tutorial

Open access

A Review of Graph Neural Networks in Epidemic Modeling

Authors:

B. Aditya Prakash,

Wei JinAuthors Info & Claims

KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Pages 6577 - 6587

https://doi.org/10.1145/3637528.3671455

Published: 24 August 2024 Publication History

Abstract

Since the onset of the COVID-19 pandemic, there has been a growing interest in studying epidemiological models. Traditional mechanistic models mathematically describe the transmission mechanisms of infectious diseases. However, they often fall short when confronted with the growing challenges of today. Consequently, Graph Neural Networks (GNNs) have emerged as a progressively popular tool in epidemic research. In this paper, we endeavor to furnish a comprehensive review of GNNs in epidemic tasks and highlight potential future directions. To accomplish this objective, we introduce hierarchical taxonomies for both epidemic tasks and methodologies, offering a trajectory of development within this domain. For epidemic tasks, we establish a taxonomy akin to those typically employed within the epidemic domain. For methodology, we categorize existing work into Neural Models and Hybrid Models. Following this, we perform an exhaustive and systematic examination of the methodologies, encompassing both the tasks and their technical details. Furthermore, we discuss the limitations of existing methods from diverse perspectives and systematically propose future research directions. This survey aims to bridge literature gaps and promote the progression of this promising field. We hope that it will facilitate synergies between the communities of GNNs and epidemiology, and contribute to their collective progress.

References

[1]

Barbara Bramanti. 2012. Ancient Epidemic Diseases in a New Light. German Research, 34, 2, 22--27.

[2]

L. J. Bruce-Chwatt. 1977. Plagues and Peoples. By William H. McNeill. Pp. 369. (Basil Blackwell, Oxford, 1977.) Journal of Biosocial Science, 9, 4, 501--503.

[3]

Mathilde Frérot, Annick Lefebvre, Simon Aho, Patrick Callier, Karine Astruc, and Ludwig Serge Aho Glélé. 2018. What is epidemiology? changing definitions of epidemiology 1978--2017. PloS one, 13, 12, e0208442.

[4]

Paul Fine. 2015. Another defining moment for epidemiology. The Lancet, 385, 9965, 319--320.

[5]

Milton Terris. 1993. The Society for Epidemiologic Research and the Future of Epidemiology. Journal of Public Health Policy, 14, 2, 137. JSTOR: 3342960.

[6]

Sebastian Funk, Anton Camacho, Adam J. Kucharski, Rosalind M. Eggo, and W. John Edmunds. 2018. Real-time forecasting of infectious disease dynamics with a stochastic semi-mechanistic model. Epidemics, 22, 56--61.

[7]

Mikhail Alexandrovich Kondratyev. 2013. Forecasting methods and models of disease spread. Computer Research and Modeling, 5, 5, 863--882.

[8]

Derrick Louz, Hans E. Bergmans, Birgit P. Loos, and Rob C. Hoeben. 2010. Emergence of viral diseases: mathematical modeling as a tool for infection control, policy and decision making. Critical Reviews in Microbiology, 36, 3, 195--211.

[9]

Rafael Mikolajczyk, Ralf Krumkamp, Reinhard Bornemann, Amena Ahmad, Markus Schwehm, and Hans-Peter Duerr. 2009. Influenza. Deutsches -rzteblatt international.

[10]

Connor Shorten, Taghi M Khoshgoftaar, and Borko Furht. 2021. Deep learning applications for covid-19. Journal of big Data, 8, 1, 1--54.

[11]

Yuexin Wu, Yiming Yang, Hiroshi Nishiura, and Masaya Saitoh. 2018. Deep learning for epidemiological predictions. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, 1085--1088.

Digital Library

[12]

Farrukh Saleem, Abdullah Saad Al-Malaise Al-Ghamdi, Madini O Alassafi, and Saad Abdulla AlGhamdi. 2022. Machine learning, deep learning, and mathematical models to analyze forecasting and epidemiology of covid-19: a systematic literature review. International journal of environmental research and public health, 19, 9, 5099.

[13]

Federico Baldo, Lorenzo Dall'Olio, Mattia Ceccarelli, Riccardo Scheda, Michele Lombardi, Andrea Borghesi, Stefano Diciotti, and Michela Milano. 2021. Deep learning for virus-spreading forecasting: a brief survey. arXiv:2103.02346.

[14]

Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. 2020. A comprehensive survey on graph neural networks. IEEE TNNLS, 4--24.

[15]

Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR.

[16]

Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903.

[17]

Shaked Brody, Uri Alon, and Eran Yahav. 2022. How attentive are graph attention networks? In ICLR.

[18]

Xiao Liu, Lijun Zhang, and Hui Guan. 2023. Uplifting Message Passing Neural Network with Graph Original Information. (2023). eprint: 2210.05382.

