Prevention of vector-borne disease by the identification and risk assessment of mosquito vector habitats using GIS and remote sensing: a case study of Gorakhpur, India

Kumar, Shiv Datt; Agrawal, Sonam

doi:10.1007/s41204-020-00084-y

Cited by 8 publications

(5 citation statements)

References 61 publications

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“…The synergistic approach of integrating GIS with an AHP analysis has demonstrated substantial efficacy in forecasting disease risk and demarcating high-risk areas [ 29 – 35 ]. Furthermore, GIS serves as a precise tool in risk mapping, capable of pinpointing risk areas at a micro level, hence proving indispensable in identifying vulnerable localities [ 53 , 54 ].…”

Section: Discussionmentioning

confidence: 99%

Modelling the risk of Japanese encephalitis virus in Victoria, Australia, using an expert-systems approach

Flores Lima,

Cotton,

Marais

et al. 2024

BMC Infect Dis

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Predictive models for vector-borne diseases (VBDs) are instrumental to understanding the potential geographic spread of VBDs and therefore serve as useful tools for public health decision-making. However, predicting the emergence of VBDs at the micro-, local, and regional levels presents challenges, as the importance of risk factors can vary spatially and temporally depending on climatic factors and vector and host abundance and preferences. We propose an expert-systems-based approach that uses an analytical hierarchy process (AHP) deployed within a geographic information system (GIS), to predict areas susceptible to the risk of Japanese encephalitis virus (JEV) emergence. This modelling approach produces risk maps, identifying micro-level risk areas with the potential for disease emergence. The results revealed that climatic conditions, especially the minimum temperature and precipitation required for JEV transmission, contributed to high-risk conditions developed during January and March of 2022 in Victora. Compared to historical climate records, the risk of JEV emergence was increased in most parts of the state due to climate. Importantly, the model accurately predicted 7 out of the 8 local government areas that reported JEV-positive cases during the outbreak of 2022 in Victorian piggeries. This underscores the model’s potential as a reliable tool for supporting local risk assessments in the face of evolving climate change.

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Section: Discussionmentioning

confidence: 99%

Modelling the risk of Japanese encephalitis virus in Victoria, Australia, using an expert-systems approach

Flores Lima,

Cotton,

Marais

et al. 2024

BMC Infect Dis

View full text Add to dashboard Cite

show abstract

“…The synergistic approach of integrating GIS with an AHP analysis has demonstrated substantial e cacy in forecasting disease risk and demarcating high-risk areas [27]- [33]. Furthermore, GIS serves as a precise tool in risk mapping, capable of pinpointing risk areas at a micro level, hence proving indispensable in identifying vulnerable localities [50], [51].…”

Section: Discussionmentioning

confidence: 99%

Modelling the risk of Japanese encephalitis virus in Victoria, Australia, using an expert-systems approach.

Lima,

Cotton,

Marais

et al. 2023

Preprint

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Predictive models for vector-borne diseases (VBDs) are instrumental to understanding the potential geographic spread of VBDs and therefore serve as useful tools for public health decision-making. However, predicting the emergence of VBDs at the micro-, local, and regional levels presents challenges, as the importance of risk factors can vary depending on climatic factors and vector and host preferences. We propose an expert-systems-based approach that uses an analytical hierarchy process (AHP) deployed within a geographic information system (GIS), to predict areas susceptible to the risk of Japanese encephalitis virus (JEV) emergence. This modelling approach produces risk maps, identifying micro-level risk areas with the potential for disease emergence. The results revealed that climatic conditions, especially the minimum temperature and precipitation required for JEV transmission, contributed to high-risk conditions developed during January and March of 2022 in Victora. Compared to historical climate records, the risk of JEV emergence was increased in most parts of the state due to climate. Importantly, the model accurately predicted 7 out of the 8 local government areas that reported JEV-positive cases during the outbreak of 2022 in Victorian piggeries. This underscores the model’s potential as a reliable tool for supporting local risk assessments in the face of evolving climate change.

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“…https://www.indjst.org/ The government and the local community need to work together to develop a sustainable land use plan for the region. This plan should aim to protect the environment, meet the needs of the growing population, and promote economic development (17)(18)(19)(20) .…”

Section: Study Areamentioning

confidence: 99%

Application of Remote Sensing and GIS Techniques for Identification of Changes in Land Use and Land Cover (LULC): A Case Study

Gupta,

Singh,

Gupta

et al. 2023

IJST

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Objective: This study investigates the Spatial temporal changes of LULC in Mathura district of Uttar Pradesh, India, over the past 30 years (1990 to 2020). The study uses Landsat-5, Landsat-7 as well as Landsat-8 OLI images and GIS technique. Methods: The images are classified into LULC classes using a random forest classifier. The LULC changes are analyzed using a change detection analysis. The results of the change detection analysis were visualized using GIS. Confusion matrix is used for comparing the Crop classification during 1990-2020. User accuracy, consumer accuracy, producer accuracy, overall accuracy and kappa coefficient are used to compare the study of approximately three decades from 1990 to 2020. Findings: The classification of LULC is done into six classes i.e., urban, water bodies, vegetation, wheat, mustard and other crops. The results show that there are significant changes in LULC over the past 30 years. In the year 1990, 70.1 % accuracy and kappa 61.61 are obtained. In the year 2000, 70.8 % accuracy and kappa 63.02 are obtained. In the year 2010, 71.3% accuracy and kappa 64.8 is obtained. In the year 2020, 84% accuracy and kappa 80 are obtained. Novelty: In traditional mapping is not feasible, multi temporal satellite imagery can offer the crucial measurement of spatial and temporal phenomena for the research area. The study shows that the predominant land use in the area under investigation is vegetation. Less agricultural activity occurred between 2000 and 2010, which increased the amount of land covered by vegetation. The importance of monitoring LULC changes in order to understand the effects of these variations on the society as well as environment.

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Prevention of vector-borne disease by the identification and risk assessment of mosquito vector habitats using GIS and remote sensing: a case study of Gorakhpur, India

Cited by 8 publications

References 61 publications

Modelling the risk of Japanese encephalitis virus in Victoria, Australia, using an expert-systems approach

Modelling the risk of Japanese encephalitis virus in Victoria, Australia, using an expert-systems approach

Modelling the risk of Japanese encephalitis virus in Victoria, Australia, using an expert-systems approach.

Application of Remote Sensing and GIS Techniques for Identification of Changes in Land Use and Land Cover (LULC): A Case Study

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