A spatial decision support system for malaria elimination

Kelly, Gerard Charles (2013). A spatial decision support system for malaria elimination PhD Thesis, School of Population Health, The University of Queensland. doi:10.14264/uql.2015.293

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Author Kelly, Gerard Charles
Thesis Title A spatial decision support system for malaria elimination
School, Centre or Institute School of Population Health
Institution The University of Queensland
DOI 10.14264/uql.2015.293
Publication date 2013
Thesis type PhD Thesis
Open Access Status Other
Supervisor Archie Clements
Marcel Tanner
Andrew Vallely
Lasse Vestergaard
Total pages 180
Total colour pages 29
Total black and white pages 151
Language eng
Subjects 111711 Health Information Systems (incl. Surveillance)
111706 Epidemiology
090903 Geospatial Information Systems
Formatted abstract
Following renewed international attention and political commitment, malaria elimination is back on the world health agenda. Whilst there is currently a global focus and dedication of resources towards elimination, malaria programs pursuing this goal face significant challenges in meeting increased operational requirements, particularly in resource-poor and remote settings. Key priorities of elimination include the need to ensure the effective delivery of scaled-up services and interventions at optimal levels of coverage in target areas; the ability to rapidly identify transmission foci and target appropriate responses; and the capacity to readily provide detailed and accurate data to generate useful information, knowledge and evidence  throughout all phases of program implementation. A need for further research into new tools and approaches to support intensified malaria control and elimination is identified within the Roll Back Malaria (RBM) Global Malaria Action Plan (GMAP) as a core global strategy.
Aims of the thesis were to develop and implement a spatial decision support system (SDSS) for malaria elimination to guide program priorities in Solomon Islands and Vanuatu including: modern geographical reconnaissance (GR) mapping and data collection; frontline vector-control and malaria prevention intervention management; and high resolution geospatial surveillance-response.
Customized geographic information system (GIS) based SDSS were developed at a provincial level to support progressive malaria elimination campaigns in Solomon Islands and Vanuatu. Geographical Reconnaissance (GR) surveys were conducted in the elimination provinces of Temotu, Solomon Islands and Tafea, Vanuatu in 2008 and 2009 to rapidly map and enumerate households and collect associated population and household structure data using integrated handheld computers and global positioning systems (GPS). A SDSS approach was adopted to guide the planning, implementation and assessment of frontline focal indoor residual spraying (IRS) interventions on Tanna Island, Vanuatu in 2009. High-resolution surveillance-response systems were also developed in the elimination provinces of Temotu and Isabel, Solomon Islands and Tafea, Vanuatu in 2011 to support rapid reporting and mapping of confirmed cases by household, automatic classification and mapping of active transmission foci, and the generation of areas of interest (AOI) regions to conduct targeted response. Quantitative and qualitative analysis were conducted throughout the course of the thesis to assess the performance and acceptability of the SDSS-framework. A retrospective overall examination of the customised SDSS applications developed to support elimination in Solomon Islands and Vanuatu was also conducted in 2013 to review the role of SDSS for malaria elimination.
A total of 10,459 households were mapped and enumerated, with a population of 43,497 and 30,663 household structures recorded and uploaded into the SDSS framework during three GR surveys. Household maps, as well as detailed  summaries were extracted from the SDSS and used to describe the spatial distribution of the target population in the elimination provinces. A map-based SDSS application was used identify the focal IRS boundary on Tanna Island and delineate 21 individual operation areas comprising 187 settlements and 3,422 households. Household distribution maps, data summaries and checklists were generated to support IRS implementation. Spray coverages of 94.4% of households and 95.7% of the population were achieved. Spray status maps were also produced at a sub-village level to visualise the delivery and coverage of IRS by household. A total of 183 confirmed cases were reported and mapped in the SDSS and used to classify active transmission foci within a target population of 90,354. Automated AOI regions were also generated to identify response areas. Of the reported confirmed cases, 82.5% were successfully mapped at the household level, with 100% of remaining cases geo-referenced at a village level. By 2013, a total of 20,733 households, 55,711 structures and a population of 91,319 were recorded and mapped in the SDSS in four elimination provinces in Solomon Islands and Vanuatu. The framework has been used to guide both IRS and long-lasting insecticidal net (LLIN) distribution achieving an overall household coverage of 97.5% and 91.7% respectively. High-resolution surveillance-response applications are also ongoing. A high acceptability of the SDSS was recorded from stakeholder surveys and group discussions.
This thesis presents an SDSS-based approach to addressing scaled-up demands of elimination utilising modern geospatial tools and technology in remote and challenging settings. Geospatial systems developed to support Pacific Island progressive malaria elimination campaigns demonstrate the suitability of a SDSS-framework as a platform to rapidly collect, store and extract essential data throughout key phases of program implementation; effectively manage and ensure essential services are delivered at optimal levels of coverage in target areas; and actively locate and classify transmission to guide swift and appropriate responses. Findings presented in the thesis also highlight the importance of the integral role of malaria program personnel, and the need to transition from traditional styles of monitoring and evaluation to active surveillance-response using minimal essential data integrated using modern SDSS technology.
Keyword Malaria elimination
Spatial decision support systems (SDSS)
Geographic information systems (GIS)
Disease control

Document type: Thesis
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Created: Sat, 23 Nov 2013, 13:10:48 EST by Gerard Kelly on behalf of Scholarly Communication and Digitisation Service