ArticleApplied network science2018
A spatially heterogeneous network-based metapopulation software model applied to the simulation of a pulmonary tuberculosis infection.
Article in Applied network science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- The effect of population size for pathogen transmission on prediction of COVID-19 spread.Scientific reports · 2021Article
- Development of New Tuberculosis Drugs: Translation to Regimen Composition for Drug-Sensitive and Multidrug-Resistant Tuberculosis.Annual review of pharmacology and toxicology · 2021Review
- Modelling the effects of environmental heterogeneity within the lung on the tuberculosis life-cycle.Journal of theoretical biology · 2020Article
- Mechanistic Modeling of Mycobacterium tuberculosis Infection in Murine Models for Drug and Vaccine Efficacy Studies.Antimicrobial agents and chemotherapy · 2020Article
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4 authors.
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Abstract
Tuberculosis (TB) is an ancient disease that, although curable, still accounts for over 1 million deaths worldwide. Shortening treatment time is an important area of research but is hampered by the lack of models that mimic the full range of human pathology. TB shows distinct localisations during different stages of infection, the reasons for which are poorly understood. Greater understanding of how heterogeneity within the human lung influences disease progression may hold the key to improving treatment efficiency and reducing treatment times. In this work, we present a novel
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