2,503
Views
3
CrossRef citations to date
0
Altmetric
Research Article

A Reinforcement Learning Based Decision Support Tool for Epidemic Control: Validation Study for COVID-19

ORCID Icon &
Article: 2031821 | Received 09 Oct 2021, Accepted 18 Jan 2022, Published online: 08 Feb 2022

References

  • Achaiah, N. C., S. B. Subbarajasetty, and R. M. Shetty. 2020. R0 and Re of COVID-19: Can We Predict When the Pandemic Outbreak will be Contained? Indian Journal of Critical Care Medicine 24 (11): 1125–1908. doi:10.5005/jp-journals-10071-23649.
  • Arango, M., and L. Pelov. 2020. COVID-19 Pandemic Cyclic Lockdown Optimization Using Reinforcement Learning. arango2020covid19. http://arxiv.org/abs/2009.04647
  • Askitas, N., K. Tatsiramos, and B. Verheyden. 2021. Estimating worldwide effects of non-pharmaceutical interventions on COVID-19 incidence and population mobility patterns using a multiple-event study. Scientific Reports 11 (1):1–13. doi:10.1038/s41598-021-81442-x.
  • Bergwerk, M., T. Gonen, Y. Lustig, S. Amit, M. Lipsitch, C. Cohen, M. Mandelboim, Levin, E. G., Rubin, C., Indenbaum, V., Tal, I., Zavitan, M., Zuckerman, N., Bar-Chaim, A., Kreiss, Y., Regev-Yochay, G. et al. 2021. Covid-19 breakthrough infections in vaccinated health care workers. New England Journal of Medicine 385 (16):1474–84. doi:10.1056/nejmoa2109072.
  • Bhadra, A., A. Mukherjee, and K. Sarkar. 2021. Impact of population density on covid-19 infected and mortality rate in India. Modeling Earth Systems and Environment 7 (1):623–29. doi:10.1007/s40808-020-00984-7.
  • Busoniu, L., R. Babuska, and B. De schutter. 2010. Chapter 7 multi-agent reinforcement learning : An overview. Technology 38 (2):183–221.
  • Chinazzi, M., J. T. Davis, M. Ajelli, C. Gioannini, M. Litvinova, S. Merler, A. P. Y Piontti, Mu, K., Rossi, L., Sun, K., Viboud, C., Xiong, X., Yu, H., Halloran, M. E., Longini Jr, I. M., Vespignani, A., et al. 2020. The effect of travel restrictions on the spread of the 2019 novel coronavirus (covid-19) outbreak. Science 368 (6489):395–400. doi:10.1126/science.aba9757.
  • CHO, S. A. N. G. W. O. O. K. 2020. Quantifying the impact of nonpharmaceutical interventions during the covid-19 outbreak: The case of Sweden. Econometrics Journal 23 (3):323–44. doi:10.1093/ECTJ/UTAA025.
  • Cohen, K., and A. Leshem. 2021. Suppressing the impact of the covid-19 pandemic using controlled testing and isolation. Scientific Reports 11 (1):1–15. doi:10.1038/s41598-021-85458-1.
  • Corona, G., A. Pizzocaro, W. Vena, G. Rastrelli, F. Semeraro, A. M. Isidori, R. Pivonello, A. Salonia, A. Sforza, and M. Maggi. 2021. Diabetes is most important cause for mortality in covid-19 hospitalized patients: Systematic review and meta-analysis. Reviews in Endocrine & Metabolic Disorders 22 (2):275–96. doi:10.1007/s11154-021-09630-8.
  • Cotton, C., B. Crowley, B. Kashi, A. Huw Lloyd-Ellis, and F. Tremblay. 2020. “Quantifying the economic impacts of covid-19 policy responses on Canada's provinces in (almost) real time.” https://www.econ.queensu.ca/sites/econ.queensu.ca/files/wpaper/qed_wp_1441.pdf.
  • Daoust, J.-F., R. Nadeau, R. Dassonneville, E. Lachapelle, É. Bélanger, J. Savoie, and C. van der Linden. 2020. How to survey citizens’ compliance with covid-19 public health measures: Evidence from three survey experiments. Journal of Experimental Political Science 1–8. doi:10.1017/xps.2020.25.