[19]

Sohir Maskey, Ron Levie, Yunseok Lee, and Gitta Kutyniok. 2022. Generalization Analysis of Message Passing Neural Networks on Large Random Graphs. (2022). eprint: 2202.00645.

[20]

Songgaojun Deng, Shusen Wang, Huzefa Rangwala, Lijing Wang, and Yue Ning. 2020. Cola-GNN: Cross-location Attention based Graph Neural Networks for Long-term ILI Prediction. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. ACM, 245--254.

Digital Library

[21]

Lijing Wang, Aniruddha Adiga, Jiangzhuo Chen, Adam Sadilek, Srinivasan Venkatramanan, and Madhav Marathe. 2022. CausalGNN: Causal-Based Graph Neural Networks for Spatio-Temporal Epidemic Forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 36, 11, 12191--12199.

[22]

Junyi Gao, Rakshith Sharma, Cheng Qian, Lucas M Glass, Jeffrey Spaeder, Justin Romberg, Jimeng Sun, and Cao Xiao. 2021. STAN: spatio-temporal attention network for pandemic prediction using real-world evidence. Journal of the American Medical Informatics Association, 28, 4, 733--743.

[23]

Qi Cao, Renhe Jiang, Chuang Yang, Zipei Fan, Xuan Song, and Ryosuke Shibasaki. 2022. MepoGNN: Metapopulation Epidemic Forecasting with Graph Neural Networks. Tech. rep.

[24]

Tao Feng, Sirui Song, Tong Xia, and Yong Li. 2023. Contact Tracing and Epidemic Intervention via Deep Reinforcement Learning. ACM Transactions on Knowledge Discovery from Data, 17, 3, 1--24.

[25]

Mutong Liu, Yang Liu, and Jiming Liu. 2023. Epidemiology-aware Deep Learning for Infectious Disease Dynamics Prediction. In International Conference on Information and Knowledge Management, Proceedings. Association for Computing Machinery, 4084--4088.

[26]

Xiaolei Ru, Jack Murdoch Moore, Xin-Ya Zhang, Yeting Zeng, and Gang Yan. 2023. Inferring Patient Zero on Temporal Networks via Graph Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 37, 8, 9632--9640.

Digital Library

[27]

Sirui Song, Zefang Zong, Yong Li, Xue Liu, and Yang Yu. 2020. Reinforced Epidemic Control: Saving Both Lives and Economy. (2020). eprint: 2008.01257.

[28]

J. Christopher Clement, VijayaKumar Ponnusamy, K.C. Sriharipriya, and R. Nandakumar. 2022. A Survey on Mathematical, Machine Learning and Deep Learning Models for COVID-19 Transmission and Diagnosis. IEEE Reviews in Biomedical Engineering, 15, 325--340.

[29]

J Nayak B Naik P Dinesh and K Vakula PB Dash D Pelusi. [n. d.] Significance of deep learning for covid-19: state-of-the-art review. Research Biomedical Engineering, doi, 10.

[30]

Firuz Kamalov, Khairan Rajab, Aswani Kumar Cherukuri, Ashraf Elnagar, and Murodbek Safaraliev. 2022. Deep learning for Covid-19 forecasting: Stateof-the-art review. Neurocomputing, 511, 142--154.

Digital Library

[31]

Patrick Reiser et al. 2022. Graph neural networks for materials science and chemistry. Communications Materials, 3, 1, 93.

[32]

Hongzhi Wen, Jiayuan Ding, Wei Jin, YiqiWang, Yuying Xie, and Jiliang Tang. 2022. Graph neural networks for multimodal single-cell data integration. In Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, 4153--4163.

Digital Library

[33]

Oliver Wieder, Stefan Kohlbacher, Mélaine Kuenemann, Arthur Garon, Pierre Ducrot, Thomas Seidel, and Thierry Langer. 2020. A compact review of molecular property prediction with graph neural networks. Drug Discovery Today: Technologies, 37, 1--12.

[34]

Alaa Bessadok, Mohamed Ali Mahjoub, and Islem Rekik. 2022. Graph neural networks in network neuroscience. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 5, 5833--5848.

Digital Library

[35]

Enyan Dai, Tianxiang Zhao, Huaisheng Zhu, Junjie Xu, Zhimeng Guo, Hui Liu, Jiliang Tang, and Suhang Wang. 2022. A comprehensive survey on trustworthy graph neural networks: privacy, robustness, fairness, and explainability. arXiv preprint arXiv:2204.08570.

[36]

Yue Liu et al. 2022. A survey of deep graph clustering: taxonomy, challenge, and application. arXiv preprint arXiv:2211.12875.

[37]

Alberto Aleta et al. 2020. Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19. Nature Human Behaviour, 4, 9, 964--971.

