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2024 | OriginalPaper | Chapter

Deep Graph Machine Learning Models for Epidemic Spread Prediction and Prevention

Authors : Charalampos Salis, Katia Papakonstantinopoulou

Published in: Complex Networks XV

Publisher: Springer Nature Switzerland

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Abstract

Epidemic spread prediction and prevention have been of paramount significance for safeguarding the public health and quality of life. However, the adoption of the appropriate safety measures and actions should also take into account other societal challenges, such as the impact on the local economy or the psychological strain on its inhabitants. Recent approaches for preventing an epidemic spread have led to the adoption of rather aggressive strategies with significantly negative side-effects. In this work we address the aforementioned issue by developing a dynamic and data-driven prevention strategy, using modern graph machine learning predictive models. This strategy imposes more realistic assumptions about the pandemic spread and the underlying network structure, while also minimizing the negative side-effects. Finally, the experimental evaluation of our novel architecture for the predictive model demonstrates that we significantly outperform existing methods.

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Bo, D., Wang, X., Liu, Y., Fang, Y., Li, Y., Shi, C.: A survey on spectral graph neural networks (2023). arXiv:2302.05631

Chertkov, M., Abrams, R., Sikaroudi, A.M.E., Krechetov, M., Slagle, C.N., Efrat, A., Fulek, R., Oren, E.: Graphical models of pandemic. Medrxiv, pp. 2021–02 (2021)

Deb, P., Furceri, D., Ostry, J.D., Tawk, N.: The economic effects of covid-19 containment measures. Open Econ. Rev. 33(1), 1–32 (2022)

Del Valle, S.Y., Hyman, J.M., Hethcote, H.W., Eubank, S.G.: Mixing patterns between age groups in social networks. Soc. Netw. 29(4), 539–554 (2007)

Eubank, S., Guclu, H., Anil Kumar, V.S., Marathe, M.V., Srinivasan, A., Toroczkai, Z., Wang, N.: Modelling disease outbreaks in realistic urban social networks. Nature 429(6988), 180–184 (2004)

Ivorra, B., Ferrández, M.R., Vela-Pérez, M., Ramos, A.M.: Mathematical modeling of the spread of the coronavirus disease 2019 (covid-19) considering its particular characteristics. The case of China. Commun. Nonlinear Sci. Numer. Simul. 88, 105303 (2020)

Jin, M., Koh, H.Y., Wen, Q., Zambon, D., Alippi, C., Webb, G.I., King, I., Pan, S.: A survey on graph neural networks for time series: forecasting, classification, imputation, and anomaly detection (2023). arXiv:2307.03759

Kermack, W.O., McKendrick, A.G.: A contribution to the mathematical theory of epidemics. Proc. Roy. Soc. Lond. Ser. A, Containing papers of a mathematical and physical character 115(772), 700–721 (1927)

Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks (2016). arXiv:1609.02907

10.

Krechetov, M., Sikaroudi, A.M.E., Efrat, A., Polishchuk, V., Chertkov, M.: Prediction and prevention of pandemics via graphical model inference and convex programming. Sci. Rep. 12(1), 7599 (2022)CrossRef

11.

Kucharski, A.J., Russell, T.W., Diamond, C., Liu, Y., Edmunds, J., Funk, S., Eggo, R.M., Sun, F., Jit, M., Munday, J.D., et al.: Early dynamics of transmission and control of covid-19: a mathematical modelling study. Lancet Infect. Dis. 20(5), 553–558 (2020)

12.

Panagopoulos, G., Nikolentzos, G., Vazirgiannis, M.: Transfer graph neural networks for pandemic forecasting. Proc. AAAI Conf. Artif. Intell. 35, 4838–4845 (2021)

13.

Passavanti, M., Argentieri, A., Barbieri, D.M., Lou, B., Wijayaratna, K., Mirhosseini, A.S.F., Wang, F., Naseri, S., Qamhia, I., Tangerås, M. et al.: The psychological impact of covid-19 and restrictive measures in the world. J. Affect. Disorders 283, 36–51 (2021)

14.

Phucharoen, C., Sangkaew, N., Stosic, K.: The characteristics of covid-19 transmission from case to high-risk contact, a statistical analysis from contact tracing data. EClinical Med. 27 (2020)

15.

Major Ronald Ross: The prevention of malaria. Nature 85, 263–264 (1910)CrossRef

16.

Tsang, T.K., Fang, L.Q., Zhang, A., Jiang, F.C., Ruan, S.M., Liu, L.Z., Cowling, B.J., Liu, W., Yang, Y.: Variability in transmission risk of sars-cov-2 in close contact settings: a contact tracing study in Shandong province, China. Epidemics 39, 100553 (2022)

17.

Yao, Q., Wang, M., Chen, Y., Dai, W., Li, Y.F., Tu, W.W., Yang, Q., Yu, Y.: Taking human out of learning applications: a survey on automated machine learning (2018). arXiv:1810.13306

Title: Deep Graph Machine Learning Models for Epidemic Spread Prediction and Prevention
Authors: Charalampos Salis
Katia Papakonstantinopoulou
Publisher: Springer Nature Switzerland
Book: Complex Networks XV
Print ISBN: 978-3-031-57514-3

Electronic ISBN: 978-3-031-57515-0

Copyright Year: 2024
DOI: https://doi.org/10.1007/978-3-031-57515-0_12

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