Surveillance Systems and Predictive Analytics for Epidemics

Dear Colleagues,

In an interconnected world where diseases can spread rapidly across borders, the ability to detect epidemics promptly is more critical than ever. The COVID-19 pandemic and other recent outbreaks have underscored that early detection can dramatically reduce the impact of an epidemic by enabling swift public health interventions. Modern digital surveillance systems harness advances in technology—ranging from real-time data collection to AI-driven analytics—to monitor emerging health threats worldwide and overcome delays and gaps inherent in traditional reporting. Predictive analytics, including machine learning models and big data approaches, offer a powerful means to anticipate outbreak trends and enhance preparedness, effectively turning surveillance into a proactive tool for response. Together, improved surveillance and predictive insights can significantly strengthen epidemic preparedness and guide more effective responses. Achieving these goals, however, requires broad collaboration across disciplines, including public health experts, epidemiologists, data scientists, and systems engineers, to integrate diverse expertise into robust systems for epidemic surveillance and predictive analytics. This multidisciplinary Topic encourages contributions from experts across these domains and beyond, aiming to foster innovative approaches for early epidemic detection and to improve our collective ability to respond to health crises and future outbreaks, ultimately strengthening global health security.

Dr. Georgia Kourlaba
Dr. Elisavet Stavropoulou
Topic Editors