Spatial epidemiology and adaptive targeted sampling to manage the Chagas disease vector Triatoma dimidiata

TL;DR

This paper introduces an adaptive sampling method using Bayesian geostatistical models to efficiently target houses for Chagas disease vector control, reducing sampling effort while maintaining effectiveness.

Contribution

It develops a sequential, community-specific sampling framework that transitions from uncertainty-based to risk-based targeting using a single parameter, improving efficiency in vector control.

Findings

Fewer homes need to be sampled compared to random sampling.

The method effectively reduces infestation rates below thresholds.

Incorporating socioeconomic data enhances savings but affects consistency.

Abstract

Widespread application of insecticide remains the primary form of control for Chagas disease in Central America, despite only temporarily reducing domestic levels of the endemic vector Triatoma dimidiata and having little long-term impact. Recently, an approach emphasizing community feedback and housing improvements has been shown to yield lasting results. However, the additional resources and personnel required by such an intervention likely hinders its widespread adoption. One solution to this problem would be to target only a subset of houses in a community while still eliminating enough infestations to interrupt disease transfer. Here we develop a sequential sampling framework that adapts to information specific to a community as more houses are visited, thereby allowing us to efficiently find homes with domiciliary vectors while minimizing sampling bias. The method fits Bayesian geostatistical models to make spatially informed predictions, while gradually transitioning from prioritizing houses based on prediction uncertainty to targeting houses with a high risk of infestation. A key feature of the method is the use of a single exploration parameter, $\alpha$, to control the rate of transition between these two design targets. In a simulation study using empirical data from five villages in southeastern Guatemala, we test our method using a range of values for $\alpha$, and find it can consistently select fewer homes than random sampling, while still bringing the village infestation rate below a given threshold. We further find that when additional socioeconomic information is available, much larger savings are possible, but that meeting the target infestation rate is less consistent, particularly among the less exploratory strategies. Our results suggest new options for implementing long-term T. dimidiata control.