  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.
