Stratifying land use/land cover for spatial analysis of disease ecology and risk: an example using object-based classification techniques

Koch, David E.; Mohler, Rhett L.; Goodin, Douglas G.

doi:10.4081/gh.2007.251

Cited by 23 publications

(18 citation statements)

References 29 publications

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“…[3][4][5][6][7][8] Environmental changes in habitat could lead to an increase in virus transmission risk from infected rodents to humans. [4][5][6][7][8][9] Studies on HPS indicated that the emergence of hantavirus was associated with weather and climatic events like El Niño Southern Oscillation. 3,10,11 The prevalence of HFRS in China was determined by various environmental factors such as elevation, precipitation, temperature, vegetation, and soil types.…”

Section: Introductionmentioning

confidence: 99%

Using Geographic Information System-based Ecologic Niche Models to Forecast the Risk of Hantavirus Infection in Shandong Province, China

Wei¹,

Qian²,

Wang³

et al. 2011

The American Society of Tropical Medicine and Hygiene

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Abstract. Hemorrhagic fever with renal syndrome (HFRS) is an important public health problem in Shandong Province, China. In this study, we combined ecologic niche modeling with geographic information systems (GIS) and remote sensing techniques to identify the risk factors and affected areas of hantavirus infections in rodent hosts. Land cover and elevation were found to be closely associated with the presence of hantavirus-infected rodent hosts. The averaged area under the receiver operating characteristic curve was 0.864, implying good performance. The predicted risk maps based on the model were validated both by the hantavirus-infected rodents' distribution and HFRS human case localities with a good fit. These findings have the applications for targeting control and prevention efforts. Ecological niche models. The ENMs were developed to understand environmental variation associated with the distribution of infected rodent reservoirs. 21 A recent introduced presence-only distribution modeling technique-the Maximum Entropy approach, was applied in various domains and achieved high predictive accuracy, [22][23][24][25][26][27][28] and showed the best predictive power across all sample sizes. 3,25,[29][30][31] Detailed descriptions of the Maximum Entropy program (MAXENT, version 3.3.1) can be found in References 25 and 32. There were 33 sample sites positive for hantavirus infection in rodents from the study areas during [2005][2006][2007][2008], and 10,000 background points (2,500 for each year) are sampled by a spatially random method. The importance of EGVs contributing to the distribution of hantavirus infection was determined by three analyses. In the jackknife analysis of the average gain with training and test data, models were respectively created with each individual variable, all the remaining variables and all variables in turn. Next, corresponding results were compared. Second, the average values of area under the curve (AUC) of 10 iterations were compared. Third, the average percentage contribution of each variable was evaluated. In each iteration of the training algorithm, the increase or decrease in regularized gain was added or subtracted with the input of the corresponding variable, giving a heuristic estimate of variable contribution for the model. 25The final model predictors were selected using a stepwise fashion, as saturated models are likely to be oversized, overfitted, or redundant. 33,34 To determine variable significance, several models using the same occurrence data but different variable sets were examined. These included models with single predictors alone, as well as leaving out individual predictors from suites of variables. The loss in modeling performance for individual models were compared with the model generated using all predictors. The algorithm converges to the optimum probable distribution, and the gain is interpreted as representing how much better the distribution fits the sample points than a uniform distribution. 25,29,32 Model evaluation. To validate the accuracy and p...

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Section: Introductionmentioning

confidence: 99%

Using Geographic Information System-based Ecologic Niche Models to Forecast the Risk of Hantavirus Infection in Shandong Province, China

Wei¹,

Qian²,

Wang³

et al. 2011

The American Society of Tropical Medicine and Hygiene

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“…Promising results had already been published on Rift Valley fever in Kenya by Linthicum et al (1990) and the relationship of trypanosomiasis and its tsetse fly vectors to satellite imagery by Rodgers and Randolph (1991). Under the umbrella concept of "landscape epidemiology" initiated by Pavlovsky (1966), these early efforts were soon joined by other researchers (Galuzo, 1975;Kitron, 1998;Koch et al, 2007;Estrada-Peña, 2009;Anaruma Filho et al, 2010;Delgado-Petrocelli et al, 2011).…”

Section: Geospatial Technologies and Neglected Tropical Diseases (Ntds)mentioning

confidence: 99%

Mapping and modelling neglected tropical diseases and poverty in Latin America and the Caribbean

Malone

Bergquist²

2012

Geospat Health

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“…Landscapes are complex systems, displaying a dynamic interplay between structure (the spatial relationship among distinct elements or structural components of a system) and function (the productivity, nutrient cycling, animal movement and population dynamics of a system) [35,96,97]. In this landscape epidemiological view, disease occurrence can arise not just from the presence of a habitat, but from the underlying variations in these landscapes [54,98]. Land use/land cover maps have been used to examine the interplay between pattern and process across a range of environmental sciences.…”

Section: Review Of the Use Of Discrete Products In Public Health Resementioning

confidence: 99%

“…Commercially available software commonly used in geographic OBIA applications, such as eCognition (previously known as Definiens' eCognition) [22,30,41,50,51] were influenced in their early development by such analysis of images from cell-based assays, whole tissue slides and full body scans [52,53], and indeed the two arenas, medical OBIA and geographic or environmental OBIA, continue to develop side-by-side with few interdisciplinary publication outlets. While it is the classification accuracy benefits to OBIA that are most often cited in the public health remote sensing literature [54,55], it is the ability of OBIA to capture fundamental spatial content across scales, and their interactions, that can be critical to understanding a system. This type of spatial content could be more frequently used in public health applications to understand complex systems and their interrelations.…”

Section: Introductionmentioning

confidence: 99%

Terrestrial Remotely Sensed Imagery in Support of Public Health: New Avenues of Research Using Object-Based Image Analysis

et al. 2011

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Abstract:The benefits of terrestrial remote sensing in the environmental sciences are clear across a range of applications, and increasingly remote sensing analyses are being integrated into public health research. This integration has largely been in two areas: first, through the inclusion of continuous remote sensing products such as normalized difference vegetation index (NDVI) or moisture indices to answer large-area questions associated with the epidemiology of vector-borne diseases or other health exposures; and second, through image classification to map discrete landscape patches that provide habitat to disease-vectors or that promote poor health. In this second arena, new improvements in object-based image analysis (or "OBIA") can provide advantages for public health research. Rather than classifying each pixel based on its spectral content alone, the OBIA approach first segments an image into objects, or segments, based on spatially connected pixels with similar spectral properties, and then these objects are classified based on their spectral, spatial and contextual attributes as well as by their interrelations across scales. The approach can lead to increases in classification accuracy, and it can also develop multi-scale topologies between objects that can be utilized to help understand human-disease-health systems. This paper provides a brief review of what has been done in the public health literature with continuous and discrete mapping, and then highlights the key concepts in OBIA that could be more of use to public health researchers interested in integrating remote sensing into their work.

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Stratifying land use/land cover for spatial analysis of disease ecology and risk: an example using object-based classification techniques

Cited by 23 publications

References 29 publications

Using Geographic Information System-based Ecologic Niche Models to Forecast the Risk of Hantavirus Infection in Shandong Province, China

Using Geographic Information System-based Ecologic Niche Models to Forecast the Risk of Hantavirus Infection in Shandong Province, China

Mapping and modelling neglected tropical diseases and poverty in Latin America and the Caribbean

Terrestrial Remotely Sensed Imagery in Support of Public Health: New Avenues of Research Using Object-Based Image Analysis

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