Micromobility systems and disease transmission: a spatially explicit agent-based approach

Abstract

We propose a novel micro-level and spatially explicit agent-based modeling framework dubbed TranEpiSim to model the spread of infectious diseases through micromobility systems and a baseline population in an urban area. A case study is conducted on the Chicago City public bikesharing system to demonstrate the ability of the model to isolate the role of transportation as a vector and assess the efficacy of the model. Results show that the emergence of viral disease through micromobility transportation is possible, but the overall impact of the system on the disease dynamics in a worst-case scenario, especially with the current size of the system, is rather small. The proposed model offers a comprehensive approach to estimate the impact of micromobility (and more generally, specific transportation modes) on disease spread by considering infections that occur both at destinations and in/on transportation vehicles and infrastructure. The spatial pattern for the risk of exposure shows a higher risk in the central business district and north of it, where most of the shared bike transportation occurs. Because of its intrinsic features, the proposed framework is uniquely placed to assess the efficacy of interventions and make trade-offs between competing scenarios when dealing with epidemics.

Keywords:

• Micromobility
• infectious disease
• agent-based modelling
• synthetic population
• human contact network

Data and codes availability statement

The data, codes, and instructions that support the findings of this study are available with the identifier(s) at the link (https://github.com/behnamnkp/TranEpiSim) and here is the related DOI (10.5281/zenodo.8327873).

Disclosure statement

The authors report there are no competing interests to declare.

Notes

1 By public transportation, we mean transportation available for use by the general public. This does not imply vehicles are owned and/or operated by a public agency.

4 https://graph-tool.skewed.de/.

5 https://networkx.org/.

Additional information

Notes on contributors

Behnam Nikparvar

Behnam Nikparvar holds a BSc. in Geomatics Engineering from University of Isfahan, an MSc. in Geographic Information Systems from KN Toosi University of Technology, and a PhD in Infrastructure and Environmental Systems from University of North Carolina at Charlotte. Currently, he works as a Geospatial data scientist at Odyssey Logistics & Technology, focusing his research on mobility, transportation, spatial machine learning, and artificial intelligence.

Jean-Claude Thill

Jean-Claude Thill is Knight Distinguished Professor of Public Policy at the University of North Carolina at Charlotte. For more than three decades, he has developed his scholarship in computational social sciences, particularly emphasizing the spatial and regional perspective. He was a 2012 recipient of the Edward L. Ullman Award for Significant Contributions to Transportation Geography, awarded by the American Association of Geographers. He was elected a fellow of the Regional Science Association International and of the American Association of Geographers. In 2023, he was recognized by the North American Regional Science Council for his contribution to research in regional science with the Walter Isard Award.