Do remotely sensed time-series derived indicators of environmental change contribute to urban malaria modeling

Authors: Daniel Casey*, , Benoit Parmentier, National Socio-Environmental Synthesis Center, Marco Millones, University of Mary Washington, Albert Decatur, Mitchell Center for Sustainability Solutions, University of Maine, Brian McGill, Mitchell Center for Sustainability Solutions, University of Maine, Oscar Brousse, Geography and Tourism department, Katholieke Universiteit Leuven, Belgium, Sebastien Dujardin, Département Géographie, Université de Namur, Belgium, Stefanos Georganos, Département Géosciences, Environnement et Société, Université Libre de Bruxelles, Belgium, Matthias Demuzere, Geography and Tourism department, Katholieke Universiteit Leuven, Belgium, Hendrik Wouters, Geography and Tourism department, Katholieke Universiteit Leuven, Belgium, Sabine Vanhuysse, Département Géosciences, Environnement et Société, Université Libre de Bruxelles, Belgium, Catherine Linard, Département Géographie, Université de Namur, Belgium, Jonas Van de Walle, Geography and Tourism department, Katholieke Universiteit Leuven, Belgium, Eleonore Wolff, Département Géosciences, Environnement et Société, Université Libre de Bruxelles, Belgium, Nicole Van Lipzig, Geography and Tourism department, Katholieke Universiteit Leuven, Belgium
Topics: Medical and Health Geography, Geography and Urban Health, Spatial Analysis & Modeling
Keywords: Malaria, geospatial, remote sensing, change
Session Type: Paper
Day: 4/4/2019
Start / End Time: 8:00 AM / 9:40 AM
Room: Tyler, Marriott, Mezzanine Level
Presentation File: No File Uploaded


Despite marked declines in estimated disease burden since 2000, malaria is still the third leading cause of death in Sub-Saharan Africa, killing over 600,000 people in 2016. While recent methodological advances have allowed researchers to describe the spatial and temporal patterns of disease burden due to malaria with increasingly novel detail, these studies often ignore or otherwise smooth over likely intraurban and interurban gradients of malarial disease burden. Understanding patterns in urban malaria epidemiology is especially important as urban populations in Sub-Saharan are projected to increase substantially in the next few decades. The Remote Sensing for Epidemiology in Sub-Saharan Africa Cities (REACT) project is a multidisciplinary effort to combine remotely sensed variables at a variety of spatial and temporal scales to understand intraurban and interurban malaria epidemiology in Sub-Saharan African cities. The present study utilizes moderate spatial resolution and high temporal resolution satellite imagery to elucidate how changes to the urban environment affect the spatiotemporal patterns of malarial parasite prevalence within and between African cities. Specifically, we model abrupt and long-term urban environment change using the Space Beats Time (SBT) and Seasonal Trends Analysis (STA) methods, respectively and then use these parameters to as variables in a geostatistical model of malarial parasite prevalence.

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