Digital Epidemiology

The COVID-19 pandemic has underscored the crucial need to more fully integrate computational methods into public health practices. Techniques such as digital contact tracing have proven to be significantly impactful during the pandemic. Yet, their adoption remains limited, often due to a gap between cutting-edge science and technology and the actual practices in local public health. This course aims to bridge this divide. We will immerse participants in the challenges of epidemiology and public health, showing them recent and current examples of digital epidemiology. Participants will acquire knowledge relevant to digital epidemiology, enhancing their public health toolkit.

The course will culminate in group presentations, where participants will share their insights and recommendations based on their analysis of scientific articles related to digital epidemiology.

By the end of the course participants will:

• Understand the concepts and principles of digital epidemiology and its relevance in public health;
•  Recognize the potential benefits of incorporating AI tools in public health practice, particularly in the context of digital epidemiology;
• Understand the use of public health tools enhanced by AI to improve surveillance, monitoring, and response to disease outbreaks;
• Develop critical analysis skills to evaluate scientific articles on digital epidemiology and extract key aspects;
• Summarize and effectively communicate the key findings and implications of scientific articles on digital epidemiology;
• Generate ideas and propose next steps for advancing the field of digital epidemiology based on the insights obtained from analyzed articles;
• Apply theoretical knowledge and real-life examples from digital epidemiology and public health to propose next steps for advancing the analyzed scientific articles;
• Enhance overall understanding of how digital technologies and AI can contribute to effective public health practices and policies.

Basic background in epidemiology or public health is advantageous but not required.

The following methods will be used:

• Plenary sessions with both theoretical background as well as real examples from public health and digital epidemiology;
• Demonstrating of the use of public health tools using AI or that could potentially be improved by using AI;
•  Group exercise to analyze and dissect cutting-edge scientific articles on digital epidemiology to extract and summarize the key aspects;
• Group exercise to propose strategies for expansion and suggest next steps based on the findings derived from the analyzed scientific articles.

To get the Certificate of completion and the 1 ECTS participants MUST:

• Attend at least 80% of the course;
• Attend the plenary(s) offered during the days of the course;
• Participate in the development and presentation of the group exercises.

The facilitators plan to be in Lugano, and the course will be held entirely on-site. In case of a change of regional policies (e.g., Covid) or personal reasons, the course could change to online or hybrid format. The course will take place at Università della Svizzera italiana (USI).