Predicting the effect of landscape structure on epidemic invasion using an analytical estimate for infection rate by Yevhen F. Suprunenko, Stephen J. Cornell, Christopher A. Gilligan

“…We explore the potential of using an analytical approximation for the rate, [Formula: see text], at which susceptible crop fields become infected at the start of an epidemic to predict the effect that the spatial structure of a host landscape will have on an epidemic. …”
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Modeling Spatial Data with Heteroscedasticity Using PLVCSAR Model: A Bayesian Quantile Regression Approach by Rongshang Chen, Zhiyong Chen

“…We apply a Bayesian quantile regression (BQR) of the partially linear varying coefficient spatial autoregressive (PLVCSAR) model for spatial data to improve the prediction of performance. …”
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Disease prediction models and operational readiness. by Courtney D Corley, Laura L Pullum, David M Hartley, Corey Benedum, Christine Noonan, Peter M Rabinowitz, Mary J Lancaster

“…As a result, we systematically reviewed 44 papers, and the results are presented in this analysis. We identified 44 models, classified as one or more of the following: event prediction (4), spatial (26), ecological niche (28), diagnostic or clinical (6), spread or response (9), and reviews (3). …”
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Modeling Big, Heterogeneous, Non-Gaussian Spatial and Spatio-Temporal Data Using FRK by Matthew Sainsbury-Dale, Andrew Zammit-Mangion, Noel Cressie

“…FRK is an R package for spatial and spatio-temporal modeling and prediction with very large data sets that, to date, has only supported linear process models and Gaussian data models. …”
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Metabolism-associated marker gene-based predictive model for prognosis, targeted therapy, and immune landscape in ovarian cancer: an integrative analysis of single-cell and bulk RNA sequencing with spatial transcriptomics by Lele Ling, Bingrong Li, Boliang Ke, Yinjie Hu, Kaiyong Zhang, Siwen Li, Te Liu, Peng Liu, Bimeng Zhang

“…The MRG-based prognostic model was further utilized for functional analysis of the model gene set, pan-cancer analysis of genomic variations, spatial transcriptomics analysis, as well as GO and KEGG enrichment analysis. …”
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Multi-scale electricity consumption prediction model based on land use and interpretable machine learning: A case study of China by Haizhi Luo, Yiwen Zhang, Xinyu Gao, Zhengguang Liu, Xiangzhao Meng, Xiaohu Yang

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Performance Prediction and Evaluation of Biogas-Fed Solid Oxide Fuel Cell Plants Based on the Spatial and Temporal Fusion Model under Fluctuation Fuel Types and Processing Options by Shuai Ma, Run Jia, Yanan Li, Changyan Liu, Hao Guo

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A data-driven supervised machine learning approach to estimating global ambient air pollution concentrations with associated prediction intervals by Liam Jordan Berrisford, Hugo Barbosa, Ronaldo Menezes

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Bayesian Adaptive Lasso for the Partial Functional Linear Spatial Autoregressive Model by Dengke Xu, Ruiqin Tian, Ying Lu

“…This study introduces a partial functional linear spatial autoregressive model which can explore the relationship between a scalar spatially dependent response variable and predictive variables containing both multiple scalar covariates and a functional covariate. …”
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Forecasting Lattice and Point Spatial Data: Comparison of Unilateral and Multilateral SAR Models by Carlo Grillenzoni

“…Spatial auto-regressive (SAR) models are widely used in geosciences for data analysis; their main feature is the presence of weight (W) matrices, which define the neighboring relationships between the spatial units. …”
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The role of host plants, land cover and bioclimate in predicting the invasiveness of Aromia bungii on a global scale by Enrico Ruzzier, Seunghyun Lee, Pietro Tirozzi, Valerio Orioli, Andrea Di Giulio, Olivia Dondina, Luciano Bani

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Modified STARIMA model for space-time data by Laura Šaltytė

“… In this paper we propose spatial time series model. ARIMA model class is considered for each location. …”
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A novel approach to skin disease segmentation using a visual selective state spatial model with integrated spatial constraints by Yu Bai, Hai Zhou, Hongjie Zhu, Shimin Wen, Binbin Hu, Haotian Li, Huazhang Wang, Daji Ergu, Fangyao Liu

“…Additionally, we introduce a spatially-constrained loss function that mitigates gradient stability issues by considering the distance between label and prediction boundaries. …”
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Integrative spatial and single-cell transcriptomics elucidate programmed cell death-driven tumor microenvironment dynamics in hepatocellular carcinoma by Kai Lei, Kai Lei, Yutong Zhao, Shumin Li, Jiawei Liu, Wenhao Chen, Caihong Zhou, Yi Zhang, Jinmei Tan, Jian Wu, Qi Zhou, Qi Zhou, Jiehui Tan

“…This study aims to develop a PCD scores prediction model to evaluate the prognosis of hepatocellular carcinoma (HCC) and elucidate the tumor microenvironment differences.MethodsWe analyzed transcriptomic data from 363 HCC patients in the TCGA database and 221 patients in the GEO database to develop a PCD prediction model. …”
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Spatial modelling of vector-borne diseases: Where? When? How many? by Mr Cedric Marsboom

“…Avia-GIS R&D team has an extensive expertise in the spatial modeling of vector-borne diseases (VBDs) to address critical concerns regarding the epidemiology and control of VBDs. …”
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A model of spatially restricted transcription in opposing gradients of activators and repressors by Michael A White, Davis S Parker, Scott Barolo, Barak A Cohen

“…This model quantitatively predicts the boundaries of gene expression within OARGs. …”
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Penalized Composite Likelihood Estimation for Spatial Generalized Linear Mixed Models by Mohsen Mohammadzadeh, Leyla Salehi

“…When discussing non-Gaussian spatially correlated variables, generalized linear mixed models have enough flexibility for modeling various data types. …”
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Latent spectral-spatial diffusion model for single hyperspectral super-resolution by Yingsong Cheng, Yong Ma, Fan Fan, Jiayi Ma, Yuan Yao, Xiaoguang Mei

“…To address these issues, we propose a novel latent spectral-spatial diffusion model (LSDiff) for single hyperspectral SR. …”
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Integration of single-nuclei and spatial transcriptomics to decipher tumor phenotype predictive of relapse-free survival in Wilms tumor by Ran Yang, Ran Yang, Lulu Xie, Rui Wang, Yi Li, Yifei Lu, Baihui Liu, Shuyang Dai, Shan Zheng, Shan Zheng, Kuiran Dong, Kuiran Dong, Rui Dong, Rui Dong

“…A prognostic ensemble machine learning model was constructed based on the Scissor+ tumor signature to accurately predict patient RFS. …”
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Modeling of spatial spread of COVID-19 pandemic waves in Russia using a kinetic-advection model by V. V. Aristov, A. V. Stroganov, A. D. Yastrebov

“…This paper studies the development of epidemic events in Russia, starting from the third and including the most recent fifth and sixth waves. Our twoparameter model is based on a kinetic equation. The investigated possibility of predicting the spatial spread of the virus according to the time lag of reaching the peak of infections in Russia as a whole as compared to Moscow is connected with geographical features: in Russia, as in some other countries, the main source of infection can be identified. …”
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