We utilize available epidemiological surveillance data (cases, hospitalizations, wastewater), travel data, land use patterns, climate data, sociodemographic data, behavioural data, policy and ecological models within statistical, machine learning and AI frameworks to forecast near-term infectious disease risk at various spatial and temporal scales. This line of work also includes the development of new methods and metrics to evaluate forecasting models. We synthesize our findings to support public health decision making, planning and response.
Forecasting Outbreaks
Summary
Related Publications
- H Du, J Zhao, Y Zhao, S Xu, X Lin, Y Chen, LM Gardner (2025). Advancing Real-time Pandemic Forecasting Using Large Language Models: A COVID-19 Case Study. In Press: Nature Computational Medicine. 2025 doi: 10.1038/s43588-025-00798-6.
- S Xu, H Du, E Dong, X Wang, L Zhang, LM Gardner. A Multi-pathogen Hospitalization Forecasting Model for the United States: An Optimized Geo-Hierarchical Ensemble Framework. Epidemics. In press.
- N Rankin, S Saiyed, H Du, L Gardner. (2025) A Multi-City COVID-19 Forecasting Model Utilizing Wastewater-Based Epidemiology. Science of The Total Environment Volume 960, 15 January 2025, 178172 https://doi.org/10.1016/j.scitotenv.2024.178172
- Du et al, A Deep Learning Approach to Forecast Short-Term COVID-19 Cases and Deaths in the US. eBioMedicine, 89, 104482, March 2023. Published February 21, 2023 DOI: https://doi.org/10.1016/j.ebiom.2023.104482
- Ahktar M, Kraemer, MU, Gardner L. A real-time outbreak prediction model for the spread of Zika in the Americas. BMC Medicine 2019 Sep 2; 17(171):1-16. Available from: https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-019-1389-3#citeas doi: 10.1186/s12916-019-1389-3
- Marshall, M., Parker, F. & Gardner, L.M. When are predictions useful? A new method for evaluating epidemic forecasts. BMC Global Public Health 2, 67 (2024). https://doi.org/10.1186/s44263-024-00098-7
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