Additional file 1: of Model-based analysis of experimental data from interconnected, row-configured huts elucidates multifaceted effects of a volatile chemical on Aedes aegypti mosquitoes
Creators: name: Bosch, Quirine Ten
name: Castro-Llanos, Fanny
name: Hortance Manda
name: Morrison, Amy
name: Grieco, John
name: Achee, Nicole
name: T. Alex Perkins
Publisher: name: figshare
Year: 2018
Subjects: subject: Medicine
subject: Ecology
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: 69999 Biological Sciences not elsewhere classified
subject: 19999 Mathematical Sciences not elsewhere classified
subject: FOS: Mathematics
subject: FOS: Mathematics
subject: Developmental Biology
subject: Hematology
subject: 110309 Infectious Diseases
subject: FOS: Health sciences
subject: FOS: Health sciences
subject: Computational Biology

description: Table S1. Average Gelman-Rubin statistics across simulated data sets (median and the upper bound of the 95% confidence interval). Figure S1. Correlations between parameter posteriors of model fit on baseline scenario with all parameters estimated. Marginal posteriors are depicted on the diagonals. The numbers on the right of the diagonal depict the Spearman rank correlation coefficients for each side by side comparison. Figure S2. Correlations between parameter posteriors of model fit on baseline scenario with r i fixed (rT: 0.040, r1:0.036, r2: 0.088). Marginal posteriors are depicted on the diagonals. The numbers on the right of the diagonal depict the Spearman rank correlation coefficients for each side by side comparison. Under this parameterization, the movement rate q i is exactly equal to the product x i / r i . Figure S3. Correlations between parameter posteriors of model fit on low dosage scenario with r i fixed (rT: 0.040, r1:0.036, r2: 0.088). Marginal posteriors are depicted on the diagonals. The numbers on the right of the diagonal depict the Spearman rank correlation coefficients for each side by side comparison. Under this parameterization, the movement rate q i is exactly equal to the product x i / r i . Figure S4. Correlations between parameter posteriors of model fit on high dosage scenario with r i fixed (rT: 0.040, r1:0.036, r2: 0.088). Marginal posteriors are depicted on the diagonals. The numbers on the right of the diagonal depict the Spearman rank correlation coefficients for each side by side comparison. Under this parameterization, the movement rate q i is exactly equal to the product x i / r i . Figure S5. Posterior distributions of model parameters fitted to experimental data while fixing the values of r i at the 2.5th percentile of the posterior from the full parameter fit to the baseline data (rT: 0.031, r1:0.027, r2: 0.065). Posteriors are shown for the baseline (gray), low dosage (orange) and high dosage (pink) for the SR-hut (subscript 0) and huts 2 or 1 removed (subscript 2 and 1, respectively). a-c rates at which mosquitoes exit the huts, d proportion of movement from H1 (hut directly adjacent to the treatment hut) away from the SR-product. e-g knockdown rates and h loss to follow-up rates. Under this parameterization, the movement rate q i is exactly equal to the product x i / r i .The algorithm was run for 25,000 iterations with a ‘burn-in’ period of 10,000. Figure S6. Posterior distributions of model parameters fitted to experimental data while fixing the values of r i at the 97.5th percentile of the posterior from the full parameter fit to the baseline data (rT: 0.054, r1:0.040, r2: 0.11). Posteriors are shown for the baseline (gray), low dosage (orange) and high dosage (pink) for the SR-hut (subscript 0) and huts 2 or 1 removed (subscript 2 and 1, respectively). a-c rates at which mosquitoes exit the huts, d proportion of movement from H1 (hut directly adjacent to the treatment hut) away from the SR-product. e-g knockdown rates and h loss to follow-up rates. Under this parameterization, the movement rate q i is exactly equal to the product x i / r i . The algorithm was run for 25,000 iterations with a ‘burn-in’ period of 10,000. Figure S7. Gelman-Rubin convergence diagnostics by iteration for the baseline scenario. Figure S8. Gelman-Rubin convergence diagnostics by iteration for the low dosage scenario. Figure S9. Gelman-Rubin convergence diagnostics by iteration for the high dosage scenario. Figure S10. Trace plots for the baseline scenario. Figure S11. Trace plots for the low dosage scenario. Figure S12. Trace plots for the high dosage scenario. (DOCX 50818 kb)