Scientific Sessions

Digital epidemiology refers to the use of digital data sources and computational tools to study and monitor the patterns, causes, and effects of health and disease conditions in populations. Unlike traditional epidemiology, which relies heavily on hospital records and field surveys, digital epidemiology leverages data from social media, mobile apps, internet searches, wearable devices, and other digital footprints to track disease spread and predict health outcomes in real time. These methods are particularly useful in situations where timely data is crucial, such as during disease outbreaks or pandemics. By analyzing digital traces, researchers can detect emerging public health threats earlier, monitor the effectiveness of interventions, and assess public behavior and sentiment.

The core tools and techniques in digital epidemiology include machine learning, natural language processing, data mining, and geospatial analysis. These methods enable the integration of large and diverse datasets, allowing for comprehensive insights into population health dynamics. Digital epidemiology has been instrumental in tracking infectious diseases like influenza and COVID-19, as well as in non-communicable diseases through behavioral pattern analysis. However, the approach also raises concerns about data privacy, ethical use, and the representativeness of digital data, as it may not capture information from all demographic groups equally. Despite these challenges, digital epidemiology is reshaping public health surveillance and decision-making by offering faster, broader, and more flexible approaches to disease monitoring and control.