Impact of Seeding and Spatial Heterogeneity on Metapopulation Disease Dynamics

Rohit Srinivas Rajuladevi, Madhav Marathe, Przemyslaw Porebski, Srinivasan Venkataramanan

Keywords: Metapopulation models, Computational epidemiology, Information Theory

TL;DR: This is a work in progress paper to better characterize the role of initial seeding on disease spread using various metrics such as entropy, and also to demonstrate the utility of open frameworks and datasets for pandemic preparedness.

Abstract:

Metapopulation models capture the spatial interactions in disease dynamics through mobility and mixing matrices among regions of interest. However, the impact of seeding (initialization) in such networks is not well understood. We have constructed and open-sourced metapopulation networks for countries around the world (Patch-Flow) and we use them to study the effect of seeding using an extension of a discrete-time SEIR simulator, PatchSim. The impact of initialization is studied by looking at the resulting epidemic curves.
We use various metrics to characterize the epidemic curves, including those based on epidemic intensity entropy. Using these, we study the impact of levels of connectivity, skewness in seeding, and spatial resolution at the national scale. We find that these effects vary across countries and are more pronounced at certain transmissibility levels. This study provides early insights into the impact of model initialization and demonstrates the use of PatchFlow networks.