Health-Based Geographic Information Systems for Mapping and Risk Modeling of Infectious Diseases and COVID-19 to Support Spatial Decision-Making

Huang, Xiao; Zhang, Renyi; Li, Xiao; Dadashova, Bahar; Zhu, Lingli; Zhang, Kai; Li, Yu; Shen, Bairong

doi:10.1007/978-981-16-8969-7_8

Cited by 4 publications

(7 citation statements)

References 73 publications

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“…Applying such an approach to VBD modelling offers actionable insights based on ranking alternatives and sensitivity analysis within targeted locations [ 23 ]. Integrating spatiotemporal disease modelling can help to identify and understand variations over time and space while considering environmental and sociodemographic factors [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

“…Such a GIS-AHP-based model can support local government efforts in assessing disease emergence risk, especially by identifying areas where the disease may be established due to climatic shifts. Furthermore, this approach can catalyse effective public health policy formulation, medication and vaccination distribution, business intelligence insights, and healthcare infrastructure planning [ 25 ]. It does so by illuminating patterns, demarcating high-risk zones, and identifying communities susceptible to infectious diseases [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, this approach can catalyse effective public health policy formulation, medication and vaccination distribution, business intelligence insights, and healthcare infrastructure planning [ 25 ]. It does so by illuminating patterns, demarcating high-risk zones, and identifying communities susceptible to infectious diseases [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

“…Applying such an approach to VBD modelling offers actionable insights based on ranking alternatives and sensitivity analysis within targeted locations [23]. Integrating spatiotemporal disease modelling can help to identify and understand variations over time and space while considering environmental and sociodemographic factors [25].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, this approach can catalyse effective public health policy formulation, medication and vaccination distribution, business intelligence insights, and healthcare infrastructure planning. It does so by illuminating patterns, demarcating high-risk zones, and identifying communities susceptible to infectious diseases [25].…”

Section: Introductionmentioning

confidence: 99%

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

Lima,

Cotton,

Marais

et al. 2023

Preprint

View full text Add to dashboard Cite

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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Geographical and temporal weighted regression: examining spatial variations of COVID-19 mortality pattern using mobility and multi-source data

Hu,

Zhang,

Duffield

et al. 2024

Comput.Urban Sci.

Self Cite

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The COVID-19 pandemic has had profound adverse effects on public health and society, with increased mobility contributing to the spread of the virus and vulnerable populations, such as those with pre-existing health conditions, at a higher risk of COVID-19 mortality. However, the specific spatial and temporal impacts of health conditions and mobility on COVID-19 mortality have yet to be fully understood. In this study, we utilized the Geographical and Temporal Weighted Regression (GTWR) model to assess the influence of mobility and health-related factors on COVID-19 mortality in the United States. The model examined several significant factors, including demographic and health-related factors, and was compared with the Multiscale Geographically Weighted Regression (MGWR) model to evaluate its performance. Our findings from the GTWR model reveal that human mobility and health conditions have a significant spatial impact on COVID-19 mortality. Additionally, our study identified different patterns in the association between COVID-19 and the explanatory variables, providing insights to policymakers for effective decision-making.

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Health-Based Geographic Information Systems for Mapping and Risk Modeling of Infectious Diseases and COVID-19 to Support Spatial Decision-Making

Cited by 4 publications

References 73 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.

Geographical and temporal weighted regression: examining spatial variations of COVID-19 mortality pattern using mobility and multi-source data

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