  • Daron, A., V. Chernozhukov, I. Werning, and M. Whinston. 2020. A multi-risk SIR model with optimally targeted lockdown. NBER Working Paper Series 27102:1–39. https://mr-sir.herokuapp.com/.
  • Deschepper, M., K. Eeckloo, S. Malfait, D. Benoit, S. Callens, and S. Vansteelandt. 2021. Prediction of hospital bed capacity during the COVID− 19 pandemic. BMC Health Services Research 21 (1):1–10. doi:10.1186/s12913-021-06492-3.
  • Eker, S. 2020. Validity and usefulness of COVID-19 models. Humanities and Social Sciences Communications 7 (1):1–5. doi:10.1057/s41599-020-00553-4.
  • Ferguson, N., D. Laydon, Nedjati-Gilani, G., N. Imai, K. Ainslie, M. Baguelin, S. Bhatia, Boonyasiri, A., Cucunubá, Z., Cuomo-Dannenburg, G., Dighe, A., Dorigatti, I., Fu, H., Gaythorpe, K., Green , W., Hamlet, A., Hinsley, W., Okell, L. C, van Elsland, S., Thompson, H., Verity, R. et al. 2020. Impact of non-pharmaceutical interventions (NPIs) to reduce covid-19 mortality and healthcare demand. https://Www.Imperial.Ac.Uk/Media/Imperial-College/Medicine/Sph/Ide/Gida-Fellowships/Imperial-College-COVID19-NPI-Modelling-16-03-2020.Pdf. Imperial College COVID-19 Response Team. March. 1–20.
  • Fujimoto, S., H. Van hoof, and D. Meger. 2018. “addressing function approximation error in actor-critic methods.” 35th International Conference on Machine Learning, ICML 2018, July 10th to July 15th, 2018, Stockholm, Sweden, 4, PMLR, 2587–601. https://proceedings.mlr.press/v80/fujimoto18a.html
  • Giordano, G., F. Blanchini, R. Bruno, P. Colaneri, A. Di Filippo, A. Di Matteo, and M. Colaneri. 2020. Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy.Nature medicine 26 (6): 855–860. https://www.nature.com/articles/s41591-020-0883-7. doi:10.1038/s41591-020-0883-7.
  • Haddad, E. A., E. Karim, A. Abdelaaziz, A. Ali, M. Arbouch, and I. F. Araújo. 2020. The Impact Of Covid-19 In Morocco: Macroeconomic, Sectoral And Regional Effects. Rabat, Morocco: Policy Center for the New South. https://www.policycenter.ma/publications/impact-covid-19-morocco-macroeconomic-sectoral-and-regional-effects
  • Hall, V. J., S. Foulkes, A. Charlett, A. Atti, E. J. M. Monk, R. Simmons, E. Wellington, J Cole, M., Saei, A., Oguti, B., Munro, K., Wallace, S., D Kirwan, P., Shrotri, M., Vusirikala, A., Rokadiya, S., Kall, M., Zambon, M., Ramsay, M., Brooks, T., S Brown, C. et al. 2021. SARS-CoV-2 infection rates of antibody-positive compared with antibody-negative health-care workers in england: A large, multicentre, prospective cohort study (SIREN). The Lancet 397 (10283):1459–69. doi:10.1016/S0140-6736(21)00675-9.
  • Harshad, K., T. Ganu, and D. P. Seetharam. 2020. Optimising lockdown policies for epidemic control using reinforcement learning. Transactions of the Indian National Academy of Engineering 5 (2):129–32. doi:10.1007/s41403-020-00129-3.
  • HCP. 2021. “46_website HCP.” https://www.hcp.ma/Demographie-population_r142.html.
  • Herik, H. Jaap, V. D., W. H. M. U. Jos, and J. Van Rijswijck. 2002. Games solved: Now and in the future. Artificial Intelligence 134 (1–2):277–311. doi:10.1016/S0004-3702(01)00152-7.
  • Ian, C., A. Mondal, and C. G. Antonopoulos. 2020. A SIR model assumption for the spread of COVID-19 in different communities. Chaos, Solitons, and Fractals 139:1–14. doi:10.1016/j.chaos.2020.110057.
  • IMF. 2020 October. The great lockdown: Dissecting the economic effects. World Economic Outlook. A Long and Difficult Ascent, pp. 65–84. International Monetary Fund. 978-1-51355-605-5.
