AIMC Topic: Spatial Analysis

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Forecasting dengue across Brazil with LSTM neural networks and SHAP-driven lagged climate and spatial effects.

BMC public health
BACKGROUND: Dengue fever is a mosquito-borne viral disease that poses significant health risks and socioeconomic challenges in Brazil, necessitating accurate forecasting across its 27 federal states. With the country's diverse climate and geographica...

Spatial analysis of air pollutant exposure and its association with metabolic diseases using machine learning.

BMC public health
BACKGROUND: Metabolic diseases (MDs), exemplified by diabetes, hypertension, and dyslipidemia, have become increasingly prevalent with rising living standards, posing significant public health challenges. The MDs are influenced by a complex interplay...

Uncovering soil heavy metal pollution hotspots and influencing mechanisms through machine learning and spatial analysis.

Environmental pollution (Barking, Essex : 1987)
Soil heavy metal (HM) pollution is a significant and widespread environmental issue in China, highlighting the need to quantify influencing factors and identify priority concern areas for effective prevention and management. Based on published litera...

Spatial heterogeneity effect of built environment on traffic safety using geographically weighted atrous convolutions neural network.

Accident; analysis and prevention
The built environment exerts a significant influence on the frequency and severity of traffic accidents. Spatially uniform assumptions on the impacts of built environment factors commonly employed in existing research may lead to inconsistent and con...

Multimodal multiview bilinear graph convolutional network for mild cognitive impairment diagnosis.

Biomedical physics & engineering express
Mild cognitive impairment (MCI) is a significant predictor of the early progression of Alzheimer's disease (AD) and can serve as an important indicator of disease progression. However, many existing methods focus mainly on the image when processing b...

Machine learning approach and geospatial analysis to determine HIV infection, awareness status, and transmission knowledge among adults in Sub-Saharan Africa.

BMC research notes
BACKGROUND: HIV/AIDS remains a major public health challenge, in Sub-Saharan Africa (SSA). In 2020, 16% of people living with HIV did not know their HIV status in SSA. Understanding the geospatial distribution of HIV infection, awareness status, and ...

Spatial prediction of human brucellosis susceptibility using an explainable optimized adaptive neuro fuzzy inference system.

Acta tropica
Brucellosis, a zoonotic disease caused by Brucella bacteria, poses significant risks to human, livestock, and wildlife health, alongside economic losses from livestock morbidity and mortality. This study improves Human Brucellosis Susceptibility Mapp...

Analysis and prediction of infectious diseases based on spatial visualization and machine learning.

Scientific reports
Infectious diseases are a global public health problem that poses a threat to human society. Since the 1970s, constantly mutated new infectious viruses have been quietly attacking humanity, and at least one new type of infectious disease is discovere...

Enhancing indoor PM predictions based on land use and indoor environmental factors by applying machine learning and spatial modeling approaches.

Environmental pollution (Barking, Essex : 1987)
The presence of fine particulate matter (PM) indoors constitutes a significant component of overall PM exposure, as individuals spend 90% of their time indoors; however, personal monitoring for large cohorts is often impractical. In light of this, th...