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Spatial modeling of Mycobacterium tuberculosis transmission with dyadic genetic relatedness data

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  • Joshua L. Warren
  • Melanie H. Chitwood
  • Benjamin Sobkowiak
  • Caroline Colijn
  • Ted Cohen

Abstract

Understanding factors that contribute to the increased likelihood of pathogen transmission between two individuals is important for infection control. However, analyzing measures of pathogen relatedness to estimate these associations is complicated due to correlation arising from the presence of the same individual across multiple dyadic outcomes, potential spatial correlation caused by unmeasured transmission dynamics, and the distinctive distributional characteristics of some of the outcomes. We develop two novel hierarchical Bayesian spatial methods for analyzing dyadic pathogen genetic relatedness data, in the form of patristic distances and transmission probabilities, that simultaneously address each of these complications. Using individual‐level spatially correlated random effect parameters, we account for multiple sources of correlation between the outcomes as well as other important features of their distribution. Through simulation, we show the limitations of existing approaches in terms of estimating key associations of interest, and the ability of the new methodology to correct for these issues across datasets with different levels of correlation. All methods are applied to Mycobacterium tuberculosis data from the Republic of Moldova, where we identify previously unknown factors associated with disease transmission and, through analysis of the random effect parameters, key individuals, and areas with increased transmission activity. Model comparisons show the importance of the new methodology in this setting. The methods are implemented in the R package GenePair.

Suggested Citation

  • Joshua L. Warren & Melanie H. Chitwood & Benjamin Sobkowiak & Caroline Colijn & Ted Cohen, 2023. "Spatial modeling of Mycobacterium tuberculosis transmission with dyadic genetic relatedness data," Biometrics, The International Biometric Society, vol. 79(4), pages 3650-3663, December.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:4:p:3650-3663
    DOI: 10.1111/biom.13836
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    References listed on IDEAS

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    2. Hodges, James S. & Reich, Brian J., 2010. "Adding Spatially-Correlated Errors Can Mess Up the Fixed Effect You Love," The American Statistician, American Statistical Association, vol. 64(4), pages 325-334.
    3. Neumayer, Eric & Plümper, Thomas, 2010. "Spatial Effects in Dyadic Data," International Organization, Cambridge University Press, vol. 64(1), pages 145-166, January.
    4. Finlay Campbell & Anne Cori & Neil Ferguson & Thibaut Jombart, 2019. "Bayesian inference of transmission chains using timing of symptoms, pathogen genomes and contact data," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-20, March.
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