Adaptive Gaussian Markov random field spatiotemporal models for infectious disease mapping and forecasting-Reference-Cited by-同舟云学术

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Adaptive Gaussian Markov random field spatiotemporal models for infectious disease mapping and forecasting

Published:2023-03 Issue: Volume:53 Page:100726
ISSN:2211-6753
Container-title:Spatial Statistics
language:en
Short-container-title:Spatial Statistics

Author:

MacNab Ying C.^ORCID

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Computers in Earth Sciences,Statistics and Probability

Reference43 articles.

1. Hierarchical Modeling and Analysis for Spatial Data;Banerjee,2014

2. Complexities of the COVID-19 infection risks;Baptista,2022

3. Bayesian image restoration, with two applications in spatial statistics;Besag;Ann. Inst. Statist. Math.,1991

4. Time Series Analysis: Forecasting and Control;Box,1970

5. Time Series Analysis: Forecasting and Control;Box,2015

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Similarity- and neighbourhood-based dynamic models for infection data: Uncovering the complexities of the COVID-19 infection risks;Spatial and Spatio-temporal Epidemiology;2024-11

2. Bayesian hierarchical spatiotemporal models for prediction of (under)reporting rates and cases: COVID-19 infection among the older people in the United States during the 2020–2022 pandemic;Spatial and Spatio-temporal Epidemiology;2024-06

3. A Review of Bayesian Spatiotemporal Models in Spatial Epidemiology;ISPRS International Journal of Geo-Information;2024-03-18

4. A scalable approach for short‐term disease forecasting in high spatial resolution areal data;Biometrical Journal;2023-10-27

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