Delimitation of kala-azar risk areas in the district of Vaishali in Bihar (India) using a geo-environmental approach

Bhunia, Gouri Sankar; Chatterjee, Nandini; Kumar, Vijay; Siddiqui, Niyamat Ali; Mandal, Rakesh; Das, Pradeep; Kesari, Shreekant

doi:10.1590/s0074-02762012000500007

Cited by 23 publications

(16 citation statements)

References 40 publications

(46 reference statements)

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“…Investigation suggested an association with inland water bodies of areas that poses a risk for VL by offering suitable breeding sites for P. argentipes. More recently, Bhunia et al [27] made signi�cant contribution by delimitating the probable endemic areas for kala-azar based on geoenvironmental risk model. From the satellite image taken during the dry season NDVI, tasseled cap transformation and land use/land cover were extracted and were used in the geoenvironmental risk model.…”

Section: Kala-azar Situation In Indiamentioning

confidence: 99%

“…Sudhakar et al [16] �rst used RS techniques to identify P. argentipes habitats in an endemic (Vaishali district) and nonendemic (Lohardaga district) areas of kalaazar. Employing satellite images, the study area was classi�ed [27] into several ecological zones based on land use/land cover characteristics and vegetation coverage. It was found that vegetation, water bodies, and settlements played a great role on sand �y abundance and distribution in the study area.…”

Section: �� and �I� �Or Identi��ation O�mentioning

confidence: 99%

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The Burden of Visceral Leishmaniasis in India: Challenges in Using Remote Sensing and GIS to Understand and Control

Bhunia

Kesari

Chatterjee

et al. 2013

ISRN Infectious Diseases

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Visceral leishmaniasis (VL) continues to constitute immense public health problems and be an obstacle to socioeconomic development in India. e scrutiny of this disease remains a necessary step in its control, eradication, and prevention. Space technologies proffer new opportunities for rapid appraisal of endemic areas, stipulation of trustworthy estimation of populations at risk, prediction of disease distributions in areas that lack baseline data and are difficult to access, and guiding intervention strategies. Here, we focus on the epidemiology and control of VL in India and review remote sensing (RS) and GIS applications to date. For the original evidenced report, a search was done on electronic databases of PubMed, ERMSS, EQUEST and from online Internet searching to come across the recent research problems. �e discuss the previous work conducted in this �eld, outlined potential new applications of GIS and RS techniques, and utilization of emerging satellite information, as they hold a promising area to further enhance VL risk mapping and prediction. Finally, we concluded with a discussion of strategies to overcome some of the perspective challenges of GIS and RS applications for VL control, so that supplementary and continued progress can be made to control this disease in India and elsewhere.

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Section: Kala-azar Situation In Indiamentioning

confidence: 99%

Section: �� and �I� �Or Identi��ation O�mentioning

confidence: 99%

The Burden of Visceral Leishmaniasis in India: Challenges in Using Remote Sensing and GIS to Understand and Control

Bhunia

Kesari

Chatterjee

et al. 2013

ISRN Infectious Diseases

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“…These validation studies (35-40) demonstrate the utility of geospatial risk models, like the model developed by Strawn et al (25), to accurately and prospectively predict pathogen prevalence. Additionally, these studies (37,39,40) used the output of their models to prioritize and identify risk management strategies, suggesting that geospatial models can also be integrated with on-farm food safety plans to develop targeted approaches to disease prevention. Thus, the purpose of this study was to (i) validate the…”

