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

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, α, 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 α, 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.

Adapting vector surveillance using Bayesian experimental design: An application to an ongoing tick monitoring program in the southeastern United States

“…Second, specialized optimization strategies for finding optimal survey schedules should be developed, as spatiotemporal survey design presents distinct challenges such as incomplete control over the environmental conditions available among possible visits. Another possibility is to employ adaptive sampling, where locations are visited in smaller batches and the data collected from each iteration are able to inform sampling in future batches, although updating data sequentially leads to additional logistical constraints during surveillance ( Case et al, 2022 ; Chipeta et al, 2016 ). A final avenue for future work is in the choice of design criteria, which may change depending on the specific goals of the analysis.…”

“…Though usually such sampling decisions are made in an ad hoc manner, a decision-theoretic alternative is to assign a score to potential sampling times and locations and attempt to find visits maximizing some objective function ( Dorazio and Johnson, 2003 ). Deciding future visits for surveillance or control efforts may then be framed as an optimal experimental design problem, where a calendar of sampling times and/or locations is chosen to maximize their information content or increase the impact of vector control measures ( Case et al, 2022 ; Reich et al, 2018 ). Because observational studies of tick distributions involve complex interactions between variable environmental conditions, population dynamics, and the collection method used, it is important to employ design criteria that are robust to uncertainty in model parameters and experimental conditions ( Pronzato and Walter, 1985 ).…”

Operationalizing cultural adaptation to climate change: contemporary examples from United States agriculture

“…In epidemiology, spatial correlations between disease cases can be a signal of potential transmission, after controlling for covariates (e.g. [58]). Even then, a growing spatial cluster does not imply causation and must be interpreted carefully [59].…”

Domestic (re)infestation risk with the main vector Triatoma infestans increases with surrounding green vegetation and social vulnerability in the Argentine Chaco

“…In the current study, using a hierarchical Bayesian framework that allows for the spatial features of the processes involved, we modeled the relationship among the occurrence of domestic (re)infestation with T. infestans , green vegetation density surrounding rural houses (estimated by NDVI) and selected household sociodemographic characteristics over a 10-year period. A key aspect of this study is the inclusion of the spatial component, as several studies have rejected the hypothesis of spatial independence of house infestation with triatomines [ 16 , 27 , 28 ]. Based on empirical evidence collected in northern Argentina [ 7 , 17 ], we hypothesized that disadvantageous sociodemographic characteristics (e.g.…”