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Alanis, A. Y., Arana-Daniel, N., & Lopez-Franco, C. (2019). Artificial Neural Networks for Engineering Applications: Academic Press.
Araz, O. M., Choi, T. M., Olson, D. L., & Salman, F. S. (2020). Role of Analytics for Operational Risk Management in the Era of Big Data. Decision Sciences.
Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2019). Supply chain risk management and artificial intelligence: state of the art and future research directions. International Journal of Production Research, 57(7), 2179-2202.
Centers for Disease Control and Prevention, & Centers for Disease Control and Prevention. (2020). Clinical questions about COVID-19: questions and answers. Centers for Disease Control and Prevention.
Choi, T.-M., Chan, H. K., & Yue, X. (2016). Recent development in big data analytics for business operations and risk management. IEEE transactions on cybernetics, 47(1), 81-92.
Choi, T. M., & Lambert, J. H. (2017). Advances in risk analysis with big data. Risk Analysis, 37(8), 1435-1442.
Currie, C. S., Fowler, J. W., Kotiadis, K., Monks, T., Onggo, B. S., Robertson, D. A., & Tako, A. A. (2020). How simulation modelling can help reduce the impact of COVID-19. Journal of Simulation, 1-15.
De Oliveira, M. P. V., McCormack, K., & Trkman, P. (2012). Business analytics in supply chains–The contingent effect of business process maturity. Expert systems with applications, 39(5), 5488-5498.
Dieckmann, P., Torgeirsen, K., Qvindesland, S. A., Thomas, L., Bushell, V., & Langli Ersdal, H. (2020). The use of simulation to prepare and improve responses to infectious disease outbreaks like COVID-19: practical tips and resources from Norway, Denmark, and the UK. Advances in Simulation, 5, 1-10.
Dryhurst, S., Schneider, C. R., Kerr, J., Freeman, A. L., Recchia, G., Van Der Bles, A. M., ... & van der Linden, S. (2020). Risk perceptions of COVID-19 around the world. Journal of Risk Research, 23(7-8), 994-1006.
Dubey, R., Altay, N., Gunasekaran, A., Blome, C., Papadopoulos, T., & Childe, S. J. (2018). Supply chain agility, adaptability and alignment. International Journal of Operations & Production Management.
Fang, Y., Nie, Y., & Penny, M. (2020). Transmission dynamics of the COVID‐19 outbreak and effectiveness of government interventions: A data‐driven analysis. Journal of medical virology, 92(6), 645-659.
Forrester, J. W. (1997). Industrial dynamics. Journal of the Operational Research Society, 48(10), 1037-1041.
Gao, Y., & Er, M. J. (2005). NARMAX time series model prediction: feed-forward and recurrent fuzzy neural network approaches. Fuzzy sets systems, 150(2), 331-350.
Geissbauer, R., Vedsø, J., & Schrauf, S. (2016). A strategist’s guide to industry 4.0. Strategy and business, 83, 148-163.
Giannakis, M., & Louis, M. (2011). A multi-agent based framework for supply chain risk management. Journal of Purchasing Supply Management, 17(1), 23-31.
He, S., Peng, Y., & Sun, K. (2020). SEIR modeling of the COVID-19 and its dynamics. Nonlinear Dynamics, 101(3), 1667-1680.
Hosseini, S., Ivanov, D., & Dolgui, A. (2019). Review of quantitative methods for supply chain resilience analysis. Transportation Research Part E: Logistics and Transportation Review, 125, 285-307.
Huang, M., Yang, M., Zhang, Y., & Liu, B. (2012). System dynamics modeling-based study of contingent sourcing under supply disruptions. Systems Engineering Procedia, 4, 290-297.
Ivanov, D. (2020). Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transportation Research Part E: Logistics Transportation Review, 136, 101922.
Ivanov, D., & Dolgui, A. (2020). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning & Control, 1-14.
vanov, D., Dolgui, A., & Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3), 829-846.
Ivanov, D. (2017). Simulation-based ripple effect modelling in the supply chain. International Journal of Production Research, 55(7), 2083-2101.
Kohavi, R., Rothleder, N. J., & Simoudis, E. (2002). Emerging trends in business analytics. Communications of the ACM, 45(8), 45-48.
Kumar, S., & Eickhoff, J. H. (2005). Outsourcing: When and how should it be done? Information knowledge systems management, 5(4), 245-259.
Langroodi, R. R. P., & Amiri, M. (2016). A system dynamics modeling approach for a multi-level, multi-product, multi-region supply chain under demand uncertainty. Expert Systems with Applications, 51, 231-244.
Li, J., & Chan, F. (2013). An agent-based model of supply chains with dynamic structures. Applied Mathematical Modelling, 37(7), 5403-5413.
Li, Q., & Liu, A. (2019). Big data driven supply chain management. Procedia CIRP, 81, 1089-1094.
Lin, T., Horne, B. G., Tino, P., & Giles, C. L. (1996). Learning long-term dependencies in NARX recurrent neural networks. IEEE Transactions on Neural Networks, 7(6), 1329-1338.
Linton, T., & Vakil, B. (2020). Coronavirus is proving we need more resilient supply chains. Harvard business review, 5.
Macdonald, J. R., Zobel, C. W., Melnyk, S. A., & Griffis, S. E. (2018). Supply chain risk and resilience: theory building through structured experiments and simulation. International Journal of Production Research, 56(12), 4337-4355.
Min, H. (2010). Artificial intelligence in supply chain management: theory and applications. International Journal of Logistics: Research Applications, 13(1), 13-39.
Olivares-Aguila, J., & ElMaraghy, W. (2020). System dynamics modelling for supply chain disruptions. International Journal of Production Research, 1-19.
Oliveira, J. B., Jin, M., Lima, R. S., Kobza, J. E., & Montevechi, J. A. B. (2019). The role of simulation and optimization methods in supply chain risk management: Performance and review standpoints. Simulation Modelling Practice
Theory, 92, 17-44.
Petrovic, D. (2001). Simulation of supply chain behaviour and performance in an uncertain environment. International Journal of Production Economics, 71(1-3), 429-438.
Ritchie, R. Mortality risk of covid-19 - statistics and research. Retrieved September 16, 2020, from https://ourworldindata.org/mortality-risk-covid
Schlüter, F., Hetterscheid, E., & Henke, M. (2019). A simulation-based evaluation approach for digitalization scenarios in smart supply chain risk management. Journal of Industrial Engineering Management Science, 2019(1), 179-206.
Schmitt, A. J., & Singh, M. (2012). A quantitative analysis of disruption risk in a multi-echelon supply chain. International Journal of Production Economics, 139(1), 22-32.
Sterman, J. (2010). Business dynamics: Irwin/McGraw-Hill c2000..
Tian, L., & Noore, A. (2004). Software reliability prediction using recurrent neural network with Bayesian regularization. International Journal of Neural Systems, 14(03), 165-174.
Tsai, C.-W., Lai, C.-F., Chao, H.-C., & Vasilakos, A. V. (2015). Big data analytics: a survey. Journal of Big data, 2(1), 1-32.
Tsai, J.-M., & Hung, S.-W. (2016). Supply chain relationship quality and performance in technological turbulence: an artificial neural network approach. International Journal of Production Research, 54(9), 2757-2770.
Wang, H., Wang, Z., Dong, Y., Chang, R., Xu, C., Yu, X., ... & Cai, Y. (2020). Phase-adjusted estimation of the number of coronavirus disease 2019 cases in Wuhan, China. Cell discovery, 6(1), 1-8.