MM
Madhav Marathe
University of Virginia, Biocomplexity Institute, UVA
Research areas: Mathematics · Modeling and Simulation · Complexity and Algorithms in Graphs · COVID-19 epidemiological studies · Mathematical and Theoretical Epidemiology and Ecology Models
Employment
-
Biocomplexity Institute, UVA Division Director
Education
Education history is unavailable.
Projects & Funding
Projects & funding information is unavailable.
Publications (24)
- Tunable and computationally efficient framework for retrofitting: Budget allocation using income and spatial equity Save
- Integrated framework to study genomic surveillance of selective sweeps in multivariants dynamics Save
- CHARGE-MAP: An integrated framework to study the multicriteria EV charging infrastructure expansion problem Save
- Denoising Diffusion Probabilistic Models for Coastal Inundation Forecasting Save
- Welfare optimization for resource allocation with peer effects Save
- IrrMap: A Large-Scale Comprehensive Dataset for Irrigation Method Mapping Save
- A simple model of coupled individual behavior and its impact on epidemic dynamics Save
- Epihiper—A high performance computational modeling framework to support epidemic science Save
- Synchronous Dynamical Systems on Directed Acyclic Graphs: Complexity and Algorithms Save
- A simplicial epidemic model for COVID-19 spread analysis Save
- Scenario Design for Infectious Disease Projections: Integrating Concepts from Decision Analysis and Experimental Design Save
- Identifying Complicated Contagion Scenarios from Cascade Data Save
- Budget-constrained optimal and equitable retrofitting problems for achieving energy efficiency Save
- Airborne disease transmission during indoor gatherings over multiple time scales: Modeling framework and policy implications Save
- Disease burden among Ukrainians forcibly displaced by the 2022 Russian invasion Save
- Evaluating the Utility of High-Resolution Proximity Metrics in Predicting the Spread of COVID-19 Save
- Ensembles of realistic power distribution networks Save
- Fundamental limitations on efficiently forecasting certain epidemic measures in network models Save
- An Automated Approach for Finding Spatio-Temporal Patterns of Seasonal Influenza in the United States: Algorithm Validation Study Save
- Forecasting dengue and influenza incidences using a sparse representation of Google trends, electronic health records, and time series data Save