring, and rapid response capabilities. Furthermore, AI-driven surveillance systems and big data analytics have proven essential in identifying disease patterns and facilitating early interventions. The study concludes that the integration of technology into epidemiological practices is crucial for proactive global health management. Technological solutions, when effectively implemented, offer scalable and sustainable approaches to preventing future global disease threats.
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References
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