Climate Change and Its Impact on the Outbreak of Vector-Borne Diseases

Pandey, Vanya; Ranjan, Manju Rawat; Tripathi, Ashutosh

doi:10.1007/978-3-030-76116-5_13

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…The primary challenge with environmental data is its variability and the complex interplay between multiple environmental factors. Moreover, global climate change introduces new uncertainties, making it difficult to predict long-term trends accurately (Pandey, Ranjan, & Tripathi, 2021;Peters et al, 2020). reinforce the significance of environmental factors in public health, detailing how global challenges like climate change compound the difficulty of predicting disease outbreaks.…”

Section: Environmental Datamentioning

confidence: 99%

Predictive Modeling for Disease Outbreaks: A Review of Data Sources and Accuracy

Scholastica Ijeh,

Chioma Anthonia Okolo,

Jeremiah Olawumi Arowoogun

et al. 2024

imsrj

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This review explores the dynamic field of predictive modelling for disease outbreaks, focusing on the data sources, modelling techniques, accuracy, challenges, and future directions integral to its advancement. It underscores the significance of diverse data sources, including epidemiological, environmental, social media, and mobility data. It also discusses various modelling approaches, from statistical models to advanced machine learning algorithms and network analysis. The review highlights the critical role of accuracy and validation in predictive models, alongside the challenges posed by data quality, model complexity, and the ethical use of personal data. It outlines promising research avenues, such as improving data collection methods, integrating novel data sources like genomic data, and leveraging emerging technologies such as AI and IoT to enhance predictive capabilities. This comprehensive overview emphasizes the importance of predictive modelling in informing public health decisions and the continuous need for innovation and collaboration to address the complex dynamics of disease outbreaks. Keywords: Predictive Modeling, Disease Outbreaks, Data Sources, Machine Learning, Public Health, Emerging Technologies.

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Section: Environmental Datamentioning

confidence: 99%

Predictive Modeling for Disease Outbreaks: A Review of Data Sources and Accuracy

Scholastica Ijeh,

Chioma Anthonia Okolo,

Jeremiah Olawumi Arowoogun

et al. 2024

imsrj

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“…Human health is affected by various environmental factors, especially in places of residence, so that it should be considered that most health issues have a spatial dimension [ 15 ]. In addition, some research has shown the influence of climate change on vector populations and, consequently, on the incidence of vector borne diseases, such as malaria, chikungunya, dengue, yellow fever, and zika [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

A Geomedical Survey: Is There an Association Between Climatic Conditions and Leishmania Species Distribution in Iran During the Years 1999–2021?

Navi,

Salahi-Moghaddam,

Habibi-Nokhandan

et al. 2024

Acta Parasit.

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Purpose Iran is among the high-risk leishmaniasis regions in the world. WHO recommends the use of GIS as an ideal tool for healthcare authorities to predict the evolution of a disease, delimit the risk of outbreaks and identify critical areas. The aim of this research is to find the association between the main species of Leishmania (L. major, L. tropica, L. infantum) dispersion and climatic variables in Iran. Methods All molecular-based reports of leishmaniasis from Iran between 1999 and 2021 were gathered from reliable medical sources. Meteorological data (air and soil temperatures, annual rainfall and humidity) of the country along the study period were obtained from the Iranian Climatological Research Centre. The data concerning species distribution and climatic conditions during this period were moved to a base-map through raster layers using ArcGIS 10.4.1 software. The relationship between parasitological and climatic models was examined using ANOVA. Results High risk area maps, based on the cut-off thresholds, were generated for Leishmania major, L. tropica and L. infantum. According to the molecular-based reports, the L. major distribution was significantly related to all climatic variables, while L. tropica was merely related to rainfall and humidity, and the L. infantum distribution was significantly associated with rainfall, soil and air temperatures. Conclusion The association between climatic conditions and Leishmania species distribution in Iran has been confirmed. Consequently, both, the relationship between climatic conditions and the geographical distribution of Leishmania species, and the use of GIS to better understand the spatial epidemiology of leishmaniasis, have been reaffirmed.

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Green Clustering Analyzing Logistics Performance and Carbon Emissions with K -Means and Gaussian Mixture Models

Chomjinda,

Piladaeng,

Kulthon

et al. 2024

2024 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC)

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Climate Change and Its Impact on the Outbreak of Vector-Borne Diseases

Cited by 6 publications

References 11 publications

Predictive Modeling for Disease Outbreaks: A Review of Data Sources and Accuracy

Predictive Modeling for Disease Outbreaks: A Review of Data Sources and Accuracy

A Geomedical Survey: Is There an Association Between Climatic Conditions and Leishmania Species Distribution in Iran During the Years 1999–2021?

Green Clustering Analyzing Logistics Performance and Carbon Emissions with K -Means and Gaussian Mixture Models

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