  • Jarvis, C. I., K. Van Zandvoort, A. Gimma, K. Prem, M. Auzenbergs, K. O’Reilly, G. Medley, Emery, J. C., Houben, R. M. G. J., Davies, N., Nightingale, E. S., Flasche, S., Jombart, T., Hellewell, J., Abbott, S., Munday, J. D., Bosse, N. I., Funk, S., Sun, F., Endo, A., Rosello, A. et al. 2020. Quantifying the impact of physical distance measures on the transmission of COVID-19 in the UK. BMC Medicine 18 (1):1. doi:10.1186/s12916-020-01597-8.
  • Jasper, V., E. E. Koks, and J. W. Hall. 2021. April. 4. Global economic impacts of covid-19 lockdown measures stand out in high frequency shipping data. PLoS ONE. 16:1–16. doi: 10.1371/journal.pone.0248818.
  • Kadanali, A., and G. Karagoz. 2016. An overview of Ebola virus disease. Northern Clinics of Istanbul 2 (1):81–86. doi:10.14744/nci.2015.97269.
  • Kadi, N., and M. Khelfaoui. 2020. population density, a factor in the spread of covid-19 in Algeria: Statistic study. Bulletin of the National Research Centre 44 (1). doi: 10.1186/s42269-020-00393-x.
  • Karin, O., Y. M. Bar-On, T. Milo, I. Katzir, A. Mayo, Y. Korem, B. Dudovich, Yashiv, E., Zehavi, A. J., Davidovitch, N., Milo, R., Alon, U., et al. 2020. cyclic exit strategies to suppress covid-19 and allow economic activity. MedRxiv 1–18. doi:10.1101/2020.04.04.20053579.
  • Khalilpourazari, S., and H. Hashemi doulabi. 2021a. Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in quebec. Annals of Operations Research. Annals of Operations Research. doi:10.1007/s10479-020-03871-7.
  • Khalilpourazari, S., and H. Hashemi doulabi. 2021b. Robust modelling and prediction of the COVID-19 pandemic in Canada. International Journal of Production Research1–17. doi:10.1080/00207543.2021.1936261.
  • Khalilpourazari, S., H. Hashemi doulabi, A. Özyüksel çiftçioğlu, and G. Wilhelm weber. 2021. Gradient-based grey wolf optimizer with gaussian walk: Application in modelling and prediction of the COVID-19 pandemic. Expert Systems with Applications 177 (March):114920. doi:10.1016/j.eswa.2021.114920.
  • Khalilpourazari, Soheyl, and D. Hossein hashemi. 2021. “using reinforcement learning to forecast the spread of COVID-19 in France.” ICAS 2021 - 2021 IEEE International Conference on Autonomous Systems, Proceedings, France, IEEE. doi: 10.1109/ICAS49788.2021.9551174.
  • Konda, V. R., and J. N. Tsitsiklis. 2000. Actor-critic algorithms. Neural Information Processing Systems, 2000 12, NIPS, USA. 1008–14. https://proceedings.neurips.cc/paper/1999/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf.
  • Leech, G., C. Rogers-Smith, J. Benjamin Sandbrink, B. Snodin, R. Zinkov, B. Rader, J. S. Brownstein, Gal, Y., Bhatt, S., Sharma, M., Mindermann, S., Brauner, J. M., Aitchison, L., et al. 2021. Mass mask-wearing notably reduces COVID-19 transmission. MedRxiv 1 (1):2021.06.16.21258817. http://medrxiv.org/content/early/2021/06/18/2021.06.16.21258817.abstract.
  • Libin, P. J. K., A. Moonens, T. Verstraeten, F. Perez-Sanjines, N. Hens, P. Lemey, and A. Nowé. 2021. Deep reinforcement learning for large-scale epidemic control. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 12461 LNAI, 155–70. doi:10.1007/978-3-030-67670-4_10.
  • Lillicrap, T. P., J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra. 2016. “Continuous control with deep reinforcement learning.” lillicrap2019continuous. https://arxiv.org/abs/1509.02971
  • Martin, E., S. F. Dowell, and N. Firese. 2011. Incubation period of Ebola hemorrhagic virus subtype zaire. Osong Public Health and Research Perspectives 2 (1):3–7. doi:10.1016/j.phrp.2011.04.001.
