Disease prevention versus data privacy: using landcover maps to inform spatial epidemic models. by Michael J Tildesley, Sadie J Ryan

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A Combined Model for Simulating the Spatial Dynamics of Epidemic Spread: Integrating Stochastic Compartmentalization and Cellular Automata Approach by Murad Bashabsheh

“…The model presented in this paper is designed to simulate the spatial distribution of diseases in a spatially structured population. …”
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A Hybrid Spatial–Temporal Deep Learning Method for Metro Tunnel Displacement Prediction Under “Dual Carbon” Background by Jianyong Chai, Limin Jia, Jianfeng Liu, Enguang Hou, Zhe Chen

“…The model leverages the strengths of GCNs in capturing spatial correlations and LSTM networks in processing temporal dynamics, offering a robust framework for accurate displacement prediction. …”
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A novel hybrid machine learning approach for δ13C spatial prediction in polish hard-water lakes by Himan Shahabi, Ataollah Shirzadi, Alicja Ustrzycka, Natalia Piotrowska, Janusz Filipiak, Marzieh Hajizadeh Tahan

“…For the first time, this model is used to predict the spatial prediction of a stable isotope in Polish lakes. …”
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Value of MRI radiomics based on intratumoral and peritumoral heterogeneity in predicting spatial patterns of locally recurrent high-grade gliomas by WANG Hanwei, ZENG Linlan, ZHAO Mimi

“… ‍Objective‍　‍To establish and validate a multimodal MRI radiomics model based on intratumoral and peritumoral heterogeneity for prediction of spatial pattern of locally recurrent high-grade gliomas (HGGs). …”
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Spatial-Temporal Distribution Prediction of Charging Load of Electric Truck Cluster Considering Battery Loss Under Path Planning by CUI Jinghao, ZHANG Yi, ZHANG Zhichao, YU Yang

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Research on Spatial Heterogeneity, Impact Mechanism, and Carbon Peak Prediction of Carbon Emissions in the Yangtze River Delta Urban Agglomeration by Pin Chen, Xiyue Wang, Zexia Yang, Changfeng Shi

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Spatial Prediction of High-Risk Areas for Asthma in Metropolitan Areas: A Machine Learning Approach Applied to Tehran, Iran by Alireza Mohammadi, Elahe Pishgar, Juan Aguilera

“…Three ensemble machine learning algorithms—Random Forest (RF), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost)—were applied to model and predict asthma risk. A Negative Binomial Regression Model (NBRM) identified seven key predictors: population density, unemployment rate, particulate matter (PM_2.5 and PM₁₀), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), neighborhood deprivation index, and road intersection density. …”
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A New Prediction Model of Dam Deformation and Successful Application by Shuangping Li, Bin Zhang, Meng Yang, Senlin Li, Zuqiang Liu

“…In most dam deformation monitoring practices, some single-point models do not consider the spatial correlation, and the traditional regression models do not consider the nonlinear relationship between the environmental quantity and the deformation quantity, resulting in poor prediction accuracy. …”
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Connectome-based prediction of functional impairment in experimental stroke models. by Oliver Schmitt, Peter Eipert, Yonggang Wang, Atsushi Kanoke, Gratianne Rabiller, Jialing Liu

“…Dynamic modeling with the weighted bilateral connectome detected changes in signal propagation in the remote hippocampus in all 3 stroke types, predicting the extent of hippocampal hypoactivation and impairment in spatial learning and memory function. …”
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Ada-GCNLSTM: An adaptive urban crime spatiotemporal prediction model by Miaoxuan Shan, Chunlin Ye, Peng Chen, Shufan Peng

“…In this paper, we introduce a novel deep learning-based model, adaptive-GCNLSTM (Ada-GCNLSTM). Specifically, in the spatial feature extraction module, we enhance the model's ability to capture crime spatial distributions by leveraging graph convolutional networks to model spatial dependencies in conjunction with the maximum mean discrepancy to extract the universal features of crime data. …”
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A population spatialization method based on the integration of feature selection and an improved random forest model. by Zhen Zhao, Hongmei Guo, Xueli Jiang, Ying Zhang, Changjiang Lu, Can Zhang, Zonghang He

“…The random forest (RF) model is widely used in population spatialization studies. …”
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The effect of heterogeneity on invasion in spatial epidemics: from theory to experimental evidence in a model system. by Franco M Neri, Anne Bates, Winnie S Füchtbauer, Francisco J Pérez-Reche, Sergei N Taraskin, Wilfred Otten, Douglas J Bailey, Christopher A Gilligan

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EpiGeoPop: a tool for developing spatially accurate country-level epidemiological models by Lara Herriott, Henriette L. Capel, Isaac Ellmen, Nathan Schofield, Jiayuan Zhu, Ben Lambert, David Gavaghan, Ioana Bouros, Richard Creswell, Kit Gallagher

“…Agent-based models (ABMs) have emerged as a valuable tool, capturing population heterogeneity and spatial effects, particularly when assessing potential intervention strategies. …”
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Spatial modelling of soil-transmitted helminth infections in Kenya: a disease control planning tool. by Rachel L Pullan, Peter W Gething, Jennifer L Smith, Charles S Mwandawiro, Hugh J W Sturrock, Caroline W Gitonga, Simon I Hay, Simon Brooker

“…The model estimated that a total of 2.8 million school-age children live in districts which warrant mass treatment.…”
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Lightweight pose estimation spatial-temporal enhanced graph convolutional model for miner behavior recognition by WANG Jianfang, DUAN Siyuan, PAN Hongguang, JING Ningbo

“…MEST-GCN improved upon the spatial-temporal graph convolutional network (ST-GCN) by removing redundant layers to simplify the model structure and reduce the number of parameters. …”
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Predictive Deep Learning for High‐Dimensional Inverse Modeling of Hydraulic Tomography in Gaussian and Non‐Gaussian Fields by Quan Guo, Ming Liu, Jian Luo

“…In this work, we develop a novel method called HT‐INV‐NN, which combines dimensionality reduction techniques with a predictive deep learning (DL) model to estimate high‐dimensional Gaussian and non‐Gaussian channel fields. …”
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Liner Wear Prediction Using Bayesian Regression Models and Clustering by Jacob Van Den Broek, Melinda Hodkiewicz, Adriano Polpo

“…Notably, Model 2 predicts remaining useful life within 95% credible intervals and identifies anomalous sensor performance. …”
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Building Fire Location Predictions Based on FDS and Hybrid Modelling by Yanxi Cao, Hongyan Ma, Shun Wang, Yingda Zhang

“…With the goal of addressing the difficulty of rapidly identifying the source of fire in commercial buildings, this study builds a numerical fire model based on the fire dynamics simulator (FDS) and combines it with a hybrid model to predict the location of a fire source. …”
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Evaluation and Optimization of Prediction Models for Crop Yield in Plant Factory by Yaoqi Peng, Yudong Zheng, Zengwei Zheng, Yong He

“…By incorporating crop yield data, a comparative analysis of 28 prediction models was performed, assessing performance metrics such as MSE, RMSE, MAE, MAPE, R², prediction speed, training time, and model size. …”
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