Bringing together global experts in public health, artificial intelligence, toxicology, exposomics, respiratory epidemic modelling, data governance, and Trusted Research Environments to promote responsible, data-driven public health research.
Bridging Classical Toxicology and Exposomics
Respiratory Epidemic AI
Public Health Data Governance / Trusted Research Environment
Artificial intelligence is reshaping the way public health systems monitor risks, interpret complex data, forecast emerging threats, and support evidence-based decision-making. At the same time, responsible public health innovation requires robust scientific interpretation, trustworthy data governance, and sustainable international collaboration.
This conference provides a cross-disciplinary platform for connecting classical toxicology with exposomics, applying AI to respiratory epidemic surveillance and forecasting, and strengthening Trusted Research Environments for secure and ethical public health data science.
This seminar examines how classical toxicological and epidemiological paradigms can be connected with exposomic approaches to understand environmental causality and population health.
The session contrasts isolation-based causal inference with exposomic perspectives that treat health as the product of cumulative, time-varying, and correlated exposures embedded in social and environmental systems.
This track explores the application of artificial intelligence, machine learning, mathematical modelling, and data-driven systems for respiratory infectious disease surveillance, early warning, forecasting, intervention evaluation, and public health decision support.
This track addresses responsible data use, data governance, privacy protection, secure data infrastructure, Trusted Research Environments, federated analysis, ethical AI, regulatory frameworks, and cross-institutional data collaboration for public health research.
Institution: Faculty of Innovation Engineering, Macau University of Science and Technology
Role: Opening Remarks / Panelist
Institution: HKU-Pasteur Research Pole, HKU
Role: Opening Remarks
Institution: Beijing
Role: Keynote Speaker / Panelist
Institution: Beijing
Role: Keynote Speaker / Panelist / Concluding Remarks
Institution: IRD
Role: Keynote Speaker / Panelist / Concluding Remarks
Institution: IRD
Role: Keynote Speaker / Panelist
Institution: Institut Pasteur Paris
Role: Keynote Speaker / Panelist
Role: Roundtable Moderator
Role: Panelist
Role: Panelist
Role: Panelist
Role: Panelist
Session topic: Respiratory Epidemic AI
Track: Track 2
Session topic: Public Health Data Governance / TRE
Track: Track 3
Explore the three-day programme by theme. Select a conference day to view the detailed schedule.
Theme: Bridging Classical Toxicology and Exposomics
Day 1 focuses on classical toxicology, exposomics, environmental causality, and policy implications for public health decision-making.
Theme: Respiratory Epidemic AI
Day 2 is reserved for keynote sessions and discussions on AI-enabled respiratory epidemic surveillance, modelling, and public health decision support.
Theme: Public Health Data Governance
Day 3 focuses on public health data governance, trusted research environments, ethical AI, and the closing sessions of the conference.
Theme: Bridging Classical Toxicology and Exposomics
Theme: Respiratory Epidemic AI
Theme: Public Health Data Governance
This schedule provides a detailed three-day overview of the conference. Day 1 has been updated with confirmed programme information, while Day 2 and Day 3 remain editable placeholders.
Venue: Macau University of Science and Technology, Macau SAR, China
Conference hall: To be confirmed
Address: To be confirmed
Transportation: To be confirmed
Nearby hotels: To be confirmed
The conference is jointly organized by leading academic and research institutions committed to advancing public health, artificial intelligence, environmental health, infectious disease research, and responsible data-driven innovation.
For enquiries regarding the conference programme, invited speakers, venue information, and institutional collaboration, please contact the conference secretariat.
Email: To be confirmed
Phone: To be confirmed
Address: Macau University of Science and Technology, Macau SAR, China