A Generalizable AI-Driven Decision Support System for Infectious Disease Modeling

Marzieh Soltani, Rozita Dara, Shayan Sharif
Proceedings of the The 39th Canadian Conference on Artificial Intelligence, PMLR 318:1210-1215, 2026.

Abstract

The increasing complexity of infectious disease dynamics highlights the need for integrated and proactive surveillance systems. Traditional approaches remain largely reactive, relying on confirmed case reports and lagging indicators. This work presents a generalizable AI-driven Decision Support System (DSS) for spatiotemporal disease modeling, developed and validated using avian influenza surveillance in Canada. The proposed DSS consists of three core components: a digital surveillance module that leverages online activity for early warning signals; a spatiotemporal risk prediction module that models geographic disease risk using multi-source environmental and ecological data; and an expert system dashboard that integrates analytical outputs into an interactive, user-centered interface. The proposed DSS aims to equip policymakers and emergency responders with the tools needed to mitigate the impact of AIV outbreaks, through more informed, timely, and targeted interventions.

Cite this Paper

BibTeX
@InProceedings{pmlr-v318-soltani26a, title = {A Generalizable AI-Driven Decision Support System for Infectious Disease Modeling}, author = {Soltani, Marzieh and Dara, Rozita and Sharif, Shayan}, booktitle = {Proceedings of the The 39th Canadian Conference on Artificial Intelligence}, pages = {1210--1215}, year = {2026}, editor = {Bouzar-Benlabiod, Lydia and Leung, Carson}, volume = {318}, series = {Proceedings of Machine Learning Research}, month = {25--29 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v318/main/assets/soltani26a/soltani26a.pdf}, url = {https://proceedings.mlr.press/v318/soltani26a.html}, abstract = {The increasing complexity of infectious disease dynamics highlights the need for integrated and proactive surveillance systems. Traditional approaches remain largely reactive, relying on confirmed case reports and lagging indicators. This work presents a generalizable AI-driven Decision Support System (DSS) for spatiotemporal disease modeling, developed and validated using avian influenza surveillance in Canada. The proposed DSS consists of three core components: a digital surveillance module that leverages online activity for early warning signals; a spatiotemporal risk prediction module that models geographic disease risk using multi-source environmental and ecological data; and an expert system dashboard that integrates analytical outputs into an interactive, user-centered interface. The proposed DSS aims to equip policymakers and emergency responders with the tools needed to mitigate the impact of AIV outbreaks, through more informed, timely, and targeted interventions.} }
Endnote
%0 Conference Paper %T A Generalizable AI-Driven Decision Support System for Infectious Disease Modeling %A Marzieh Soltani %A Rozita Dara %A Shayan Sharif %B Proceedings of the The 39th Canadian Conference on Artificial Intelligence %C Proceedings of Machine Learning Research %D 2026 %E Lydia Bouzar-Benlabiod %E Carson Leung %F pmlr-v318-soltani26a %I PMLR %P 1210--1215 %U https://proceedings.mlr.press/v318/soltani26a.html %V 318 %X The increasing complexity of infectious disease dynamics highlights the need for integrated and proactive surveillance systems. Traditional approaches remain largely reactive, relying on confirmed case reports and lagging indicators. This work presents a generalizable AI-driven Decision Support System (DSS) for spatiotemporal disease modeling, developed and validated using avian influenza surveillance in Canada. The proposed DSS consists of three core components: a digital surveillance module that leverages online activity for early warning signals; a spatiotemporal risk prediction module that models geographic disease risk using multi-source environmental and ecological data; and an expert system dashboard that integrates analytical outputs into an interactive, user-centered interface. The proposed DSS aims to equip policymakers and emergency responders with the tools needed to mitigate the impact of AIV outbreaks, through more informed, timely, and targeted interventions.
APA
Soltani, M., Dara, R. & Sharif, S.. (2026). A Generalizable AI-Driven Decision Support System for Infectious Disease Modeling. Proceedings of the The 39th Canadian Conference on Artificial Intelligence, in Proceedings of Machine Learning Research 318:1210-1215 Available from https://proceedings.mlr.press/v318/soltani26a.html.

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