Data Infrastructure & Disease Intelligence

Rigorous, real-time modelling depends on rigorous, real-time data. AIMMLab has invested substantially in building a multi-source, multi-scale data infrastructure that spans conventional surveillance systems and unconventional digital signals giving our models a richer, more accurate picture of disease dynamics than any single source alone could provide.

Research Vision

To develop and deploy AI, data science, and mathematical methodologies for disease detection, data management and processing, and real-time model validation and calibration producing decision-ready insights at local, national, and global scales.

NSERC-Funded Program

Our NSERC Discovery program centres on modelling ecological and disease dynamics in changing environments. We integrate conventional surveillance data with unconventional sources social media, news feeds, African infectious disease portals, COVID-19 dashboards, and mpox stigmatisation studies to capture the full social-biological complexity of outbreaks.

Tools & Partnerships

AIMMLab has developed AI-powered interactive data visualisation frameworks for tracking disease outbreaks and health trends in real time. Through collaborations with 10 African governments, we access District Health Information System 2 (DHIS2) portals. In Canada, partnerships with government agencies, hospitals, and research institutions provide access to Acute Care Enhanced Surveillance (ACES), wastewater-based epidemiology, and national health registries.

Data Scope & Ethics

Our datasets span animal and human health, environmental, demographic, and financial domains. All data are curated following FAIR principles (Findable, Accessible, Interoperable, Reusable). For Indigenous communities and data, we adhere to OCAP® standards (Ownership, Control, Access, Possession), ensuring sovereignty and ethical stewardship at every stage.

AIMMLab Data Infrastructure Hub and spoke diagram showing multiple data source types feeding into AIMMLab's central modelling and AI platform. CONVENTIONAL AIMMLAB PLATFORM UNCONVENTIONAL AIMMLab AI & Modelling Multi-source integration Real-time validation & calibration DHIS2 Portals 10 African governments ACES & Wastewater Canadian surveillance Health Registries Animal & human health data Social Media & News Sentiment & infodemic signals Mobility & Environmental Movement & climate data Disease Portals Africa & global dashboards All data governed by FAIR principles · Indigenous data managed under OCAP® standards

Figure: AIMMLab's data infrastructure — conventional surveillance and unconventional digital signals integrated into a unified AI modelling platform.

Ongoing Priorities

  • Expanding access to DHIS2 data across additional African partner countries beyond the current 10.
  • Strengthening multi-source data integration pipelines to reduce latency between data collection and model calibration.
  • Developing improved methods for extracting reliable signals from noisy, unconventional digital sources.
  • Building open, FAIR-compliant data repositories that support reproducibility and global research collaboration.

📚 Selected Refereed Journal Publications

Sharma, Y., Laison, E. K., Philippsen, T., Ma, J., Kong, J., Ghaemi, S., ... & Nasri, B. (2024). Models and data used to predict the abundance and distribution of Ixodes scapularis (blacklegged tick) in North America: a scoping review. The Lancet Regional Health–Americas, 32.

Yuh, M. N., Ndum Okwen, G. A., Miong, R. H. P., Bragazzi, N. L., Kong JD., Movahedi Nia, Z., ... Patrick Mbah, O. (2024). Using an innovative family-centered evidence toolkit to improve the livelihood of people with disabilities in Bamenda (Cameroon): a mixed-method study. Frontiers in Public Health, 11, 1190722.

Nunes, M. C., Thommes, E., Fröhlich, H., Flahault, A., Arino, J., Baguelin, M., Kong JD ... Coudeville, L. (2024). Redefining pandemic preparedness: Multidisciplinary insights from the CERP modelling workshop in infectious diseases, workshop report. Infectious Disease Modelling.

Kaur, M., Cargill, T., Hui, K., Vu, M., Bragazzi, N. L., Kong JD (2024). A Novel Approach for the Early Detection of Medical Resource Demand Surges During Health Care Emergencies: Infodemiology Study of Tweets. JMIR Formative Research, 8, e46087.

Bain, L. E., Yankam, B. M., Kong, J. D., Nkfusai, N. C., Badru, O. A., Ebuenyi, I. D., ... & Adeagbo, O. (2023). Global Health Mentorship: Challenges and Opportunities for Equitable Partnership. BMJ Global Health, 8(11), e013751.

Kaur, M., Bragazzi, N. L., Heffernan, J., Tsasis, P., Kong JD (2023). COVID-19 in Ontario Long-term Care Facilities Project, a manually curated and validated database. Frontiers in Public Health, 11, 1133419.

Nia ZM, Ahmadi A, Mellado B, Wu J, Orbinski J, Asgary A, Kong JD. Twitter-based gender recognition using transformers. Math Biosci Eng. 2023 Aug 3;20(9):15962-15981. doi: 10.3934/mbe.2023711.

Movahedi Nia, Z., Bragazzi, N., Asgary, A., Orbinski, J., Wu, J., Kong JD (2023). Mpox Panic, Infodemic, and Stigmatization of the 2SLGBTQIA+ Community: Geospatial Analysis, Topic Modeling, and Sentiment Analysis. Journal of Medical Internet Research, 25, e45108.

Fevrier, K., Effoduh, J. O., Kong JD, Bragazzi, N. L. (2023). Artificial Intelligence, Law, and Vulnerabilities. In AI and Society. : 179-196.

Ji J, Wang H, Wang L, Ramazi P, Kong JD, Watmough J. Climate-dependent effectiveness of nonpharmaceutical interventions on COVID-19 mitigation. Mathematical Biosciences. 2023 Dec 1;366:109087.