Supporting local health decision making with spatial video: Dengue, Chikungunya and Zika risks in a data poor, informal community in Nicaragua

Curtis, Andrew; Quinn, Megan; Obenauer, Julie; Renk, Becca Mohally

doi:10.1016/j.apgeog.2017.08.008

Cited by 16 publications

(7 citation statements)

References 58 publications

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“…Then, each located video frame became a digitizing source, allowing the team to map “risk” features into Google Earth or as a Geographic Information System (GIS) layer. These risk factors include standing water, mud, trash, and human activity [ 31 , 32 , 33 , 34 ]. After the initial SV visit, a map was created for each water point depicting its immediate environmental setting including its proximity to other features such as drainage channels.…”

Section: Methodsmentioning

confidence: 99%

“…This was achieved using an environmental assessment index created with scores ranging from one (least severe risk) to ten (most severe risk) for standing water, mud, trash, and the amount of human activity within a ten meter buffer of the test site. These environmental assessments follow similar methods applied in Haiti, Nicaragua, and Tanzania [ 30 , 31 , 32 , 33 ]. This type of mapping of micro-environmental characteristics has been successfully used to track and predict the distribution of both waterborne and water-vectored diseases [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Micro-Space Complexity and Context in the Space-Time Variation in Enteric Disease Risk for Three Informal Settlements of Port au Prince, Haiti

Curtis

Squires

Rouzier

et al. 2019

IJERPH

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Diffusion of cholera and other diarrheal diseases in an informal settlement is a product of multiple behavioral, environmental and spatial risk factors. One of the most important components is the spatial interconnections among water points, drainage ditches, toilets and the intervening environment. This risk is also longitudinal and variable as water points fluctuate in relation to bacterial contamination. In this paper we consider part of this micro space complexity for three informal settlements in Port au Prince, Haiti. We expand on more typical epidemiological analysis of fecal coliforms at water points, drainage ditches and ocean sites by considering the importance of single point location fluctuation coupled with recording micro-space environmental conditions around each sample site. Results show that spatial variation in enteric disease risk occurs within neighborhoods, and that while certain trends are evident, the degree of individual site fluctuation should question the utility of both cross-sectional and more aggregate analysis. Various factors increase the counts of fecal coliform present, including the type of water point, how water was stored at that water point, and the proximity of the water point to local drainage. Some locations fluctuated considerably between being safe and unsafe on a monthly basis. Next steps to form a more comprehensive contextualized understanding of enteric disease risk in these environments should include the addition of behavioral factors and local insight.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Micro-Space Complexity and Context in the Space-Time Variation in Enteric Disease Risk for Three Informal Settlements of Port au Prince, Haiti

Curtis

Squires

Rouzier

et al. 2019

IJERPH

Self Cite

View full text Add to dashboard Cite

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“…Issues regarding inter-rater reliability, the effort required to turn SV into usable maps, and the ethics and privacy issues regarding spatial video use have been covered in several prior publications [23,43,44].…”

Section: Discussionmentioning

confidence: 99%

“…SV provides one solution for mapping IS environments [1,21,22] as the output allows the user to know exactly where each frame was captured. In this way, features such as drainage channels, latrines, standing water, mud, trash, animals, and human activity can be mapped, all of which can have implications in terms of local disease risk [23]. SV can also provide detail on the spaces between buildings, along the ground where children play, and the quality of the environment around each water point or toilet.…”

Section: Introductionmentioning

confidence: 99%

Spatial Video Health Risk Mapping in Informal Settlements: Correcting GPS Error

Curtis

Bempah

Ajayakumar

et al. 2018

IJERPH

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Informal settlements pose a continuing health concern. While spatial methodologies have proven to be valuable tools to support health interventions, several factors limit their widespread use in these challenging environments. One such technology, spatial video, has been used for fine-scale contextualized mapping. In this paper, we address one of the limitations of the technique: the global positioning system (GPS) coordinate error. More specifically, we show how spatial video coordinate streams can be corrected and synced back to the original video to facilitate risk mapping. Past spatial video collections for the Mathare informal settlement of Kenya are used as an illustration as these data had been previously discarded because of excessive GPS error. This paper will describe the bespoke software that makes these corrections possible, and then will go on to investigate patterns in the coordinate error.

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“…One such previously employed technological advance used to map health risks, and to provide micro geographic support for more traditional epidemiological surveillance in these environments, is the spatial video (SV) [ 11 , 12 ]. This field technology consists of a global positioning system (GPS) enhanced video, which for informal settlements is usually hand carried through the study space [ 43 ].…”

Section: Introductionmentioning

confidence: 99%

Exploring convolutional neural networks and spatial video for on-the-ground mapping in informal settlements

et al. 2021

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Background The health burden in developing world informal settlements often coincides with a lack of spatial data that could be used to guide intervention strategies. Spatial video (SV) has proven to be a useful tool to collect environmental and social data at a granular scale, though the effort required to turn these spatially encoded video frames into maps limits sustainability and scalability. In this paper we explore the use of convolution neural networks (CNN) to solve this problem by automatically identifying disease related environmental risks in a series of SV collected from Haiti. Our objective is to determine the potential of machine learning in health risk mapping for these environments by assessing the challenges faced in adequately training the required classification models. Results We show that SV can be a suitable source for automatically identifying and extracting health risk features using machine learning. While well-defined objects such as drains, buckets, tires and animals can be efficiently classified, more amorphous masses such as trash or standing water are difficult to classify. Our results further show that variations in the number of image frames selected, the image resolution, and combinations of these can be used to improve the overall model performance. Conclusion Machine learning in combination with spatial video can be used to automatically identify environmental risks associated with common health problems in informal settlements, though there are likely to be variations in the type of data needed for training based on location. Success based on the risk type being identified are also likely to vary geographically. However, we are confident in identifying a series of best practices for data collection, model training and performance in these settings. We also discuss the next step of testing these findings in other environments, and how adding in the simultaneously collected geographic data could be used to create an automatic health risk mapping tool.

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Supporting local health decision making with spatial video: Dengue, Chikungunya and Zika risks in a data poor, informal community in Nicaragua

Cited by 16 publications

References 58 publications

Micro-Space Complexity and Context in the Space-Time Variation in Enteric Disease Risk for Three Informal Settlements of Port au Prince, Haiti

Micro-Space Complexity and Context in the Space-Time Variation in Enteric Disease Risk for Three Informal Settlements of Port au Prince, Haiti

Spatial Video Health Risk Mapping in Informal Settlements: Correcting GPS Error

Exploring convolutional neural networks and spatial video for on-the-ground mapping in informal settlements

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