TY - JOUR

T1 - Adaptive geostatistical design and analysis for prevalence surveys

AU - Chipeta, Michael

AU - Terlouw, Dianne J.

AU - Phiri, Kamija S.

AU - Diggle, Peter John

N1 - This is the author’s version of a work that was accepted for publication in Spatial Statistics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial Statistics, 15, 2016 DOI: 10.1016/j.spasta.2015.12.004

PY - 2016/2

Y1 - 2016/2

N2 - Non-adaptive geostatistical designs (NAGDs) offer standard ways of collecting and analysing geostatistical data in which sampling locations are fixed in advance of any data collection. In contrast, adaptive geostatistical designs (AGDs) allow collection of geostatistical data over time to depend on information obtained from previous information to optimise data collection towards the analysis objective. AGDs are becoming more important in spatial mapping, particularly in poor resource settings where uniformly precise mapping may be unrealistically costly and the priority is often to identify critical areas where interventions can have the most health impact. Two constructions are: singleton and batch adaptive sampling. In singleton sampling, locations xi are chosen sequentially and at each stage, xk+1 depends on data obtained at locations x1,…,xk. In batch sampling, locations are chosen in batches of size b>1, allowing each new batch, {x(k+1),…,x(k+b)}, to depend on data obtained at locations x1,…,xkb. In most settings, batch sampling is more realistic than singleton sampling. We propose specific batch AGDs and assess their efficiency relative to their singleton adaptive and non-adaptive counterparts using simulations. We then show how we are applying these findings to inform an AGD of a rolling Malaria Indicator Survey, part of a large-scale, five-year malaria transmission reduction project in Malawi.

AB - Non-adaptive geostatistical designs (NAGDs) offer standard ways of collecting and analysing geostatistical data in which sampling locations are fixed in advance of any data collection. In contrast, adaptive geostatistical designs (AGDs) allow collection of geostatistical data over time to depend on information obtained from previous information to optimise data collection towards the analysis objective. AGDs are becoming more important in spatial mapping, particularly in poor resource settings where uniformly precise mapping may be unrealistically costly and the priority is often to identify critical areas where interventions can have the most health impact. Two constructions are: singleton and batch adaptive sampling. In singleton sampling, locations xi are chosen sequentially and at each stage, xk+1 depends on data obtained at locations x1,…,xk. In batch sampling, locations are chosen in batches of size b>1, allowing each new batch, {x(k+1),…,x(k+b)}, to depend on data obtained at locations x1,…,xkb. In most settings, batch sampling is more realistic than singleton sampling. We propose specific batch AGDs and assess their efficiency relative to their singleton adaptive and non-adaptive counterparts using simulations. We then show how we are applying these findings to inform an AGD of a rolling Malaria Indicator Survey, part of a large-scale, five-year malaria transmission reduction project in Malawi.

KW - Adaptive sampling strategies

KW - Spatial statistics

KW - Geostatistics

KW - Malaria

KW - Prevalence mapping

U2 - 10.1016/j.spasta.2015.12.004

DO - 10.1016/j.spasta.2015.12.004

M3 - Journal article

VL - 15

SP - 70

EP - 84

JO - Spatial Statistics

JF - Spatial Statistics

SN - 2211-6753

ER -