644
Views
6
CrossRef citations to date
0
Altmetric
Original Articles

Prediction-driven collaborative emergency medical resource allocation with deep learning and optimization

ORCID Icon, ORCID Icon &
Pages 590-603 | Received 12 Jun 2021, Accepted 06 Jul 2022, Published online: 20 Jul 2022

References

  • Aleman, D. M., Wibisono, T. G., & Schwartz, B. (2011). A nonhomogeneous agent-based simulation approach to modeling the spread of disease in a pandemic outbreak. Interfaces, 41(3), 301–315. https://doi.org/10.1287/inte.1100.0550
  • Arora, H., Raghu, T. S., & Vinze, A. (2010). Resource allocation for demand surge mitigation during disaster response. Decision Support Systems, 50(1), 304–315. https://doi.org/10.1016/j.dss.2010.08.032
  • Bolón-Canedo, V., Sánchez-Maroño, N., & Alonso-Betanzos, A. (2013). A review of feature selection methods on synthetic data. Knowledge and Information Systems, 34(3), 483–519. https://doi.org/10.1007/s10115-012-0487-8
  • Brailsford, S., & Vissers, J. (2011). OR in healthcare: A European perspective. European Journal of Operational Research, 212(2), 223–234. https://doi.org/10.1016/j.ejor.2010.10.026
  • Büyüktahtakın, İ. E., des-Bordes, E., & Kıbış, E. Y. (2018). A new epidemics-logistics model: Insights into controlling the Ebola virus disease in West Africa. European Journal of Operational Research, 265(3), 1046–1063. https://doi.org/10.1016/j.ejor.2017.08.037
  • Dehning, J., Zierenberg, J., Spitzner, F. P., Wibral, M., Neto, J. P., Wilczek, M., & Priesemann, V. (2020). Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions. Science, 369(6500), eabb9789. https://doi.org/10.1126/science.abb9789
  • Dimitrov, N. B., & Meyers, L. A. (2010). Mathematical approaches to infectious disease prediction and control. INFORMS TutORials in Operations Research, 7, 1–25.
  • Du, M., Sai, A., & Kong, N. (2021). A data-driven optimization approach for multi-period resource allocation in cholera outbreak control. European Journal of Operational Research, 291(3), 1106–1116. https://doi.org/10.1016/j.ejor.2020.09.052
  • Enayati, S., & Özaltın, O. Y. (2020). Optimal influenza vaccine distribution with equity. European Journal of Operational Research, 283(2), 714–725. https://doi.org/10.1016/j.ejor.2019.11.025
  • Eryarsoy, E., Delen, D., Davazdahemami, B., & Topuz, K. (2021). A novel diffusion-based model for estimating cases, and fatalities in epidemics: The case of COVID-19. Journal of Business Research, 124, 163–178.
  • Fawaz, H. I., Forestier, G., Weber, J., Idoumghar, L., & Muller, P. A. (2019). Deep learning for time series classification: A review. Data Mining and Knowledge Discovery, 33(4), 917–963. https://doi.org/10.1007/s10618-019-00619-1
  • Hastie, T., Tibshirani, R., & Friedman, J. (2010). The elements of statistical learning: Data mining. In Inference and Prediction (2nd ed.). Springer.
  • Katris, C. (2021). A time series-based statistical approach for outbreak spread forecasting: Application of COVID-19 in Greece. Expert Systems with Applications, 166, 114077. https://doi.org/10.1016/j.eswa.2020.114077
  • Kim, H.-N., El-Saddik, A., & Jo, G.-S. (2011). Collaborative error-reflected models for cold-start recommender systems. Decision Support Systems, 51(3), 519–531. https://doi.org/10.1016/j.dss.2011.02.015
  • Lasry, A., Zaric, G. S., & Carter, M. W. (2007). Multi-level resource allocation for HIV prevention: A model for developing countries. European Journal of Operational Research, 180(2), 786–799. https://doi.org/10.1016/j.ejor.2006.02.043
  • Lecun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. [Database] https://doi.org/10.1038/nature14539
  • Liu, M., Xu, X., Cao, J., & Zhang, D. (2020). Integrated planning for public health emergencies: A modified model for controlling H1N1 pandemic. Journal of the Operational Research Society, 71(5), 748–761. https://doi.org/10.1080/01605682.2019.1582589
