Spatial and Metapopulation Modeling

Online Lab Schedule:

• Monday, June 22, 1:00 - 2:30 PM ET and 3:00 - 4:30 PM ET
• Tuesday June 23, 1:00 - 2:30 PM ET and 3:00 - 4:30 PM ET

Classroom: Virtual

Module Summary:

Understanding how infectious diseases spread across space is fundamental for anticipating epidemic trajectories and evaluating intervention strategies. Spatial and metapopulation models provide a powerful framework for capturing transmission dynamics in systems where populations are geographically distributed yet connected through mobility and interaction patterns. These models make it possible to assess the role of human movement, population structure, and spatial heterogeneities in shaping epidemic spread and persistence.

This module will introduce the conceptual foundations of spatial disease modeling, from simple diffusion processes to network-based metapopulation frameworks. We will explore how mobility networks—whether local commuting, long-distance travel, or global air transportation—mediate pathogen dispersal, and how spatial coupling influences epidemic synchrony, seeding events, and spatial hierarchies of transmission. Through real-world examples, we will discuss how these modeling approaches support epidemic risk assessment and guide public health decision-making, from forecasting to targeted interventions.

Module Content:

Lectures

1. 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.
2. Metapopulation Disease Models : Explore the theory and applications of metapopulation models for understanding localized outbreaks and their interplay within broader spatial systems.
3. 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

1. Introduction to Networks & Python : Get hands-on experience with Python for network analysis, focusing on tools and techniques for modeling infectious disease spread.
2. 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.
3. Modeling Spatial Disease Transmission : Develop and implement spatial disease transmission models, integrating key concepts from mobility and network analysis.
4. 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.