[38]

Sheryl L. Chang, Nathan Harding, Cameron Zachreson, Oliver M. Cliff, and Mikhail Prokopenko. 2020. Modelling transmission and control of the COVID-19 pandemic in Australia. Nature Communications, 11, 1, 5710.

[39]

Renhe Jiang et al. 2021. Countrywide Origin-Destination Matrix Prediction and Its Application for COVID-19. In Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track. Vol. 12978. Yuxiao Dong, Nicolas Kourtellis, Barbara Hammer, and Jose A. Lozano, (Eds.) Springer International Publishing, 319--334.

[40]

Chuang Yang, Zhiwen Zhang, Zipei Fan, Renhe Jiang, Quanjun Chen, Xuan Song, and Ryosuke Shibasaki. 2023. EpiMob: Interactive Visual Analytics of Citywide Human Mobility Restrictions for Epidemic Control. IEEE Transactions on Visualization and Computer Graphics, 29, 8, 3586--3601.

Digital Library

[41]

1927. A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, 115, 772, 700--721.

[42]

Leon Danon, Ashley P Ford, Thomas House, Chris P Jewell, Matt J Keeling, Gareth O Roberts, Joshua V Ross, Matthew C Vernon, et al. 2011. Networks and the epidemiology of infectious disease. Interdisciplinary perspectives on infectious diseases, 2011.

[43]

Karel Caals, Abha Saxena, and CalvinWai-Loon Ho. 2017. Ethics of Epidemics, Research and Surveillance: a WHO Workshop Report. Asian Bioethics Review, 9, 3, 265--271.

[44]

Jonas Dehning, Johannes Zierenberg, F. Paul Spitzner, Michael Wibral, Joao Pinheiro Neto, Michael Wilczek, and Viola Priesemann. 2020. Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions. Science, 369, 6500, eabb9789.

[45]

Nicholas C. Grassly and Christophe Fraser. 2008. Mathematical models of infectious disease transmission. Nature Reviews Microbiology, 6, 6, 477--487.

[46]

Zhenyu Han, Yanxin Xi, Tong Xia, Yu Liu, and Yong Li. 2023. Devil in the Landscapes: Inferring Epidemic Exposure Risks from Street View Imagery. In Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems. ACM, 1--4.

Digital Library

[47]

Chintan Shah, Nima Dehmamy, Nicola Perra, Matteo Chinazzi, Albert-László Barabási, Alessandro Vespignani, and Rose Yu. 2020. Finding Patient Zero: Learning Contagion Source with Graph Neural Networks. (2020). eprint: 2006.11913.

[48]

Brian Dixon, George Lecakes, Paul K. Moon, and John Schmalzel. 2018. SEDS: Expanding TEDS to include physical structures. In 2018 IEEE Sensors Applications Symposium (SAS), 1--6.

Digital Library

[49]

Pauline Van Den Driessche. 2017. Reproduction numbers of infectious disease models. Infectious Disease Modelling, 2, 3, 288--303.

[50]

Abhishek Tomy, Matteo Razzanelli, Francesco Di Lauro, Daniela Rus, and Cosimo Della Santina. 2022. Estimating the state of epidemics spreading with graph neural networks. Nonlinear Dynamics, 109, 1, 249--263. eprint: 2105.05060.

[51]

Lijing Wang, Aniruddha Adiga, Jiangzhuo Chen, Adam Sadilek, Srinivasan Venkatramanan, and Madhav Marathe. 2022. Causal GNN: Causal-Based Graph Neural Networks for Spatio-Temporal Epidemic Forecasting. Tech. rep.

[52]

Elena Loli Piccolomini and Fabiana Zama. 2020. Monitoring Italian COVID-19 spread by a forced SEIRD model. PLOS ONE, 15, 8, e0237417. Alejandro F Villaverde, (Ed.)

[53]

Zewen Liu, Yunxiao Li, Mingyang Wei, Guancheng Wan, Max SY Lau, and Wei Jin. 2024. Epilearn: a python library for machine learning in epidemic modeling. arXiv e-prints, arXiv-2406.

[54]

Hao Sha, Mohammad Al Hasan, and George Mohler. 2021. Source detection on networks using spatial temporal graph convolutional networks. In 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 1--11.

[55]

Siqi Wang, Xiaoxiao Zhao, Jingyu Qiu, Haofen Wang, and Chuang Tao. 2023. WDCIP: spatio-temporal AI-driven disease control intelligent platform for combating COVID-19 pandemic. Geo-spatial Information Science, 0, 0, 1--25.

[56]

Carl Yang, Hongwen Song, Mingyue Tang, Leon Danon, and Ymir Vigfusson. 2022. Dynamic network anomaly modeling of cell-phone call detail records for infectious disease surveillance. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 4733--4742.

Digital Library

[57]

Bukyoung Jhun. 2021. Effective vaccination strategy using graph neural network ansatz. (2021). eprint: 2111.00920.

