Registration is now open. To register please click here. 
Availability for the workshop is limited; early registration is recommended.

 

Workshop 2. Wastewater Environmental Surveillance for Control of Infectious Diseases

Date and time: Tuesday, 10 November, 09:00-12:00

Wastewater surveillance has emerged as a powerful population-level monitoring tool that detects viral, bacterial, and antimicrobial-resistant pathogens shed into the environment, often before clinical cases rise. By capturing community-wide signals—including from asymptomatic individuals—this method supports early outbreak detection, trend tracking, and targeted public-health interventions. Wastewater monitoring complements traditional surveillance and enhances preparedness for pandemics, enteric diseases, and emerging threats. This workshop envisages an interactive session with presentations, group work and case studies related to the topic.

Learning Objectives:

  • Explain the scientific basis and public-health value of wastewater surveillance.
  • Describe sampling, testing, and data-analysis techniques used in environmental monitoring.
  • Interpret wastewater findings to support outbreak prediction and response.
  • Assess challenges and opportunities for integrating wastewater data into routine surveillance systems.

 

Workshop 3. Artificial Intelligence and Big Data in Infectious Diseases

Date and time: Tuesday, 10 November, 13:00-17:00

Artificial intelligence (AI) and big data analytics are transforming infectious-disease surveillance, prediction, diagnosis, and response by enabling rapid analysis of large, complex datasets. AI tools can detect outbreak signals earlier, improve clinical diagnostic accuracy, optimize resource allocation, and support decision-making in real time. As digital data sources expand—from electronic health records to genomics and mobility data—ethical, transparent, and equitable use of AI becomes essential to maximize public-health benefit. This workshop envisages an interactive session with presentations, group work and case studies related to the topic.

Learning Objectives:

  • Describe how AI and big data are applied in infectious-disease detection and response.
  • Identify data sources and analytical techniques used in AI-driven public-health systems.
  • Evaluate the strengths and limitations of AI tools in outbreak prediction and clinical care.
  • Discuss ethical, equity, and governance considerations in the use of AI for public health.