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Imran Hashmi
Imran Mahmood

Senior Postdoctoral researcher in Agent-based Modeling

Education

Affiliations

Bio

My recent focus of research has been the application of Agent-based approach in the infectious disease modeling. I have recently been focusing on the large scale, high performance simulation framework development for the pandemics. I am the main developer of the Flu and Corona virus Simulator (FACS), which is a simulation tool that models the spread of infectious disease at the sub-national level and incorporates geospatial data sources from Open Street Map to extract buildings and residential areas within a predefined region. Using FACS, we can both model Covid-19 spread at the local level and provide estimates of the spread of infections and hospital arrivals for different scenarios. FACS provides an open-ended platform for the specification and implementation of the primary components of ABS: (i) Agents; (ii) Virtual environment and (iii) Rule-set using a systematic Simulation Development Approach. It mainly specializes in modeling the complex dynamics e.g., agent movements and provides the ability to simulate a large population of agents with microscopic details using remote supercomputers, thus offers numerous benefits including: high performance, high scalability and greater re-usability. FACS generalizes the process of disease modelling and provides a template to model any infectious disease. Thus, allowing non-programmers (e.g., epidemiologists and healthcare data scientists) to use the framework as a disease modelling suite. FACS offers a built-in location graph construction tool that allows import of large spatial data-sets (e.g., Open Street Map), automated parsing and pre-processing of the spatial data and generating buildings of various types, thus allowing an ease in the synthesis of the virtual environment for the region under consideration. I am the lead developer of the Geo-Spatial SEIR Cast ABM simulation framework for the Department of Health, Florida. This framework utilizes a data driven (census driven) synthetic population generator, which produced a very large-scale agent population at a state/county scale, with complex structural attributes and behavioral logic. This framework helps Florida Health simulate and predict infectious disease transmission and evaluate scenario such as vaccination rollout plans and schools, workplace health interventions. I am also exploring Digital Twin technologies at City scale for pandemic analytics.

Preferred Pronouns

He/him/his

Personal Academic Website

Research/Topics of Interest

Agent-based models
Apply models to public health
Between-host modeling
Compartmental models
Forecasting
Geospatial models
Health systems
Intra-host modeling
Machine learning models
Mobility
Network models
New methods development
Outbreak science
Public health application of models
Public health policy/interventions
Social networks/contact patterns
Spatial transmission patterns
Surveillance/case detection
Time-series models
Vaccines
Synthetic Populations
Geospatial Datascience
Agent based Simulations
High Performance Computing
Geospatial Visual Analytics
Disease Modeling
AI / Machine Learning
Python
R
Parallel & Distributed Simulations
Multiscale Simulations
Hybrid SImulations

Pathogens/Diseases of Main Interest/Expertise

COVID-19
Dengue
Ebola
Pandemic influenza
Respiratory diseases
Season influenza
Vaccine-preventable diseases
Vector-borne diseases
Zika

Countries of Work/Collaboration

United States
Sweden
United Kingdom
Pakistan

Projects

Papers

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