[58]

Tao Feng, Sirui Song, Tong Xia, and Yong Li. 2023. Contact Tracing and Epidemic Intervention via Deep Reinforcement Learning. ACM Transactions on Knowledge Discovery from Data, 17, 3, 1--24.

[59]

Yang Liu, Yu Rong, Zhuoning Guo, Nuo Chen, Tingyang Xu, Fugee Tsung, and Jia Li. 2023. Human Mobility Modeling during the COVID-19 Pandemic via Deep Graph Diffusion Infomax. Proceedings of the AAAI Conference on Artificial Intelligence, 37, 12, 14347--14355.

Digital Library

[60]

Jie Zhang, Pengfei Zhou, Yijia Zheng, and Hongyan Wu. 2023. Predicting influenza with pandemic-awareness via Dynamic Virtual Graph Significance Networks. Computers in Biology and Medicine, 158, 106807.

Digital Library

[61]

Yinzhou Tang, Huandong Wang, and Yong Li. 2023. Enhancing Spatial Spread Prediction of Infectious Diseases through Integrating Multi-scale Human Mobility Dynamics. In Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems. ACM, 1--12.

Digital Library

[62]

Chen Lin, Jianghong Zhou, Jing Zhang, Carl Yang, and Eugene Agichtein. 2023. Graph Neural Network Modeling of Web Search Activity for Real-time Pandemic Forecasting. In 2023 IEEE 11th International Conference on Healthcare Informatics (ICHI). IEEE, 128--137.

[63]

Viet Bach Nguyen, Truong Son Hy, Long Tran-Thanh, and Nhung Nghiem. 2023. Predicting COVID-19 pandemic by spatio-temporal graph neural networks: A New Zealand's study. eprint: 2305.07731.

[64]

Xiaojun Pu, Jiaqi Zhu, Yunkun Wu, Chang Leng, Zitong Bo, and Hongan Wang. 2023. Dynamic adaptive spatio-temporal graph network for COVID-19 forecasting. CAAI Transactions on Intelligence Technology.

[65]

Mingjie Qiu, Zhiyi Tan, and Bing-kun Bao. 2023. MSGNN: Multi-scale Spatiotemporal Graph Neural Network for Epidemic Forecasting. (2023). eprint: 2308.15840.

[66]

Shuo Yu, Feng Xia, Shihao Li, Mingliang Hou, and Quan Z. Sheng. 2023. Spatio-temporal Graph Learning for Epidemic Prediction. ACM Transactions on Intelligent Systems and Technology, 14, 2.

Digital Library

[67]

Zhijian Li, Xiyang Luo, Bao Wang, Andrea L. Bertozzi, and Jack Xin. 2019. A Study on Graph-Structured Recurrent Neural Networks and Sparsification with Application to Epidemic Forecasting. (2019). eprint: 1902.05113.

[68]

Qi Cao, Renhe Jiang, Chuang Yang, Zipei Fan, Xuan Song, and Ryosuke Shibasaki. 2023. Metapopulation Graph Neural Networks: Deep Metapopulation Epidemic Modeling with Human Mobility. In vol. 13718, 453--468. eprint: 2306.14857.

[69]

Yuejiao Wang, Dajun Daniel Zeng, Qingpeng Zhang, Pengfei Zhao, Xiaoli Wang, Quanyi Wang, Yin Luo, and Zhidong Cao. 2022. Adaptively temporal graph convolution model for epidemic prediction of multiple age groups. Fundamental Research, 2, 2, 311--320.

[70]

Shun Zheng, Zhifeng Gao,Wei Cao, Jiang Bian, and Tie-Yan Liu. 2021. HierST: A Unified Hierarchical Spatial-temporal Framework for COVID-19 Trend Forecasting. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. ACM, 4383--4392.

Digital Library

[71]

Jaeuk Moon, Seungwon Jung, Sungwoo Park, and Eenjun Hwang. 2023. RESEAT: Recurrent Self-Attention Network for Multi-Regional Influenza Forecasting. IEEE Journal of Biomedical and Health Informatics, 27, 5, 2585--2596.

[72]

Seungwon Jung, Jaeuk Moon, Sungwoo Park, and Eenjun Hwang. 2022. Self-Attention-Based Deep Learning Network for Regional Influenza Forecasting. IEEE Journal of Biomedical and Health Informatics, 26, 2, 922--933.

[73]

Feng Xie, Zhong Zhang, Liang Li, and Yusong Tan. 2022. EpiGNN: Exploring Spatial Transmission with Graph Neural Network for Regional Epidemic Forecasting. Tech. rep.

[74]

Xiaolei Ru, Jack Murdoch Moore, Xin-Ya Zhang, Yeting Zeng, and Gang Yan. 2023. Inferring Patient Zero on Temporal Networks via Graph Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 37, 8, 9632--9640.

