Publications by authors named "Raphael O Anyango"
Article Synopsis
- * A machine learning approach was applied using data from the VIDA and EFGH-Shigella studies in rural Kenya to create predictive models for LGF among children aged 6-35 months, encompassing 65 potential predictors including demographic and health-related factors.
- * The models showed a prevalence of LGF at 16.9% and 22.4% in different cohorts, with the gradient boosting model providing the best prediction accuracy, demonstrating its usefulness in identifying at-risk children for targeted healthcare interventions
Background: Although is an important cause of diarrhea in Kenyan children, robust research platforms capable of conducting incidence-based estimates and eventual targeted clinical trials are needed to improve -related outcomes in children. Here, we describe characteristics of a disease surveillance platform whose goal is to support incidence and consequences of diarrhea as part of multicounty surveillance aimed at preparing sites and assembling expertise for future vaccine trials.
Methods: We mobilized our preexisting expertise in shigellosis, vaccinology, and diarrheal disease epidemiology, which we combined with our experience conducting population-based sampling, clinical trials with high (97%-98%) retention rates, and healthcare utilization surveys.