  • Masbah, M., and R. Aourraz. 2020. How Moroccans view the government ’ s measures? March: The Moroccan Institute for Policy Analysis. https://mipa.institute/7486
  • Matheron, G., N. Perrin, and O. Sigaud. 2019. The problem with DDPG: understanding failures in deterministic environments with sparse rewards. matheron2019problem. http://arxiv.org/abs/1911.11679
  • Mercer, T. R., and M. Salit. 2021. Testing at scale during the COVID-19 pandemic. Nature Reviews. Genetics 22 (7):415–26. doi:10.1038/s41576-021-00360-w.
  • Mnih, V., K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller. 2013. Playing Atari with Deep Reinforcement Learning. mnih2013playing. http://arxiv.org/abs/1312.5602
  • Moein, S., N. Nickaeen, A. Roointan, N. Borhani, Z. Heidary, S. Haghjooy Javanmard, J. Ghaisari, and Y. Gheisari. 2021. Inefficiency of SIR models in forecasting COVID-19 epidemic: a case study of isfahan. Scientific Reports 11 (1):1–9. doi:10.1038/s41598-021-84055-6.
  • Moroccan Minstry of Health. 2020. “47_website covidmaroc evolution reports portail.” http://www.covidmaroc.ma/Pages/LESINFOAR.aspx.
  • Murano, Y., R. Ueno, S. Shi, T. Kawashima, Y. Tanoue, S. Tanaka, S. Nomura, Shoji, H., Shimizu, T., Nguyen, H., Miyata, H., Gilmour, S., Yoneoka, D., et al. 2021. Impact of domestic travel restrictions on transmission of COVID-19 infection using public transportation network approach. Scientific Reports 11 (1):1–9. doi:10.1038/s41598-021-81806-3.
  • OECD. 2020. THE COVID-19 CRISIS IN Morocco as of may 6, 2020 COVID-19 update economic impact policy reactions. Organisation for Economic Co-operation and Development (OECD). https://www.oecd.org/mena/competitiveness/The-Covid-19-Crisis-in-Morocco.pdf
  • Ohi, A. Q., M. F. Mridha, M. Mostafa Monowar, and M. Abdul Hamid. 2020. Exploring optimal control of epidemic spread using reinforcement learning. Scientific Reports 10 (1):1–19. doi:10.1038/s41598-020-79147-8.
  • Okhuese, A. V. 2020. Estimation of the probability of reinfection with COVID-19 by the susceptible-exposed-infectious-removed-undetectable-susceptible model. JMIR Public Health and Surveillance 6 (2):1–11. doi:10.2196/19097.
  • Ontario Agency for Health Protection and Promotion (Public Health Ontario). 2021. economic impacts related to public health measures in response and recovery during the COVID-19 pandemic. Queen's Printer for Ontario. https://www.publichealthontario.ca/-/media/documents/ncov/phm/2021/03/eb-covid-19-economic-impacts.pdf?la=en
  • Oraby, T., M. G. Tyshenko, J. Campo Maldonado, K. Vatcheva, S. Elsaadany, W. Q. Alali, J. C. Longenecker, and M. Al-Zoughool. 2021. Modeling the effect of lockdown timing as a COVID-19 control measure in countries with differing social contacts. Scientific Reports 11 (1):1–13. doi:10.1038/s41598-021-82873-2.
  • Padmanabhan, R., N. Meskin, T. Khattab, M. Shraim, and M. Al-Hitmi. 2021. reinforcement learning-based decision support system for COVID-19. Biomedical Signal Processing and Control 68:102676. doi:10.1016/j.bspc.2021.102676.
  • PERC. 2021a. Finding the balance: public health and social measures in ghana. Partnership for Evidence-Based Response to COVID-19. August 2020. 1–9. https://preventepidemics.org/wp-content/uploads/2021/03/ghana_en_20210323_1721.pdf.
  • PERC. 2021b. Finding the Balance: Public Health and Social Measures in Morocco. Prevent Epidemics. https://preventepidemics.org/wp-content/uploads/2021/03/morocco_en_20210316_2047.pdf
  • Prague, M., L. Wittkop, A. Collin, D. Dutartre, Q. Clairon, P. Moireau, R. Thiébaut, and B. Hejblum. 2020. Multi-level modeling of early COVID-19 epidemic dynamics in French regions and estimation of the lockdown impact on infection rate. medRxiv. doi:10.1101/2020.04.21.20073536.