Digital Library

[75]

Kyungwoo Song, Hojun Park, Junggu Lee, Arim Kim, and Jaehun Jung. 2023. COVID-19 infection inference with graph neural networks. Scientific Reports, 13, 1.

[76]

Racha Gouareb, Alban Bornet, Dimitrios Proios, Sónia Gonçalves Pereira, and Douglas Teodoro. 2023. Detection of Patients at Risk of Enterobacteriaceae Infection Using Graph Neural Networks: a Retrospective Study. (2023).

[77]

S. Siji Rani, Panickar Dhanyalaxmi, A. S. Akshay, K. M. Ananthakrishnan, and A. Siva Sankar. 2023. Spatio-Temporal Prediction in Epidemiology Using Graph Convolution Network. In Lecture Notes in Networks and Systems. Vol. 720 LNNS. Springer Science and Business Media Deutschland GmbH, 367--378.

[78]

Shuo Yu, Feng Xia, Shihao Li, Mingliang Hou, and Quan Z. Sheng. 2023. Spatio-temporal Graph Learning for Epidemic Prediction. ACM Transactions on Intelligent Systems and Technology, 14, 2.

Digital Library

[79]

Viet Bach Nguyen, Truong Son Hy, Long Tran-Thanh, and Nhung Nghiem. 2023. Predicting COVID-19 pandemic by spatio-temporal graph neural networks: A New Zealand's study. eprint: 2305.07731.

[80]

V. Maxime Croft, Senna C. J. L. van Iersel, and Cosimo Della Santina. 2023. Forecasting infections with spatio-temporal graph neural networks: a case study of the Dutch SARS-CoV-2 spread. Frontiers in Physics, 11.

[81]

Jaeuk Moon, Seungwon Jung, Sungwoo Park, and Eenjun Hwang. 2023. RESEAT: Recurrent Self-Attention Network for Multi-Regional Influenza Forecasting. IEEE Journal of Biomedical and Health Informatics, 27, 5, 2585--2596.

[82]

Qi Cao, Renhe Jiang, Chuang Yang, Zipei Fan, Xuan Song, and Ryosuke Shibasaki. 2023. Metapopulation Graph Neural Networks: Deep Metapopulation Epidemic Modeling with Human Mobility. In vol. 13718, 453--468. eprint: 2306.14857.

[83]

Yinzhou Tang, Huandong Wang, and Yong Li. 2023. Enhancing Spatial Spread Prediction of Infectious Diseases through Integrating Multi-scale Human Mobility Dynamics. In Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL '23). Association for Computing Machinery, 1--12.

Digital Library

[84]

Mutong Liu, Yang Liu, and Jiming Liu. 2023. Epidemiology-aware Deep Learning for Infectious Disease Dynamics Prediction. In International Conference on Information and Knowledge Management, Proceedings. Association for Computing Machinery, 4084--4088.

[85]

Jie Zhang, Pengfei Zhou, Yijia Zheng, and Hongyan Wu. 2023. Predicting influenza with pandemic-awareness via Dynamic Virtual Graph Significance Networks. Computers in Biology and Medicine, 158, 106807.

Digital Library

[86]

Sifat Afroj Moon, Rituparna Datta, Tanvir Ferdousi, Hannah Baek, Abhijin Adiga, Achla Marathe, and Anil Vullikanti. 2023. A Graph Based Deep Learning Framework for Predicting Spatio-Temporal Vaccine Hesitancy. (2023).

[87]

Chaoyue Sun, Ruogu Fang, Marco Salemi, Mattia Prosperi, and Brittany Rife Magalis. 2023. DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction. (2023).

[88]

David Kempe, Jon Kleinberg, and Éva Tardos. 2003. Maximizing the spread of influence through a social network. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, 137--146.

Digital Library

[89]

Doina Bucur and Petter Holme. 2020. Beyond ranking nodes: predicting epidemic outbreak sizes by network centralities. PLoS computational biology, 16, 7, e1008052.

[90]

Petter Holme. 2017. Three faces of node importance in network epidemiology: exact results for small graphs. Physical Review E, 96, 6, 062305.

[91]

Eli A. Meirom, Haggai Maron, Shie Mannor, and Gal Chechik. 2021. Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks. (2021). eprint: 2010.05313.

[92]

Baoling Shan, Xin Yuan, Wei Ni, Xin Wang, Ren Ping Liu, and Eryk Dutkiewicz. 2023. Novel Graph Topology Learning for Spatio-Temporal Analysis of COVID-19 Spread. IEEE Journal of Biomedical and Health Informatics, 27, 6, 2693--2704.

