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A Framework for Network-Based Epidemiological Modeling of Tuberculosis Dynamics Using Synthetic Datasets

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Abstract

We present a framework for discrete network-based modeling of TB epidemiology in US counties using publicly available synthetic datasets. We explore the dynamics of this modeling framework by simulating the hypothetical spread of disease over 2 years resulting from a single active infection in Washtenaw County, MI. We find that for sufficiently large transmission rates that active transmission outweighs reactivation, disease prevalence is sensitive to the contact weight assigned to transmissions between casual contacts (that is, contacts that do not share a household, workplace, school, or group quarter). Workplace and casual contacts contribute most to active disease transmission, while household, school, and group quarter contacts contribute relatively little. Stochastic features of the model result in significant uncertainty in the predicted number of infections over time, leading to challenges in model calibration and interpretation of model-based predictions. Finally, predicted infections were more localized by household location than would be expected if they were randomly distributed. This modeling framework can be refined in later work to study specific county and multi-county TB epidemics in the USA.

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Acknowledgements

This research was supported by NIH Grants R01AI123093 and U01 HL131072 awarded to DEK. The 2010 U.S. Synthetic Population database was created by RTI International, which is funded by the National Institutes of General Medical Sciences (NIGMS).

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  1. Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA

    Marissa Renardy & Denise E. Kirschner

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  1. Marissa Renardy
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  2. Denise E. Kirschner
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Correspondence to Denise E. Kirschner.

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Renardy, M., Kirschner, D.E. A Framework for Network-Based Epidemiological Modeling of Tuberculosis Dynamics Using Synthetic Datasets. Bull Math Biol 82, 78 (2020). https://doi.org/10.1007/s11538-020-00752-9

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