  • Probert, W. J. M., S. Lakkur, C. J. Fonnesbeck, K. Shea, M. C. Runge, M. J. Tildesley, and M. J. Ferrari. 2019. Context matters: Using reinforcement learning to develop human-readable, state-dependent outbreak response policies. Philosophical Transactions of the Royal Society B: Biological Sciences 374 (1776):20180277. doi:10.1098/rstb.2018.0277.
  • Quesada, J. A., A. López-Pineda, V. F. Gil-Guillén, J. M. Arriero-Marín, F. Gutiérrez, and C. Carratala-Munuera. 2021. Incubation period of covid-19: A systematic review and meta-analysis. Revista Clínica Española (English Edition) 221 (2):109–17. doi:10.1016/j.rceng.2020.08.002.
  • Rajgor, D. D., M. Har Lee, S. Archuleta, N. Bagdasarian, and S. Chye Quek. 2020. The many estimates of the COVID-19 case fatality rate. The Lancet Infectious Diseases 20 (7):776–77. doi:10.1016/S1473-3099(20)30244-9.
  • Richard, S., Sutton, and G. Barto Andrew. 2017. Reinforcement learning: An introduction. 2nd ed. Cambridge, Massachusetts London, England: The MIT Press. 978-0262039246.
  • Santos, I. F. F. D., G. M. A. Almeida, and F. A. B. F. de Moura. 2021. Adaptive SIR model for propagation of SARS-CoV-2 in Brazil. Physica a: Statistical Mechanics and Its Applications 569:125773. doi:10.1016/j.physa.2021.125773.
  • Schulman, J., F. Wolski, P. Dhariwal, A. Radford, and O. Klimov. 2017. Proximal Policy Optimization Algorithms. schulman2017proximal. http://arxiv.org/abs/1707.06347
  • Sen-Crowe, B., M. Sutherland, M. McKenney, and A. Elkbuli. 2021. A closer look into global hospital beds capacity and resource shortages during the COVID-19 pandemic. Journal of Surgical Research 260:56–63. doi:10.1016/j.jss.2020.11.062.
  • Silver, D., J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, Baker, L., Lai, M., Bolton, A., Chen, Y., Lillicrap, T., Hui, F., Sifre, L., van den Driessche, G., Graepel, T., Hassabis, D., et al. 2017. Mastering the game of go without human knowledge. Nature 550 (7676):354–59. doi:10.1038/nature24270.
  • Sun, J., X. Chen, Z. Zhang, S. Lai, B. Zhao, H. Liu, S. Wang, Huan, W., Zhao, R., Ng, M. T. A., Zheng, Y., et al. 2020. Forecasting the long-term trend of COVID-19 epidemic using a dynamic model. Scientific Reports 10 (1):1–10. doi:10.1038/s41598-020-78084-w.
  • Sun, P., X. Sun, L. Han, J. Xiong, Q. Wang, B. Li, Y. Zheng, Liu, J., Liu, Y., Liu, H., Zhang, T., et al. 2018. TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game. sun2018tstarbots. http://arxiv.org/abs/1809.07193
  • Sutton, R. S., D. McAllester, S. Singh, and Y. Mansour. 1996. Policy gradient methods for reinforcement learning with function approximation Advances in Neural Information Processing Systems 12 (NIPS 1999). 12. MIT Press. https://proceedings.neurips.cc/paper/1999/file/464d828b85b0bed98e80ade0a5c43b0f-Paper.pdf
  • WHO. 2020. “who coronavirus (covid-19) dashboard | who coronavirus (COVID-19) dashboard with vaccination data 2020.”https://covid19.who.int/.
  • Woolcott, O. O., and J. P. Castilla-Bancayán. 2021. The effect of age on the association between diabetes and mortality in adult patients with COVID-19 in Mexico. Scientific Reports 11 (1):1–10. doi:10.1038/s41598-021-88014-z.
  • World Bank Group. 2020. Morocco economic monitor, fall 2020. Morocco Economic Monitor, Fall. doi:10.1596/34976.
  • World Health Organization. 2015. Anticipating emerging infectious disease epidemics. World Health Organization. https://apps.who.int/iris/bitstream/handle/10665/252646/WHO-OHE-PED-2016.2-eng.pdf