[93]

Xiaojun Pu, Jiaqi Zhu, Yunkun Wu, Chang Leng, Zitong Bo, and Hongan Wang. 2023. Dynamic adaptive spatio-temporal graph network for COVID-19 forecasting. CAAI Transactions on Intelligence Technology.

[94]

Racha Gouareb, Alban Bornet, Dimitrios Proios, Sónia Gonçalves Pereira, and Douglas Teodoro. 2023. Detection of Patients at Risk of Enterobacteriaceae Infection Using Graph Neural Networks: a Retrospective Study. (2023).

[95]

Abhishek Tomy, Matteo Razzanelli, Francesco Di Lauro, Daniela Rus, and Cosimo Della Santina. 2022. Estimating the state of epidemics spreading with graph neural networks. Nonlinear Dynamics, 109, 1, 249--263.

[96]

Kyungwoo Song, Hojun Park, Junggu Lee, Arim Kim, and Jaehun Jung. 2023. COVID-19 infection inference with graph neural networks. Scientific Reports, 13, 1.

[97]

Xiaojun Pu, Jiaqi Zhu, Yunkun Wu, Chang Leng, Zitong Bo, and Hongan Wang. [n. d.] Dynamic adaptive spatio-temporal graph network for COVID-19 forecasting. CAAI Transactions on Intelligence Technology, n/a, n/a.

[98]

Valerio La Gatta, Vincenzo Moscato, Marco Postiglione, and Giancarlo Sperli. 2021. An Epidemiological Neural Network Exploiting Dynamic Graph Structured Data Applied to the COVID-19 Outbreak. IEEE Transactions on Big Data, 7, 1, 45--55.

[99]

George Panagopoulos, Giannis Nikolentzos, and Michalis Vazirgiannis. 2021. Transfer Graph Neural Networks for Pandemic Forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 35, 6, 4838--4845.

[100]

Haorui Wang, Haoteng Yin, Muhan Zhang, and Pan Li. 2022. Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networks. (2022). eprint: 2203.00199.

[101]

Deyu Bo, Yuan Fang, Yang Liu, and Chuan Shi. [n. d.] Graph Contrastive Learning with Stable and Scalable Spectral Encoding.

[102]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2023. Attention Is All You Need. (2023). eprint: 1706.03762.

[103]

Richard E. Turner. 2024. An Introduction to Transformers. (2024). eprint: 2304.10557.

[104]

Derya Soydaner. 2022. Attention Mechanism in Neural Networks: Where it Comes and Where it Goes. Neural Computing and Applications, 34, 16, 13371--13385. eprint: 2204.13154.

Digital Library

[105]

Yue Cui, Chen Zhu, Guanyu Ye, Ziwei Wang, and Kai Zheng. 2021. Into the Unobservables: A Multi-range Encoder-decoder Framework for COVID-19 Prediction. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. ACM, 292--301.

Digital Library

[106]

S. Siji Rani, Panickar Dhanyalaxmi, A. S. Akshay, K. M. Ananthakrishnan, and A. Siva Sankar. 2023. Spatio-Temporal Prediction in Epidemiology Using Graph Convolution Network. In IOT with Smart Systems. Jyoti Choudrie, Parikshit N. Mahalle, Thinagaran Perumal, and Amit Joshi, (Eds.) Springer Nature, 367--378.

[107]

Yixin Liu, Zhao Li, Shirui Pan, Chen Gong, Chuan Zhou, and George Karypis. 2021. Anomaly detection on attributed networks via contrastive self-supervised learning. IEEE TNNLS.

[108]

Amol Kapoor, Xue Ben, Luyang Liu, Bryan Perozzi, Matt Barnes, Martin Blais, and Shawn O'Banion. 2020. Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks. (2020). eprint: 2007.03113.

[109]

Sifat Afroj Moon, Rituparna Datta, Tanvir Ferdousi, Hannah Baek, Abhijin Adiga, Achla Marathe, and Anil Vullikanti. 2023. A Graph Based Deep Learning Framework for Predicting Spatio-Temporal Vaccine Hesitancy. (2023).

[110]

Fernando Henrique Duarte, Gladston Moreira, Eduardo Luz, Leonardo Santos, and Vander Freitas. 2023. Time Series Forecasting of COVID-19 Cases in Brazil with GNN and Mobility Networks. In 361--375.

[111]

Nathan Sesti, Juan Jose Garau-Luis, Edward Crawley, and Bruce Cameron. 2021. Integrating LSTMs and GNNs for COVID-19 Forecasting. (2021). eprint: 2108.10052.

[112]

Lihao Guo and Yuxin Yang. 2021. Research on the Forecast of the Spread of COVID-19. In 2021 11th International Conference on Biomedical Engineering and Technology. ACM, 47--51.

Digital Library

[113]

Na Hu, Dafang Zhang, Kun Xie, Wei Liang, and Meng-Yen Hsieh. 2022. Graph learning-based spatial-temporal graph convolutional neural networks for traffic forecasting. Connection Science, 34, 1, 429--448.

