Leveraging big data for public health: Mapping malaria vector suitability in Malawi with Google Earth Engine

Frake, April N.; Peter, Brad G.; Walker, Edward D.; Messina, Joseph P.

doi:10.1371/journal.pone.0235697

Cited by 12 publications

(6 citation statements)

References 67 publications

(115 reference statements)

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“…To obtain the land cover change in Ouricuri and Tauá, images of the Landsat-8 OLI sensor were stored and processed on the Google Earth Engine platform (GEE). The GEE has a web programming interface and a collection of geospatial analytical tools, offering a model in spatial and temporal scales, which increases accessibility and guarantees users the possibility to access and perform procedures without limitations related to data storage capacity or computational processing 21 .…”

Section: Methodsmentioning

confidence: 99%

Spectral responses in rangelands and land cover change by livestock in regions of the Caatinga biome, Brazil

Morais

Cavalcante

Aquino³

et al. 2021

Sci Rep

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This study aimed to analyze fragments of rangelands through spectral responses and land cover change by livestock in regions of the Caatinga biome through remote sensing. For spectral behavior, the surface reflectance bidirectional (SRB) and spectral indexes of vegetation were used to verify the ragelands seasonality. Land cover change detection of Ouricuri and Tauá through Landsat-8 images with a 16-day revisit interval, were processed in the Google Earth Engine platform (GEE) and software Quantum GIS version 2.18 (QGIS). In the GEE platform, annual mosaics and stacking of the spectral bands were generated for the classification of images, and in sequence the production of thematic maps in QGIS. The analysis of land cover change considered the classes: thinned Caatinga, conserved Caatinga, herbaceous vegetation, bare soil, water and others. The analysis of the spectral responses showed that the vegetation monitored in Ouricuri presented higher SRB in the infrared band and lower SRB in the red and blue bands, and that caused the pasture to produce higher vegetation indexes than the other locations. Through validation, it was observed that in Tauá, there was an overall accuracy of 91% and Kappa index of 89%, and in Ouricuri there was an overall accuracy of 90% and Kappa index of 86%, indicating excellent correctness of the classification model. The classification model proved to be effective in verifying the temporal and spatial land cover change, making it possible to identify places with the vegetation that was most affected and susceptible to degradation and generation of political support to minimize damage to the Caatinga Biome.

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Section: Methodsmentioning

confidence: 99%

Spectral responses in rangelands and land cover change by livestock in regions of the Caatinga biome, Brazil

Morais

Cavalcante

Aquino³

et al. 2021

Sci Rep

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“…The Program to Calculate Deforestation in the Amazon (PRODES) dataset, which uses Landsat and other data sources to map annual deforestation in the Brazilian Legal Amazon, has been used to examine the effects of forest loss on malaria incidence in this region [37][38][39]. A 30-year global dataset of surface water was incorporated into a map of malaria vector suitability in Malawi [40], and a 14-year time series of Landsat-derived NDVI was used in a model of malaria cases at the health facility level in Zambia [41]. There is potential for much broader use of Landsat time series in malaria research.…”

Section: Trends In Parasitologymentioning

confidence: 99%

“…To date, the use of GEE for public health applications, and malaria in particular, has been limited [ 88 , 89 ]. However, recent studies have used GEE to map mosquito habitats in Malawi [ 40 ] and other countries in southern Africa [ 33 ]. Commercial computing service providers, such as Google Cloud and Amazon Web Services, have also provided access to extensive satellite data archives via their platforms.…”

Section: New Technology To Enhance Data Accessibility and Usabilitymentioning

confidence: 99%

Satellite Observations and Malaria: New Opportunities for Research and Applications

