Additional file 1 of Understanding the impact of covariates on the classification of implementation units for soil-transmitted helminths control: a case study from Kenya
Creators: name: Puranik, Amitha
name: Diggle, Peter J.
name: Odiere, Maurice R.
name: Gass, Katherine
name: Kepha, Stella
name: Okoyo, Collins
name: Mwandawiro, Charles
name: Wakesho, Florence
name: Omondi, Wycliff
name: Sultani, Hadley Matendechero
name: Giorgi, Emanuele
Publisher: name: figshare
Year: 2024
Subjects: subject: Statistics
subject: FOS: Mathematics

description: Additional file 1: Figure S1. Map of empirical logit - STH in Kenya. Figure S1a. Map of empirical logit - STH in Kenya by region. Table S1. Variables and the source. Table S1a. Covariates considered in different stages of selection for the final model. Table S2. Estimates and corresponding standard errors of the Binomial mixed model with unstructured random effects fitted with covariates to the STH prevalence data. Figure S2. Empirical variogram of the empirical logit of infection with any STH and theoretical variogram. Figure S3a. Scatter plots for empirical logit transformation against each of the selected covariates – Western region of Kenya. Figure S3b. Scatter plots for empirical logit transformation against each of the selected covariates – Eastern region of Kenya. Figure S3c. Map of covariates included in the geostatistical model and map of population density; Western region (top) and Eastern region (bottom). Figure S4. Standard error of predicted prevalence obtained from the geostatistical models without and with covariates in Western region of Kenya. Figure S5. Standard error of predicted prevalence obtained from the geostatistical models without and with covariates in Eastern region of Kenya. Table S3. Mean of log of standard errors of predicted prevalence of STH. Figure S6. Predictive probability maps for endemicity level between 10% and 20% for STH obtained from geostatistical models without and with covariates in Western region of Kenya. Figure S7. Predictive probability maps for endemicity level between 20% and 50% for STH obtained from geostatistical models without and with covariates in Western region of Kenya. Figure S8. Predictive probability maps for endemicity level greater than 50% for STH obtained from geostatistical models without and with covariates in Western region of Kenya. Figure S9. Predictive probability maps for endemicity level less than 2% for STH obtained from geostatistical models without and with covariates in Eastern region of Kenya. Figure S10. Predictive probability maps for endemicity level between 2% and 10% for STH obtained from geostatistical models without and with covariates in Eastern region of Kenya. Figure S11. Predictive probability maps for endemicity level between 10% and 20% for STH obtained from geostatistical models without and with covariates in Eastern region of Kenya. Figure S12. Predictive probability maps for endemicity level between 20% and 50% for STH obtained from geostatistical models without and with covariates in Eastern region of Kenya. Figure S13. Predictive probability maps for endemicity level greater than 50% for STH obtained from geostatistical models without and with covariates in Eastern region of Kenya. Figure S14. Proportion of correct classification and their differences at each sub-county for simulated dataset - sample size: 875; average prevalence: 12%. Figure S15. Proportion of correct classification and their differences at each sub-county for simulated dataset - sample size: 438; average prevalence: 6%. Figure S16. Proportion of correct classification and their differences at each sub-county for simulated dataset - sample size: 438; average prevalence: 12%. Figure S17. Proportion of correct classification and their differences at each sub-county for simulated dataset - sample size: 618; average prevalence: 10%. Figure S18. Proportion of correct classification and their differences at each sub-county for simulated dataset - sample size: 309; average prevalence: 1%. Figure S19. Proportion of correct classification and their differences at each sub-county for simulated dataset - sample size: 309; average prevalence: 10%.