[114]

Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. 2021. Graph Neural Networks: A Review of Methods and Applications. (2021). eprint: 1812.08434.

[115]

Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu. 2021. A Comprehensive Survey on Graph Neural Networks. IEEE Transactions on Neural Networks and Learning Systems, 32, 1, 4--24. eprint: 1901.00596.

[116]

Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. NeurIPS, 29.

[117]

Mingguo He, Zhewei Wei, and Ji-Rong Wen. 2022. Convolutional neural networks on graphs with chebyshev approximation, revisited. NeurIPS, 35, 7264--7276.

[118]

Huaze Xie, Da Li, Yuanyuan Wang, and Yukiko Kawai. 2022. Visualization Method for the Spreading Curve of COVID-19 in Universities using GNN. In 2022 IEEE International Conference on Big Data and Smart Computing (Big-Comp). IEEE, 121--128.

[119]

Qi Cao, Renhe Jiang, Chuang Yang, Zipei Fan, Xuan Song, and Ryosuke Shibasaki. 2023. MepoGNN: Metapopulation Epidemic Forecasting with Graph Neural Networks. In Machine Learning and Knowledge Discovery in Databases. Vol. 13718. Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, and Grigorios Tsoumakas, (Eds.) Springer Nature Switzerland, 453--468.

[120]

Alexander Rodríguez, Harshavardhan Kamarthi, Pulak Agarwal, Javen Ho, Mira Patel, Suchet Sapre, and B Aditya Prakash. 2022. Data-centric epidemic forecasting: a survey. arXiv preprint arXiv:2207.09370.

[121]

Duygu Balcan, Vittoria Colizza, Bruno Gonçalves, Hao Hu, José J. Ramasco, and Alessandro Vespignani. 2009. Multiscale mobility networks and the spatial spreading of infectious diseases. Proceedings of the National Academy of Sciences, 106, 51, 21484--21489.

[122]

Sayyed Rasoul Mousavi, Fateme Bahri, and Farzaneh Sadat Tabataba. 2012. An enhanced beam search algorithm for the shortest common supersequence problem. Engineering Applications of Artificial Intelligence, 25, 3, 457--467.

Digital Library

[123]

O. Diekmann, J. A. P. Heesterbeek, and M. G. Roberts. 2010. The construction of next-generation matrices for compartmental epidemic models. Journal of The Royal Society Interface, 7, 47, 873--885.

[124]

Zhenyu Han, Yanxin Xi, Tong Xia, Yu Liu, and Yong Li. 2023. Devil in the Landscapes: Inferring Epidemic Exposure Risks from Street View Imagery. In Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems. ACM, 1--4.

Digital Library

[125]

Sebastian Me?nar, Nada Lavra?, and Bla? ?krlj. 2021. Prediction of the Effects of Epidemic Spreading with Graph Neural Networks. In Complex Networks & Their Applications IX (Studies in Computational Intelligence). Rosa M. Benito, Chantal Cherifi, Hocine Cherifi, Esteban Moro, Luis Mateus Rocha, and Marta Sales-Pardo, (Eds.) Springer International Publishing, 420--431.

[126]

William Ogilvy Kermack and Anderson G McKendrick. 1927. A contribution to the mathematical theory of epidemics. Proceedings of the royal society of london. Series A, Containing papers of a mathematical and physical character, 115, 772, 700--721.

[127]

Siqi Wang, Xiaoxiao Zhao, Jingyu Qiu, Haofen Wang, and Chuang Tao. 2023. WDCIP: spatio-temporal AI-driven disease control intelligent platform for combating COVID-19 pandemic. Geo-spatial Information Science, 0, 0, 1--25.

[128]

Mingjie Qiu, Zhiyi Tan, and Bing-kun Bao. 2023. MSGNN: Multi-scale Spatiotemporal Graph Neural Network for Epidemic Forecasting. (2023). eprint: 2308.15840.

[129]

Chen Lin, Jianghong Zhou, Jing Zhang, Carl Yang, and Eugene Agichtein. 2023. Graph Neural Network Modeling of Web Search Activity for Real-time Pandemic Forecasting. In 2023 IEEE 11th International Conference on Healthcare Informatics (ICHI), 128--137.

[130]

Louis-Pascal Xhonneux, Meng Qu, and Jian Tang. 2020. Continuous graph neural networks. In International conference on machine learning. PMLR, 10432--10441.

[131]

Ben Chamberlain, James Rowbottom, Maria I Gorinova, Michael Bronstein, Stefan Webb, and Emanuele Rossi. 2021. Grand: graph neural diffusion. In International Conference on Machine Learning. PMLR, 1407--1418.