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confidence: 99%

“…This model consisted of a set of hierarchical rules based on, in order, the proximity of the fields to surface water, temperature, the proximity of fields to impervious cover, available water storage (AWS), and the proximity of fields to pasture (25). Studies in other disease systems (e.g., Lyme disease and West Nile virus) have not only developed (28)(29)(30)(31)(32)(33)(34) but have also validated (35)(36)(37)(38)(39)(40) geospatial predictive risk models. These validation studies (35)(36)(37)(38)(39)(40) demonstrate the utility of geospatial risk models, like the model developed by Strawn et al (25), to accurately and prospectively predict pathogen prevalence.…”

mentioning

confidence: 99%

“…Studies in other disease systems (e.g., Lyme disease and West Nile virus) have not only developed (28)(29)(30)(31)(32)(33)(34) but have also validated (35)(36)(37)(38)(39)(40) geospatial predictive risk models. These validation studies (35)(36)(37)(38)(39)(40) demonstrate the utility of geospatial risk models, like the model developed by Strawn et al (25), to accurately and prospectively predict pathogen prevalence. Additionally, these studies (37,39,40) used the output of their models to prioritize and identify risk management strategies, suggesting that geospatial models can also be integrated with on-farm food safety plans to develop targeted approaches to disease prevention.…”

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confidence: 99%

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Validation of a Previously Developed Geospatial Model That Predicts the Prevalence of Listeria monocytogenes in New York State Produce Fields

Weller

Shiwakoti

Bergholz

et al. 2016

Appl Environ Microbiol

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dTechnological advancements, particularly in the field of geographic information systems (GIS), have made it possible to predict the likelihood of foodborne pathogen contamination in produce production environments using geospatial models. Yet, few studies have examined the validity and robustness of such models. This study was performed to test and refine the rules associated with a previously developed geospatial model that predicts the prevalence of Listeria monocytogenes in produce farms in New York State (NYS). Produce fields for each of four enrolled produce farms were categorized into areas of high or low predicted L. monocytogenes prevalence using rules based on a field's available water storage (AWS) and its proximity to water, impervious cover, and pastures. Drag swabs (n ‫؍‬ 1,056) were collected from plots assigned to each risk category. Logistic regression, which tested the ability of each rule to accurately predict the prevalence of L. monocytogenes, validated the rules based on water and pasture. Samples collected near water (odds ratio [OR], 3.0) and pasture (OR, 2.9) showed a significantly increased likelihood of L. monocytogenes isolation compared to that for samples collected far from water and pasture. Generalized linear mixed models identified additional land cover factors associated with an increased likelihood of L. monocytogenes isolation, such as proximity to wetlands. These findings validated a subset of previously developed rules that predict L. monocytogenes prevalence in produce production environments. This suggests that GIS and geospatial models can be used to accurately predict L. monocytogenes prevalence on farms and can be used prospectively to minimize the risk of preharvest contamination of produce.F resh produce presents a unique food safety challenge due to the absence of a kill step between harvest and consumption. An increase in recalls and reported outbreaks linked to fresh produce over the past decade (1-3) have been associated with consumer avoidance of products linked to outbreaks (4, 5). This trend can negatively affect growers and the produce industry (4-6). For example, following a 2011 listeriosis outbreak in the United States associated with fresh cantaloupe (7), cantaloupe consumption dropped 53% nationwide (6). The prevention of produce contamination in production environments is therefore a concern for growers, the produce industry, and public health professionals. To develop effective prevention strategies, it is important to understand the ecological processes and environmental factors that affect foodborne pathogen prevalence in produce production environments. Technological advancements, such as geographic information systems (GIS), have the potential to drastically improve our ability to examine these processes and to develop novel tools for ensuring the safety of fresh produce.Numerous studies (8-21) have examined the ecology of foodborne pathogens in agricultural environments, and several (22-27) have used GIS and geospatial analysis. For example, Chapin...

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Introduction to Geoinformatics in Public Health

Bhunia

Shit²

2018

Geospatial Analysis of Public Health

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Delimitation of kala-azar risk areas in the district of Vaishali in Bihar (India) using a geo-environmental approach

Cited by 23 publications

References 40 publications

The Burden of Visceral Leishmaniasis in India: Challenges in Using Remote Sensing and GIS to Understand and Control

The Burden of Visceral Leishmaniasis in India: Challenges in Using Remote Sensing and GIS to Understand and Control

Validation of a Previously Developed Geospatial Model That Predicts the Prevalence of Listeria monocytogenes in New York State Produce Fields

Introduction to Geoinformatics in Public Health

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