Letters in Biomathematics
Antibiotic-resistant tuberculosis (TB) strains pose a major challenge to TB eradication. Existing US epidemiological models have not fully incorporated the impact of antibiotic-resistance. To develop a more realistic model of US TB dynamics, we formulated a compartmental model integrating single-and multi-drug resistance. We fit twenty-seven parameters to twenty-two years of historical data using a genetic algorithm to minimize a non-differentiable error function. Since counts for several compartments are not available, many parameter combinations achieve very low error. We demonstrate that a crowd of near-best fits can provide compelling new evidence about the ranges of key parameters. While available data is sparse and insufficient to produce point estimates, our crowd of near-best fits computes remarkably consistent predictions about TB prevalence. We believe that our crowd-based approach is applicable to a common problem in mathematical biological research, namely situations where data are sparse and reliable point estimates cannot be directly obtained.
Compartmental models, Disease dynamics, Genetic algorithm, Model fitting, Tuberculosis
Mainou, Ellie, Gwen Spencer, Dylan Shepardson, and Robert Dorit. 2020. “The Wisdom of a Crowd of Near-Best Fits”. Letters in Biomathematics 7 (1), 15–35. https://lettersinbiomath.journals.publicknowledgeproject.org/index.php/lib/article/view/261.