Wimberly

Beurs

Loboda

et al. 2021

Trends in Parasitology

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Satellite remote sensing provides a wealth of information about environmental factors that influence malaria transmission cycles and human populations at risk. Long-term observations facilitate analysis of climate-malaria relationships, and high-resolution data can be used to assess the effects of agriculture, urbanization, deforestation, and water management on malaria. New sources of very-high-resolution satellite imagery and synthetic aperture radar data will increase the precision and frequency of observations. Cloud computing platforms for remote sensing data combined with analysis-ready datasets and high-level data products have made satellite remote sensing more accessible to nonspecialists. Further collaboration between the malaria and remote sensing communities is needed to develop and implement useful geospatial data products that will support global efforts toward malaria control, elimination, and eradication. Satellite Observations and MalariaSince 2000, considerable progress has been made in reducing the global burden of malaria, shrinking the malaria map, and moving toward the goal of malaria eradication [1-3]. However, there is concern that declines in malaria cases and deaths have slowed [4]. Although the reasons for this slowdown are multifaceted, an important factor is the limited set of tools and approaches that are currently available for combating malaria. The recent Lancet Commission report on malaria eradication emphasized that new technologies, including innovations in the field of malaria informatics, are needed to facilitate more effective datadriven management of malaria interventions [5]. Geospatial data, including satellite observations, were highlighted as a key data source for monitoring human populations and their environments in support of malaria eradication. This recommendation is concordant with other assessments that have emphasized the importance of spatial decision support systems to enable national and subnational program management as well as regional and global strategic planning [6].The value of satellite observations for malaria research has long been recognized, with the earliest reviews appearing more than two decades ago [7-9]. Since then, considerable changes in global antimalaria efforts and advances in the field of remote sensing have taken place. Myriad connections between the environmental phenomena observed by satellite-borne sensors and different aspects of the malaria transmission cycle have been identified (Figure 1). However, the challenges of discovering, accessing, and processing relevant satellite data (Figure 2) still limit their use for malaria projects. The purpose of this review is to present an up-to-date assessment of satellite missions relevant to malaria and identify opportunities where new sources of remote sensing data can be leveraged to support novel applications. Major themes include long-term satellite records of environmental changes that affect malaria risk, new sources of satellite data with higher spatial resolution, measurement frequen...

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“…The GEE platform integrates multi-sensor satellite images, ready-to-use datasets, and various algorithms (e.g., image preprocessing, image composite-visual interpretation, feature extraction, traditional machine learning, and deep learning) [ 12 , 13 , 14 ]. The GEE has been used to identify the driving factors of malaria transmission and is proven to be useful to generate climate and environmental factors and match with spatio-temporal resolutions of epidemiological data [ 15 , 16 ]; however, it has not been used in dengue risk forecasting yet.…”

Section: Introductionmentioning

confidence: 99%

Improving Dengue Forecasts by Using Geospatial Big Data Analysis in Google Earth Engine and the Historical Dengue Information-Aided Long Short Term Memory Modeling

Gurgel

et al. 2022

Biology

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Timely and accurate forecasts of dengue cases are of great importance for guiding disease prevention strategies, but still face challenges from (1) time-effectiveness due to time-consuming satellite data downloading and processing, (2) weak spatial representation capability due to data dependence on administrative unit-based statistics or weather station-based observations, and (3) stagnant accuracy without the application of historical case information. Geospatial big data, cloud computing platforms (e.g., Google Earth Engine, GEE), and emerging deep learning algorithms (e.g., long short term memory, LSTM) provide new opportunities for advancing these efforts. Here, we focused on the dengue epidemics in the urban agglomeration of the Federal District of Brazil (FDB) during 2007–2019. A new framework was proposed using geospatial big data analysis in the Google Earth Engine (GEE) platform and long short term memory (LSTM) modeling for dengue case forecasts over an epidemiological week basis. We first defined a buffer zone around an impervious area as the main area of dengue transmission by considering the impervious area as a human-dominated area and used the maximum distance of the flight range of Aedes aegypti and Aedes albopictus as a buffer distance. Those zones were used as units for further attribution analyses of dengue epidemics by aggregating the pixel values into the zones. The near weekly composite of potential driving factors was generated in GEE using the epidemiological weeks during 2007–2019, from the relevant geospatial data with daily or sub-daily temporal resolution. A multi-step-ahead LSTM model was used, and the time-differenced natural log-transformed dengue cases were used as outcomes. Two modeling scenarios (with and without historical dengue cases) were set to examine the potential of historical information on dengue forecasts. The results indicate that the performance was better when historical dengue cases were used and the 5-weeks-ahead forecast had the best performance, and the peak of a large outbreak in 2019 was accurately forecasted. The proposed framework in this study suggests the potential of the GEE platform, the LSTM algorithm, as well as historical information for dengue risk forecasting, which can easily be extensively applied to other regions or globally for timely and practical dengue forecasts.

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Leveraging big data for public health: Mapping malaria vector suitability in Malawi with Google Earth Engine

Cited by 12 publications

References 67 publications

Spectral responses in rangelands and land cover change by livestock in regions of the Caatinga biome, Brazil

Spectral responses in rangelands and land cover change by livestock in regions of the Caatinga biome, Brazil

Satellite Observations and Malaria: New Opportunities for Research and Applications

Improving Dengue Forecasts by Using Geospatial Big Data Analysis in Google Earth Engine and the Historical Dengue Information-Aided Long Short Term Memory Modeling

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