Just the core logic of VA algorithms in R. Presented in an RStudio R Markdown file.
For pedagogical purposes only. Real-world algorithm implemenations include significant additional functionality to ensure data quality, and they also work with far more symptoms and causes. As a result, real world algorithm performance may be significantly different from the model algorithms presented here.
References:
NBC
Miasnikof, P., Giannakeas, V., Gomes, M., Aleksandrowicz, L., Shestopaloff, A. Y., Alam, D., ... & Jha, P. (2015). Naive Bayes classifiers for verbal autopsies: comparison to physician-based classification for 21,000 child and adult deaths. BMC medicine, 13(1), 286.
InterVA
Byass, P., Chandramohan, D., Clark, S. J., D'ambruoso, L., Fottrell, E., Graham, W. J., ... & Krishnan, A. (2012). Strengthening standardised interpretation of verbal autopsy data: the new InterVA-4 tool. Global health action, 5(1), 19281.
InSilicoVA
McCormick, T. H., Li, Z. R., Calvert, C., Crampin, A. C., Kahn, K., & Clark, S. J. (2016). Probabilistic cause-of-death assignment using verbal autopsies. Journal of the American Statistical Association, 111(515), 1036-1049.
Tariff
James, S. L., Flaxman, A. D., & Murray, C. J. (2011). Performance of the Tariff Method: validation of a simple additive algorithm for analysis of verbal autopsies. Population Health Metrics, 9(1), 31.