SPEW: Synthetic Populations and Ecosystems of the World

TL;DR

SPEW is an open-source framework that generates customizable synthetic ecosystems for agent-based modeling, supporting diverse sampling methods and applicable worldwide, with demonstrated accuracy and efficiency.

Contribution

It introduces a flexible, open-source R package for creating synthetic populations and ecosystems adaptable to various data sources and geographic regions.

Findings

Generated over five billion human agents globally

Supported diverse sampling methods for agent characteristics and locations

Provided diagnostics for assessing synthetic ecosystem quality

Abstract

Agent-based models (ABMs) simulate interactions between autonomous agents in constrained environments over time. ABMs are often used for modeling the spread of infectious diseases. In order to simulate disease outbreaks or other phenomena, ABMs rely on "synthetic ecosystems," or information about agents and their environments that is representative of the real world. Previous approaches for generating synthetic ecosystems have some limitations: they are not open-source, cannot be adapted to new or updated input data sources, and do not allow for alternative methods for sampling agent characteristics and locations. We introduce a general framework for generating Synthetic Populations and Ecosystems of the World (SPEW), implemented as an open-source R package. SPEW allows researchers to choose from a variety of sampling methods for agent characteristics and locations when generating synthetic ecosystems for any geographic region. SPEW can produce synthetic ecosystems for any agent (e.g. humans, mosquitoes, etc), provided that appropriate data is available. We analyze the accuracy and computational efficiency of SPEW given different sampling methods for agent characteristics and locations and provide a suite of diagnostics to screen our synthetic ecosystems. SPEW has generated over five billion human agents across approximately 100,000 geographic regions in about 70 countries, available online.