  • Long, E. F., Nohdurft, E., & Spinler, S. (2018). Spatial resource allocation for emerging epidemics: A comparison of greedy, myopic, and dynamic policies. Manufacturing & Service Operations Management, 20(2), 181–198. https://doi.org/10.1287/msom.2017.0681
  • Markowitz, H. M. (1987). Mean-Variance Analysis in portfolio choice and capital markets. Oxford.
  • Mueller-Peltzer, M., Feuerriegel, S., Nielsen, A. M., Kongsted, A., Vach, W., & Neumann, D. (2020). Longitudinal healthcare analytics for disease management: Empirical demonstration for low back pain. Decision Support Systems, 132, 113271. https://doi.org/10.1016/j.dss.2020.113271
  • Nikolopoulos, K., Punia, S., Schäfers, A., Tsinopoulos, C., & Vasilakis, C. (2021). Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions. European Journal of Operational Research, 290(1), 99–115.
  • Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. https://doi.org/10.1109/TKDE.2009.191
  • Pastor-Satorras, R., Castellano, C., Mieghem, P. V., & Vespignani, A. (2015). Epidemic processes in complex networks. Reviews of Modern Physics, 87(3), 925–979. https://doi.org/10.1103/RevModPhys.87.925
  • Pietz, J., McCoy, S., & Wilck, J. H. (2020). Chasing John Snow: Data analytics in the COVID-19 era. European Journal of Information Systems, 29(4), 388–404. https://doi.org/10.1080/0960085X.2020.1793698
  • Rachaniotis, N. P., Dasaklis, T. K., & Pappis, C. P. (2012). A deterministic resource scheduling model in epidemic control: A case study. European Journal of Operational Research, 216(1), 225–231. https://doi.org/10.1016/j.ejor.2011.07.009
  • Steuer, R. E. (1986). Multiple criteria optimization: Theory, computation, and applications. John Wiley and Sons.
  • Sun, M. (1992). Interactive multiple objective programming procedures via adaptive random search and feed-forward artificial neural networks [Ph.D. dissertation]. The University of Georgia
  • Sun, M. (2005). Some issues in measuring and reporting solution quality of interactive multiple objective programming procedures. European Journal of Operational Research, 162(2), 468–483. https://doi.org/10.1016/j.ejor.2003.08.058
  • Sun, M. (2014). Multiple objective programming. In Wang, J. (Ed.), Encyclopedia of business analytics and optimization (Vol. 3) (1585–1604). IGI Global.
  • Tang, L., Zhou, Y., Wang, L., Purkayastha, S., Zhang, L., He, J., Wang, F., & Song, P. X.-K. (2020). A review of multi-compartment infectious disease models. International Statistical Review = Revue Internationale de Statistique, 88(2), 462–513.
  • Wang, Z., Zhao, W., Deng, N., Zhang, B., & Wang, B. (2021). Mixed data-driven decision-making in demand response management: An empirical evidence from dynamic time-warping based nonparametric-matching DID. Omega, 100, 102233. https://doi.org/10.1016/j.omega.2020.102233
  • Yaesoubi, R., & Cohen, T. (2011). Dynamic health policies for controlling the spread of emerging infections: Influenza as an example. PloS One, 6(9), e24043.
  • Yang, Q., Zhang, Y., Dai, W., & Pan, S. (2020). Transfer learning. Cambridge University Press.
  • Yang, Z., Zeng, Z., Wang, K., Wong, S.-S., Liang, W., Zanin, M., Liu, P., Cao, X., Gao, Z., Mai, Z., Liang, J., Liu, X., Li, S., Li, Y., Ye, F., Guan, W., Yang, Y., Li, F., Luo, S., … He, J. (2020). Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions. Journal of Thoracic Disease, 12(3), 165–174.
  • Zhang, W.-D., Zu, Z.-H., Xu, Q., Xu, Z.-J., Liu, J.-J., & Zheng, T. (2014). Optimized strategy for the control and prevention of newly emerging influenza revealed by the spread dynamics model. PloS One, 9(1), e84694.
  • Zheng, N., Du, S., Wang, J., Zhang, H., Cui, W., Kang, Z., Yang, T., Lou, B., Chi, Y., Long, H., Ma, M., Yuan, Q., Zhang, S., Zhang, D., Ye, F., & Xin, J. (2020). Predicting COVID-19 in China using hybrid AI model. IEEE Transactions on Cybernetics, 50(7), 2891–2904.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.