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.

翻译：患病率调查通常用于监测大规模药物管理（MDA）项目控制被忽视热带病（NTDs）的有效性。我们提出了一种衰减调整的时空（DAST）模型，该模型明确地考虑了MDA随时间变化对NTD患病率的影响，为从稀疏的调查数据中估计干预效果提供了一个灵活且可解释的框架。通过针对土壤传播的蠕虫病和淋巴丝虫病的案例研究，我们证明了在包括量化MDA影响和支持短期项目预测的目标下，DAST是标准地统计学模型的一个实用替代方案。我们还讨论了扩展模型和可识别性挑战，主张在可用数据过于稀疏而无法支持高度参数化模型估计的情况下，采用数据驱动的简约性而非复杂性。