ArticleBulletin of mathematical biology2020
A Framework for Network-Based Epidemiological Modeling of Tuberculosis Dynamics Using Synthetic Datasets.
Article in Bulletin of mathematical biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Agent-based modelling of Mycobacterium tuberculosis transmission: a systematic review.BMC infectious diseases · 2024Pooled it
- Determining parameter impact in systems biology models via sensitivity analysis: a comparative approach.NPJ systems biology and applications · 2026Article
- Emergence of Sprouty role from the signaling interplay of DUSP1 and DUSP4 interactions in NSCLC pathogenesis.NPJ systems biology and applications · 2026Article
- From data to decisions: a modular platform for modelling and simulation of infectious disease diffusion in networks.BMC medical informatics and decision making · 2026Article
- How do social network models compare to all-to-all models for forecasting tuberculosis epidemics? A mathematical modeling study.PloS one · 2026Article
- A social network analysis model approach to understand tuberculosis transmission in remote rural Madagascar.BMC public health · 2023Article
- Generating synthetic multidimensional molecular time series data for machine learning: considerations.Frontiers in systems biology · 2023Article
- Structural identifiability analysis of age-structured PDE epidemic models.Journal of mathematical biology · 2022Article
- Predicting the second wave of COVID-19 in Washtenaw County, MI.Journal of theoretical biology · 2020Article
- Predicting the second wave of COVID-19 in Washtenaw County, MI.medRxiv : the preprint server for health sciences · 2020Article
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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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