Scientific Sessions

Artificial Intelligence (AI) and Big Data analytics have emerged as powerful tools in the fight against infectious diseases, offering unprecedented capabilities in early detection, prediction, and management. AI algorithms can process massive datasets from diverse sources, including electronic health records, genomic data, environmental monitoring, and social media trends, to identify patterns and detect outbreaks in real time. Machine learning models can analyze complex epidemiological trends to forecast the spread of infections, enabling public health authorities to implement timely containment strategies. For example, AI-driven diagnostic tools can rapidly analyze medical images, laboratory results, and clinical notes to provide accurate and early diagnoses for conditions such as tuberculosis, malaria, and COVID-19. Coupled with natural language processing (NLP), AI can scan global health reports and news sources to identify emerging threats, providing critical lead time for intervention.

Big Data complements AI by serving as the foundation for data-driven decision-making in infectious disease control. It integrates vast amounts of structured and unstructured information from hospitals, research institutions, wearable devices, and public health databases. This enables comprehensive disease surveillance, mapping hotspots, and understanding transmission dynamics. During pandemics, real-time analytics can assist in resource allocation, such as distributing vaccines, medicines, and hospital beds to areas of greatest need. Predictive analytics powered by Big Data also supports drug discovery and vaccine development by identifying potential therapeutic targets more efficiently than traditional methods. Furthermore, AI and Big Data together enable personalized healthcare by assessing individual risk factors and tailoring prevention or treatment plans accordingly. While challenges such as data privacy, interoperability, and ethical considerations remain, the integration of these technologies offers a transformative approach to infectious disease management, potentially reducing mortality rates and minimizing the socioeconomic impact of future outbreaks.