Abstract
The identification of disease hotspots is an increasingly important public health problem. While geospatial modeling offers an opportunity to predict the locations of hotspots using suitable environmental and climatological data, little attention has been paid to optimizing the design of surveys used to inform such models. Here we introduce an adaptive sampling scheme optimized to identify hotspot locations where prevalence exceeds a relevant threshold. Our approach incorporates ideas from Bayesian optimization theory to adaptively select sample batches. We present an experimental simulation study based on survey data of schistosomiasis and lymphatic filariasis across four countries. Results across all scenarios explored show that adaptive sampling produces superior results and suggest that similar performance to random sampling can be achieved with a fraction of the sample size.
Highlights
The identification of disease hotspots is an increasingly important public health problem
For neglected tropical diseases (NTDs), decisions relating to mass drug administration (MDA) are based on infection prevalence estimates at the implementation unit (IU) level obtained from cross sectional surveys
We compared the performance of two approaches for selecting survey sites: random sampling (RS), where sites are chosen randomly; and adaptive sampling (AS), that follows the acquisition function of Eq (6)
Summary
The identification of disease hotspots is an increasingly important public health problem. Modeling and intuition suggest that as disease transmission declines, moving away from decision making at coarse scales towards a more targeted approach is more cost-effective[3] Such targeting is predicated on sufficiently accurate information on the location of sites with an infection prevalence above a policy relevant threshold, from hereon referred to as hotspots. Variations of contact tracing, whereby testing is targeted at families and neighbours of individuals found positive during surveys or routine surveillance, have been explored for a number of diseases including schistosomiasis[4], lymphatic filariasis[5] and malaria[6,7] Such approaches can, be expensive and can still fail to identify hotspots if positive individuals from those communities are not identified by the initial surveys
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