Open-source satellite enumeration to map households: planning and targeting indoor residual spraying for malaria

Abstract

BackgroundDefining the number and location of sprayable structures (houses) is foundational to plan and monitor indoor residual spray (IRS) implementation, a primary intervention used to control the transmission of malaria. Only by mapping the location and type of all sprayable structures can IRS operations be planned, estimates of spray coverage determined, and targeted delivery of IRS to specific locations be achieved. Previously, field-based enumeration has been used to guide IRS campaigns, however, this approach is costly, time-consuming and difficult to scale. As a result, field-based enumeration typically fails to map all structures in a given area, making estimations less reliable and reducing the enumerated coverage.MethodsUsing open source satellite imagery and Geographic Information System software, satellite enumeration was conducted to guide IRS operations in 15 districts (91,302 km2) in northern Zambia during the 2014 spray season. Cost of satellite enumeration was compared to standard enumeration. Enumerated households were sampled to estimate sprayable surface area and wall type from the satellite enumeration using linear and logistic regression, respectively.ResultsIn comparison to the traditional field-based enumeration procedure, satellite-based enumeration was 22 times faster, and 10 times less costly. An estimated 98 % of the satellite enumerated buildings correctly classified roof type. Predicted surface area of each household correlated at a value of 0.91 with measured surface area of each household.ConclusionFor IRS campaigns, high quality and high coverage enumeration data aid in planning, through informed insecticide procurement. Through the identification of geographical areas and populations to target, enumeration data guide operations and assist monitoring and evaluation of IRS through the unbiased estimation of coverage achieved. Satellite enumeration represents a quick, cheap and accurate system to provide these data, and has potential applications beyond IRS for delivery of other targeted or non-targeted interventions (e.g. net distributions, mass drug administration, immunization campaigns, or even sampling frames for field studies).

Mapping and enumerating houses and households to support malaria control interventions on Bioko Island

“…The data are encrypted and hosted on an online server and are accessible only to authorized users. Demographic and environmental conditions on Bioko Island can limit the application of satellite imagery alone for mapping households, as was proposed by Kamanga and colleagues in their study in Zambia [16]. First, high population density urban areas on Bioko are equally targeted for control but difficult to map using remote sensing images.…”

“…Notwithstanding, for many malaria control programmes, enumeration methods remain rudimentary or nonexistent and relatively little effort seems to have been invested in instituting mapping and enumeration systems as part of malaria control programmes. Recently, new methodologies have been tested driven by the need to reassess vector control strategies towards more targeted approaches in sub-Saharan malaria endemic areas [15,16]. In Mozambique, houses in Mopeia district were enumerated by the use of GPS and satellite imagery triangulation; satellite images allowed the identification of areas that had been missed in the first round of enumeration that later were revisited for inclusion.…”

Modeling consensus and distance to delivering indoor residual spray (IRS) implementation strategies to control malaria transmission

“…Technicians in Lusaka, Zambia digitized structures in Eastern, Luapula, Muchinga, and Northern province visible in publicly available satellite imagery as part of the planning process of IRS campaigns in 2015 and 2016, as has been described elsewhere [22]. As part of the planning process of IRS campaigns, digitized structures were spatially aggregated into communities based upon distance between structures (< 50m), and then communities with fewer than 25 houses were deemed too small for IRS [11], and not included in the modeling.…”

Ending Malaria Transmission in the Asia Pacific Malaria Elimination Network (APMEN) Countries: Challenges and the Way Forward

“…Risk mapping and temporal forecasting of malaria using environmental and climatic factors as spatial and/or temporal risk predictors has been routinely undertaken [107,159,160]. Environmental data for geospatial and temporal analysis can be collected through satellite sensors or meteorological stations [159][160][161][162]. Image analysis techniques can be applied to satellite data to derive useful variables for the investigation of environmental drivers of malaria, such as land surface temperature, cold cloud duration (an indirect measure of rainfall), land use or land cover class, and normalised difference vegetation index (NDVI) [85,161].…”