[132]

Michael Poli, Stefano Massaroli, Junyoung Park, Atsushi Yamashita, Hajime Asama, and Jinkyoo Park. 2019. Graph neural ordinary differential equations. arXiv preprint arXiv:1911.07532.

[133]

Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud. 2018. Neural ordinary differential equations. Advances in neural information processing systems, 31.

[134]

Himashree Bhattacharyya and Rashmi Agarwalla. 2023. Public health interventions in the control of emerging diseases. International Journal Of Community Medicine And Public Health, 10, 9, 3398--3402.

[135]

Dean T. Jamison. 2007. Disease control. In Solutions for the World's Biggest Problems: Costs and Benefits. Bjørn Lomborg, (Ed.) Cambridge University Press, 295--344.

[136]

Mingyu Luo, Jimin Sun, Zhenyu Gong, and Zhen Wang. 2021. What is always necessary throughout efforts to prevent and control COVID-19 and other infectious diseases? A physical containment strategy and public mobilization and management. BioScience Trends, 15, 3, 188--191.

[137]

Enyan Dai and SuhangWang. 2021. Towards self-explainable graph neural network. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 302--311.

Digital Library

[138]

Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec. 2019. Gnnexplainer: generating explanations for graph neural networks. Advances in neural information processing systems, 32.

[139]

Hao Yuan, Haiyang Yu, Shurui Gui, and Shuiwang Ji. 2022. Explainability in graph neural networks: a taxonomic survey. IEEE transactions on pattern analysis and machine intelligence, 45, 5, 5782--5799.

Cited By

Guo KWen HJin WGuo YTang JChang YBaeza-Yates RBonchi F(2024)Investigating Out-of-Distribution Generalization of GNNs: An Architecture PerspectiveProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671792(932-943)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671792
Huang WYe MShi ZWan GLi HDu BYang Q(2024)Federated Learning for Generalization, Robustness, Fairness: A Survey and BenchmarkIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.341886246:12(9387-9406)Online publication date: Dec-2024
https://doi.org/10.1109/TPAMI.2024.3418862
Elabid ZSasal LBusby DHadid A(2024)TG-PhyNN: An Enhanced Physically-Aware Graph Neural Network Framework for Forecasting Spatio-Temporal DataPRICAI 2024: Trends in Artificial Intelligence10.1007/978-981-96-0119-6_4(42-48)Online publication date: 12-Nov-2024
https://doi.org/10.1007/978-981-96-0119-6_4

Index Terms

A Review of Graph Neural Networks in Epidemic Modeling

Index terms have been assigned to the content through auto-classification.

Recommendations

Graph Neural Networks: Foundation, Frontiers and Applications
KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the recent years. Graph neural networks, also known as deep learning on graphs, graph representation learning, or geometric deep learning, have become one of the fastest-...
A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions
Recommender system is one of the most important information services on today’s Internet. Recently, graph neural networks have become the new state-of-the-art approach to recommender systems. In this survey, we conduct a comprehensive review of the ...
Graph-Based Data Representation and Prediction in Medical Domain Tasks Using Graph Neural Networks
Computational Science – ICCS 2024
Abstract
Medical data often presents as a time series, reflecting the disease's progression. This can be captured through longitudinal health records or hospital treatment notes, encompassing diagnoses, health states, medications, and procedures. ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 2024

6901 pages

ISBN:9798400704901

DOI:10.1145/3637528

General Chairs:
Ricardo Baeza-Yates
Northeastern University, USA
,
Francesco Bonchi
CENTAI / Eurecat, Italy

Copyright © 2024 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 August 2024

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Tutorial

Conference

KDD '24

Sponsor:

KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 25 - 29, 2024

Barcelona, Spain

Acceptance Rates

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

Upcoming Conference

KDD '25

Sponsor:
sigkdd
sigkdd

The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
1,239
Total Downloads

Downloads (Last 12 months)1,239
Downloads (Last 6 weeks)524

Reflects downloads up to 22 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Guo KWen HJin WGuo YTang JChang YBaeza-Yates RBonchi F(2024)Investigating Out-of-Distribution Generalization of GNNs: An Architecture PerspectiveProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671792(932-943)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671792
Huang WYe MShi ZWan GLi HDu BYang Q(2024)Federated Learning for Generalization, Robustness, Fairness: A Survey and BenchmarkIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.341886246:12(9387-9406)Online publication date: Dec-2024
https://doi.org/10.1109/TPAMI.2024.3418862
Elabid ZSasal LBusby DHadid A(2024)TG-PhyNN: An Enhanced Physically-Aware Graph Neural Network Framework for Forecasting Spatio-Temporal DataPRICAI 2024: Trends in Artificial Intelligence10.1007/978-981-96-0119-6_4(42-48)Online publication date: 12-Nov-2024
https://doi.org/10.1007/978-981-96-0119-6_4

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents