2025/12/03 by Emanuele Giorgi, Giorgi, Emanuele, Claudio Fronterrè +3
Immunology and Microbiology · Medicine · Veterinary · #Applications (stat.AP) #FOS: Computer and information sciences #Helminth infection and control #Methodology (stat.ME) #Parasites and Host Interactions #Parasitic Diseases Research and Treatment
paper · pdf · doi:10.48550/arxiv.2512.03760
openalex publication_date 2025/12/03 · openalex created_date 2025/12/05 · openalex updated_date 2026/07/28
Prevalence surveys are routinely used to monitor the effectiveness of mass drug administration (MDA) programmes for controlling neglected tropical diseases (NTDs). We propose a decay-adjusted spatio-temporal (DAST) model that explicitly accounts for the time-varying impact of MDA on NTD prevalence, providing a flexible and interpretable framework for estimating intervention effects from sparse survey data. Using case studies on soil-transmitted helminths and lymphatic filariasis, we show that DAST offers a practical alternative to standard geostatistical models when the objective includes quantifying MDA impact and supporting short-term programmatic forecasting. We also discuss extensions and identifiability challenges, advocating for data-driven parsimony over complexity in settings where the available data are too sparse to support the estimation of highly parameterised models.