Bioinformatics, Modeling & Biostatistics Core

Bioinformatics, Modeling & Biostatistics Core
The overall goal of the Bioinformatics, Modeling, and Biostatistics Core (BMBC) is to enhance the ability of TB researchers to apply the most modern computational approaches to their studies to address critical gaps in TB knowledge. The Core will ensure that TB researchers in general, and new or early stage investigators in particular, are not limited in their research aspirations by access to, or ability to navigate, resources and expertise in computational and analytical methods.

Specific Aims

  1. Recruit and support junior investigators who seek to incorporate the tools of bioinformatics, mathematical modeling, and biostatistics for TB research
  2. Engage computational researchers to pair with TB researchers to develop effective collaborations that apply modern computational techniques to advance TB research
  3. Provide access to resources required for modern computational research

BMBC Services

  • Statistical consultation on TB-related grant proposals 
  • Methodological assistance with computational modeling projects 
  • Linkage to high-performance computing facilities available at JHU, including ARCH (Advanced Research Computing at Hopkins) 
  • Assistance with high-throughput data management and bioinformatics 
  • Consultation on grants that might benefit from a modeling, bioinformatics, or statistical component

Featured Publications

The American Journal of Respiratory and Critical Care Medicine
Tuberculosis, Wildfires, and Case-crossover Studies: An Epidemiological Trifecta?
Checkley W. Tuberculosis, Wildfires, and Case-crossover Studies: An Epidemiological Trifecta? Am J Respir Crit Care Med. 2023 Feb 1;207(3):242-243. doi: 10.1164/rccm.202210-1936ED. PMID: 36315435; PMCID: PMC9896637.
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Multicomponent strategy with decentralised molecular testing for tuberculosis in Uganda: a cost and cost-effectiveness analysis
Thompson RR, Nalugwa T, Oyuku D, Tucker A, Nantale M, Nakaweesa A, Musinguzi J, Reza TF, Zimmer AJ, Ferguson O, Turyahabwe S, Joloba M, Cattamanchi ...
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Infectious and clinical tuberculosis trajectories: Bayesian modeling with case finding implications
Theresa S Ryckman, David W Dowdy , Emily A Kendall
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