Selected Publications
13. Wang, H., Chakraborty, A. K., & Saha, E. (2026). Hybrid mechanistic-machine learning models in biosciences: causality, forecasting, and lab-to-field translation. Mathematical Biosciences, 401, 109801. https://doi.org/10.1016/j.mbs.2026.109801
12. Saha, E., Wang, O., Chakraborty, A. K., Garcia, P. V., Milne, R., & Wang, H. (2025). Dispersion based recurrent neural network model for methane monitoring in Albertan tailings ponds. Journal of Environmental Management, 395, 127748.
11. Chakraborty, A. K., Miry, R., Greiner, R., Lewis, M. A., Wang, H., Guan, T., & Ramazi, P. (2025). Deep learning for disease outbreak prediction: a parallel LSTM-CNN model. Journal of the Royal Society Interface, 22(229), 20250046.
10. Gao, S., Chakraborty, A. K., Greiner, R., Lewis, M. A., & Wang, H. (2025). Early detection of disease outbreaks and non-outbreaks using incidence data: A framework using feature-based time series classification and machine learning. PLOS Computational Biology, 21(2), e1012782.
9. Chakraborty, A. K., Wang, H., & Ramazi, P. (2024). From policy to prediction: Assessing forecasting accuracy in an integrated framework with machine learning and disease models. Journal of Computational Biology, 31(11), 1104-1117.
8. Chakraborty, A. K., Gao, S., Miry, R., Ramazi, P., Greiner, R., Lewis, M. A., & Wang, H. (2024). An early warning indicator trained on stochastic disease-spreading models with different noises. Journal of the Royal Society Interface, 21(217), 20240199.
7. Zhang, S., Meng, Y., Chakraborty, A. K., & Wang, H. (2024). Controlling smoking: A smoking epidemic model with different smoking degrees in deterministic and stochastic environments. Mathematical Biosciences, 368, 109132. https://doi.org/10.1016/j.mbs.2023.109132.
6. Haque, M. A., Chakraborty, A. K., & Islam, M. A. (2021). Mathematical Modeling and Analysis of the Dynamics of Chikungunya in Bangladesh. MATEMATIKA: Malaysian Journal of Industrial and Applied Mathematics, 37(2), 91–108.
5. Chakraborty, A. K., Haque, M. A., & Islam, M. A. (2020). Mathematical Modelling and Analysis of Dengue Transmission in Bangladesh with Saturated Incidence Rate and Constant Treatment Function. Communication in Biomathematical Sciences. 3(2): 101-113.
4. Haque, M. A., Chakraborty, A. K., Islam, M. A., Rahman, M. A., & Barman, V. C. M. (2020). A Study of HIV Infection of CD4+ T Cells through Saturated Incidence Rate and Constant Cytokine Effect: A mathematical modelling approach. Global Journal of Pure and Applied Mathematics, 16(5), 723-739.
3. Paul, S.C., Haque, M.A., Islam, M.A., and Chakraborty, A.K. (2018). Mathematical Modeling and Analyzing of Transmission Dynamics of Influenza with Carrier. International Journal of Applied Mathematics and Statistics, 57(4),26-40.
2. Shahrear, P., Chakraborty, A.K., Islam, M.A., and Habiba, U. (2017). Analysis of Computer Virus Propagation by Compartmental Model. Applied and Computational Mathematics, 7(1-2), 12-21.
1. Chakraborty, A.K., Shahrear, P., and Islam, M.A., (2017). Analysis of Epidemic Model by Differential Transform Method. Journal of Multidisciplinary Engineering Science and Technology, 4(2), 6574-6581.
Submitted/Under Review
Chakraborty, A. K., Wang, H., & Ramazi, P. (2026). Turning mechanistic models into forecasters by using machine learning. arXiv preprint arXiv:2602.04114.
Projects
4. HQP (2024-Ongoing) for NSERC Alliance Missions: Anthropogenic Greenhouse Gas Research, Interdisciplinary Lab for Mathematical Ecology & Epidemiology (ILMEE), University of Alberta.
3. HQP (2023-2024): Machine Learning and Statistical Approaches for Early Warning Signals, One Health Modeling Network for Emerging Infections (OMNI).
2. CI (2020-2021): Mathematical modeling and analysis of HIV infection dynamics of CD4+ T-cells, Shahjalal University of Science & Technology Research Center.
1. PI (2019-2020): Mathematical Model and Analysis of Prion Proliferation, Shahjalal University of Science & Technology Research Center.
Talks
Learning and Forecasting Dynamical Systems with Time-Varying Parameters, Alberta Mathematics Dialogue, MacEwan University, Edmonton, Alberta, May 2026.
Predicting infectious diseases with Bayesian networks, Graduate Colloquium, University of Alberta, December 2024.
From policy to prediction of infectious diseases: all machine learning models do well; Bayesian networks do better, Mathematical Biology seminar, University of Alberta, March 2024.
How to detect impending disease outbreaks? –Anticipation using statistics and machine learning methods, OMNI-RÉUNIS Annual Meeting 2023-2024, York University, February 2024.
Modelling and analysis of dengue and chikungunya transmission in Bangladesh, SUST Research Center, Shahjalal University of Science & Technology, Sylhet, Bangladesh, January 2020.