A Hybrid System For Pandemic Evolution Prediction
Abstract The areas of data science and data engineering have experienced strong advances in recent years. This has had a particular impact in areas such as healthcare, where, as a result of the pandemic caused by the COVID-19 virus, technological development has accelerated. This has led to a need to produce solutions that enable the collection, integration and efficient use of information for decision making scenarios. This is evidenced by the proliferation of monitoring, data collection, analysis, and prediction systems aimed at controlling the pandemic. This article proposes a hybrid model that combines the dynamics of epidemiological processes with the predictive capabilities of artificial neural networks to go beyond the prediction of the first ones. In addition, the system allows for the introduction of additional information through an expert system, thus allowing the incorporation of additional hypotheses on the adoption of containment measures.
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Azouani, A., Olson, E., and Titi, E. S., 2014. Continuous data assimilation using general interpolant observables. Journal of Nonlinear Science, 24(2):277–304.
Bauch, C. T., 2005. Imitation dynamics predict vaccinating behaviour. Proceedings of the Royal Society B: Biological Sciences, 272(1573):1669–1675.
Bertozzi, A. L., Franco, E., Mohler, G., Short, M. B., and Sledge, D., 2020. The challenges of modeling and forecasting the spread of COVID-19. Proceedings of the National Academy of Sciences, 117(29):16732–16738.
Casas, E. and Mateos, M., 2017. Optimal control of partial differential equations. In Computational mathematics, numerical analysis and applications, pages 3–59. Springer.
Castillo Ossa, L. F., Chamoso, P., Arango-Lépez, J., Pinto-Santos, F., Isaza, G. A., Santa-Cruz-González, C., Ceballos-Marquez, A., Hernández, G., and Corchado, J. M., 2021. A Hybrid Model for COVID-19 Monitoring and Prediction. Electronics, 10(7):799.
Chimmula, V. K. R. and Zhang, L., 2020. Time series forecasting of COVID-19 transmission in Canada using LSTM networks. Chaos, Solitons & Fractals, page 109864.
Corchado, J. M., Chamoso, P., Hernández, G., Gutierrez, A. S. R., Camacho, A. R., González-Briones, A., Pinto-Santos, F., Goyenechea, E., Garcia-Retuerta, D., Alonso-Miguel, M. et al., 2021. Deepint. net: A Rapid Deployment Platform for Smart Territories. Sensors, 21(1):236.
Daza-Torres, M. L., Capistrán, M. A., Capella, A., and Christen, J. A., 2021. Bayesian sequential data assimilation for COVID-19 forecasting. arXiv preprint arXiv:2103.06152.
Engbert, R., Rabe, M. M., Kliegl, R., and Reich, S., 2021. Sequential data assimilation of the stochastic SEIR epidemic model for regional COVID-19 dynamics. Bulletin of mathematical biology, 83(1):1–16. Hethcote, H. W., 1976. Qualitative analyses of communicable disease models. Mathematical Biosciences, 28(3–4):335–356.
Hethcote, H. W., 1989. Three basic epidemiological models. In Applied mathematical ecology, pages 119–144. Springer.
Kabir, K. A. and Tanimoto, J., 2019. Dynamical behaviors for vaccination can suppress infectious disease-A game theoretical approach. Chaos, Solitons & Fractals, 123:229–239.
Kabir, K. A. and Tanimoto, J., 2020. Evolutionary game theory modelling to represent the behavioural dynamics of economic shutdowns and shield immunity in the COVID-19 pandemic. Royal Society open science, 7(9):201095.
Kermack, W. O. and McKendrick, A. G., 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, 115:700–721.
Kermack, W. O. and McKendrick, A. G., 1932. A contribution to the mathematical theory of epidemics, part. II. Proceedings of the Royal Society of London. Series A, Containing papers of a mathematical and physical character, 138:55–83.
Kermack, W. O. and McKendrick, A. G., 1933. A contribution to the mathematical theory of epidemics, part. III. Proceedings of the Royal Society of London. Series A, Containing papers of a mathematical and physical character, 141:94–112.
Kuperman, M. and Wio, H., 1999. Front propagation in epidemiological models with spatial dependence. Physica A: Statistical Mechanics and its Applications, 272(1–2):206–222.
Le Gruenwald, S. and Jain, S. G., 2021. Leveraging Artificial Intelligence in Global Epidemics. Elsevier.
Li, H., Peng, R., and Wang, Z. A., 2018. On a diffusive susceptible-infected-susceptible epidemic model with mass action mechanism and birth-death effect: Analysis, simulations, and comparison with other mechanisms. SIAM Journal on Applied Mathematics, 78(4), 2129–2153. https://doi.org/10.1137/18M1167863.
Markowich, P. A., Titi, E. S., and Trabelsi, S., 2016. Continuous data assimilation for the three-dimensional Brinkman-Forchheimer-extended Darcy model. Nonlinearity, 29(4):1292.
Mondaini, R. P., 2020. Trends in Biomathematics: Modeling Cells, Flows, Epidemics, and the Environment. Springer.
Ozturk, T., Talo, M., Yildirim, E. A., Baloglu, U. B., Yildirim, O., and Acharya, U. R., 2020. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in Biology and Medicine, page 103792.
Perc, M., Gorišek Miksi?, N., Slavinec, M., and Stožer, A., 2020. Forecasting covid-19. Frontiers in Physics, 8:127.
Presentaciones Covid-19 - Ministerio de Salud, Gobierno de Panamá Ministerio de Salud, Gobierno de Panamá http://www.minsa.gob.pa/informacion-salud/presentaciones-covid-19-detalles. (Accessed on 05/20/2021).
Suo, J. and Li, B., 2020. Analysis on a diffusive SIS epidemic system with linear source and frequency-dependent incidence function in a heterogeneous environment. Math. Biosci. Eng., 17(1):418–441.
Tanimoto, J., 2021. Sociophysics Approach to Epidemics, volume 23. Springer Nature.
Tröltzsch, F., 2010. Optimal control of partial differential equations: theory, methods, and applications, volume 112. American Mathematical Soc.
Wang, S., Yang, X., Li, L., Nadler, P., Arcucci, R., Huang, Y., Teng, Z., and Guo, Y., 2020. A Bayesian Updating Scheme for Pandemics: Estimating the Infection Dynamics of COVID-19. IEEE Computational Intelligence Magazine, 15(4):23–33.
Yousaf, M., Zahir, S., Riaz, M., Hussain, S. M., and Shah, K., 2020. Statistical analysis of forecasting COVID-19 for upcoming month in Pakistan. Chaos, Solitons & Fractals, 138:109926.
Muñoz, L., Alonso-García, M., Villarreal, V., Hernández, G., Nielsen, M., Pinto-Santos, F., Saavedra, A., Areiza, M., Montenegro, J., Sittón-Candanedo, I., Caballero-González, Y., Trabelsi, S., & Corchado, J. M. (2022). A Hybrid System For Pandemic Evolution Prediction. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 11(1), 111–128. https://doi.org/10.14201/adcaij.28093
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