AI4Mpox Resources

Below, we have compiled some open-access resources created by AI4PEP researchers to support AI-enhanced modelling for hotspot detection, prediction & early warnings, sentiment analysis, and understanding of Mpox risk factors. These resources are available for use by modellers interested in developing AI-based tools to address Mpox-related epidemiological challenges.

Hotspot Detection:

Lieberman, B., Kong, J. D., Gusinow, R., Asgary, A., Bragazzi, N. L., Choma, J., … & Mellado, B. (2023). Big data-and artificial intelligence-based hot-spot analysis of COVID-19: Gauteng, South Africa, as a case study. BMC Medical Informatics and Decision Making, 23(1), 19.
Zhou, X., Nia, Z., Bragazzi, N. L., Gizo, I., Gillies, M., Gardner, E., … & Kong, J. D. (2025). Predicting Avian Influenza Hotspots in the US Using Logistic Regression and Random Forest with Augmented Data via Empirical Distribution Method and Stochastic Variational Inference. Available at SSRN 5211568.
Nia, Z. M., Prescod, C., Westin, M., Perkins, P., Goitom, M., Fevrier, K., … & Kong, J. (2024). Cross-sectional study to assess the impact of the COVID-19 pandemic on healthcare services and clinical admissions using statistical analysis and discovering hotspots in three regions of the Greater Toronto Area. BMJ open, 14(3), e082114.

Prediction Modelling:

Nia, Z., Bragazzi, N. L., Gizo, I., Gillies, M., Gardner, E., Leung, D., & Kong, J. D. (2025). Integrating Deep Learning Methods and Web-Based Data Sources for Surveillance, Forecasting, and Early Warning of Avian Influenza. Forecasting, and Early Warning of Avian Influenza (May 13, 2025).
Nia, Z. M., Seyyed-Kalantari, L., Goitom, M., Mellado, B., Ahmadi, A., Asgary, A., … & Kong, J. D. (2025). Leveraging deep-learning and unconventional data for real-time surveillance, forecasting, and early warning of respiratory pathogens outbreak. Artificial Intelligence in Medicine, 161, 103076.
Early Warning System for Avian Influenza Outbreaks. Artificial Intelligence for Mathematical Modelling (AIMM Lab), University of Toronto. https://aimmlab.org/early-warning-system-for-avian-influenza-outbreaks/

Sentiment Analysis:

Movahedi Nia, Z., Bragazzi, N., Asgary, A., Orbinski, J., Wu, J., & Kong, J. (2023). Mpox panic, infodemic, and stigmatization of the two-spirit, lesbian, gay, bisexual, transgender, queer or questioning, intersex, asexual community: geospatial analysis, topic modeling, and sentiment analysis of a large, multilingual social media database. Journal of Medical Internet Research, 25, e45108.
Pang, O., Nia, Z., Gillies, M., Leung, D., Bragazzi, N. L., Gizo, I., & Kong, J. D. (2024). Analyzing Reddit Social Media Content in the United States Related to H5N1: A Sentiment and Topic Modeling Study. Available at SSRN 5078277.
Movahedi Nia, Z., Bragazzi, N. L., Ahamadi, A., Asgary, A., Mellado, B., Orbinski, J., … & Kong, J. D. (2023). Off-label drug use during the COVID-19 pandemic in Africa: topic modelling and sentiment analysis of ivermectin in South Africa and Nigeria as a case study. Journal of the Royal Society Interface, 20(206), 20230200.
Ogbuokiri, B., Ahmadi, A., Nia, Z. M., Mellado, B., Wu, J., Orbinski, J., … & Kong, J. (2023). Vaccine hesitancy hotspots in Africa: an insight from geotagged twitter posts. IEEE Transactions on Computational Social Systems, 11(1), 1325-1338.
Ogbuokiri, B., Ahmadi, A., Bragazzi, N. L., Movahedi Nia, Z., Mellado, B., Wu, J., … & Kong, J. (2022). Public sentiments toward COVID-19 vaccines in South African cities: An analysis of Twitter posts. Frontiers in public health, 10, 987376.
Yan, C., Law, M., Nguyen, S., Cheung, J., & Kong, J. (2021). Comparing public sentiment toward COVID-19 vaccines across Canadian cities: analysis of comments on Reddit. Journal of medical Internet research, 23(9), e32685.

Mpox Risk Factors:

Ugwu, C. L. J., Bragazzi, N. L., Wu, J., Kong, J. D., Asgary, A., Orbinski, J., & Woldegerima, W. A. (2025). Risk factors associated with human Mpox infection: a systematic review and meta-analysis. BMJ Global Health, 10(2).