Spatial and Metapopulation Modeling
Online Lab Meeting Times:
- Thursday July 10, 1:00 - 2:30 PM ET and 3:00 - 4:30 PM ET
- Friday July 11, 1:00 - 2:30 PM ET and 3:00 - 4:30 PM ET
Classroom: Virtual
Module Summary:
Metapopulation disease modeling plays a crucial role in understanding, predicting, and managing the spread of infectious diseases across interconnected populations. Its importance lies in its ability to capture transmission dynamics in complex systems where populations are distributed across distinct spatial locations but are connected through movement or interactions.
This module introduces spatial and metapopulation modeling in the context of infectious disease dynamics. Participants will learn the principles behind the geographic spread of diseases, explore mobility networks, and understand how spatial interactions impact disease transmission.
Module Content:
Lectures
- Mobility Networks and Spatial Infectious Disease Transmission: Learn how human mobility patterns influence the spatial spread of infectious diseases and understand the role of networks in disease transmission.
- Metapopulation Disease Models: Explore the theory and applications of metapopulation models for understanding localized outbreaks and their interplay within broader spatial systems.
- Applications for Public Health: Case Studies: Examine real-world scenarios where spatial and metapopulation models have informed public health strategies, showcasing their impact on decision-making.
Labs
- Introduction to Networks & Python: Get hands-on experience with Python for network analysis, focusing on tools and techniques for modeling infectious disease spread.
- Use and Analysis of Mobility Data in Disease Modeling: Learn to analyze mobility data to understand how population movement contributes to the geographic transmission of diseases.
- Modeling Spatial Disease Transmission: Develop and implement spatial disease transmission models, integrating key concepts from mobility and network analysis.
- Controlling the Geographic Spread of Disease: Experiment with intervention strategies and assess their effectiveness in managing the spatial spread of infectious diseases.
Prerequisites:
The course is ideal for graduate students, researchers, and public health professionals who have a background in infectious disease epidemiology and basic disease modeling. Prior experience with Python is beneficial but not required.
Instructors
Shweta Bansal, PhD
Professor, Department of Biology, Georgetown University
Dr. Bansal is a Professor at the Department of Biology at Georgetown University. She also serves as graduate faculty in the Global Infectious Diseases PhD Program and the Biology PhD Program and is an Affiliate Faculty for the Massive Data Institute, the Global Health Institute, and Earth Commons. With a passion for interdisciplinary approaches, she has made significant contributions to mathematical epidemiology, disease ecology, network science, and public health. Dr. Bansal's research has garnered widespread recognition for its practical applications in public health. Her work often involves leveraging large-scale datasets and advances computational techniques to understand disease dynamics and inform public health and animal health decision-making. Her work has been published in esteemed scientific journals, she regularly presents invited talks at international conferences, and has garnered support for her lab's research from the National Science Foundation, the National Institutes of Health, as well as private funders.
Vittoria Colizza, PhD
Research Director, INSERM (French National Institute for Health and Medical Research)
Dr. Colizza is a distinguished scientist and specializes in mathematical modeling of infectious diseases and computational epidemiology. Her work has significantly contributed to understanding the dynamics of various epidemics, including seasonal and pandemic influenza, Ebola, and the COVID-19 pandemic. She leads the EPIcx lab at INSERM, focusing on evaluating epidemic risks, predicting disease spread, and assessing the effectiveness of public health interventions. Trained as a physicist, Colizza earned her PhD in Statistical and Biological Physics from the International School for Advanced Studies in Italy. Her research integrates data on population behavior, such as mobility and contact patterns, to inform public health policies. Recognized for her contributions, she has received numerous awards, including the Knight of the Order of Merit of the Italian Republic and the prix Irène Joliot Curie.
Required Software:
Recommended Reading:
Primary research and tutorial articles will be provided for additional reading.
