A decay-adjusted spatio-temporal model to account for the impact of mass drug administration on neglected tropical disease prevalence

TL;DR

The paper introduces a decay-adjusted spatio-temporal model that explicitly captures the time-varying effects of mass drug administration on neglected tropical disease prevalence, aiding monitoring and forecasting.

Contribution

It presents a novel flexible model that accounts for intervention decay, improving impact estimation from sparse survey data compared to standard geostatistical approaches.

Findings

DAST model effectively estimates MDA impact on NTD prevalence.

The model performs well in case studies on soil-transmitted helminths and lymphatic filariasis.

Extensions and challenges of the model are discussed.

Abstract

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.