Logo PTI Logo FedCSIS

Proceedings of the 16th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 25

Mass Vaccine Administration under Uncertain Supply Scenarios

, , ,

DOI: http://dx.doi.org/10.15439/2021F78

Citation: Proceedings of the 16th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 25, pages 393402 ()

Full text

Abstract. The insurgence of COVID-19 requires fast mass vac- cination, hampered by scarce availability and uncertain supply of vaccine doses and a tight schedule for boosters. In this paper, we analyze planning strategies for the vaccination campaign to vaccinate as many people as possible while meeting the booster schedule. We compare a conservative strategy and q-days-ahead strategies against the clairvoyant strategy. The conservative strat- egy achieves the best trade-off between utilization and compliance with the booster schedule. Q-days-ahead strategies with q < 7 provide a larger utilization but run out of stock in over 30\% of days.


  1. E. J. Edwardes, A concise history of small-pox and vaccination in Europe. HK Lewis, 1902.
  2. D. L. Heymann and R. B. Aylward, “Mass vaccination: when and why,” Mass Vaccination: Global Aspects—Progress and Obstacles, pp. 1–16, 2006. http://dx.doi.org/http://dx.doi.org/10.1007/3-540-36583-4
  3. P. Homayounfar, “Process mining challenges in hospital information systems,” in 2012 Federated Conference on Computer Science and Information Systems (FedCSIS). IEEE, 2012, pp. 1135–1140.
  4. E. Zaitseva, V. Levashenko, and M. Rusin, “Reliability analysis of healthcare system,” in 2011 Federated Conference on Computer Science and Information Systems (FedCSIS). IEEE, 2011, pp. 169–175.
  5. M. Naldi, G. Nicosia, A. Pacifici, and U. Pferschy, “Profit-fairness trade-off in project selection,” Socio-Economic Planning Sciences, vol. 67, pp. 133–146, 2019. http://dx.doi.org/http://dx.doi.org/10.1016/j.seps.2018.10.007
  6. M. J. Keeling and P. Rohani, Modeling Infectious Diseases in Humans and Animals. Princeton University Press, 2011. ISBN 9781400841035. http://dx.doi.org/http://dx.doi.org/10.1515/9781400841035
  7. E. H. Kaplan, D. L. Craft, and L. M. Wein, “Emergency response to a smallpox attack: The case for mass vaccination,” Proceedings of the National Academy of Sciences, vol. 99, no. 16, pp. 10 935–10 940, 2002. http://dx.doi.org/http://dx.doi.org/10.1073/pnas.162282799
  8. A. R. da Cruz, R. T. N. Cardoso, and R. H. C. Takahashi, “Multiobjective synthesis of robust vaccination policies,” Applied Soft Computing, vol. 50, pp. 34–47, 2017. http://dx.doi.org/http://dx.doi.org/10.1016/j.asoc.2016.11.010
  9. D. Bertsimas, J. Ivanhoe, A. Jacquillat, M. Li, A. Previero, O. S. Lami, and H. T. Bouardi, “Optimizing Vaccine Allocation to Combat the COVID-19 Pandemic,” medRxiv, 2020. doi: http://dx.doi.org/10.1101/2020.11.17.20233213
  10. H. M. Wagner and T. M. Whitin, “Dynamic version of the economic lot size model,” Management Science, vol. 5, pp. 89–96, 1958. http://dx.doi.org/http://dx.doi.org/10.1287/mnsc.5.1.89
  11. W. I. Zangwill, “A backlogging model and a multi-echelon model of a dynamic economic lot size production system-a network approach,” Management Science, vol. 15, no. 9, pp. 506–527, 1969. http://dx.doi.org/http://dx.doi.org/10.1287/mnsc.15.9.506
  12. S. S. DeRoo, N. J. Pudalov, and L. Y. Fu, “Planning for a COVID-19 vaccination program,” Jama, vol. 323, no. 24, pp. 2458–2459, 2020. http://dx.doi.org/http://dx.doi.org/10.1001/jama.2020.8711
  13. R. Shretta, N. Hupert, P. Osewe, and L. J. White, “Vaccinating the world against COVID-19: getting the delivery right is the greatest challenge,” BMJ Global Health, vol. 6, no. 3, 2021. http://dx.doi.org/http://dx.doi.org/10.1136/bmjgh-2021-005273
  14. L. Bell and R. Wagner, “Modeling Emergency Room Arrivals Using the Poisson Process,” The College Mathematics Journal, vol. 50, no. 5, pp. 343–350, 2019. http://dx.doi.org/http://dx.doi.org/10.1080/07468342.2019.1662710
  15. M. Naldi, “Measurement-based modelling of internet dial-up access connections,” Computer networks, vol. 31, no. 22, pp. 2381–2390, 1999. http://dx.doi.org/http://dx.doi.org/10.1016/S1389-1286(99)00091-2
  16. D. K. Agarwal, A. E. Gelfand, and S. Citron-Pousty, “Zero-inflated models with application to spatial count data,” Environmental and Ecological statistics, vol. 9, no. 4, pp. 341–355, 2002. http://dx.doi.org/http://dx.doi.org/10.1023/A:1020910605990
  17. A. F. Zuur, E. N. Ieno, N. J. Walker, A. A. Saveliev, and G. M. Smith, Zero-truncated and zero-inflated models for count data. Springer, 2009, pp. 261–293. http://dx.doi.org/http://dx.doi.org/10.1023/10.1007/9780–387–87 458–6_11.
  18. S. Beckett, J. Jee, T. Ncube, S. Pompilus, Q. Washington, A. Singh, and N. Pal, “Zero-inflated Poisson (ZIP) distribution: Parameter estimation and applications to model data from natural calamities,” Involve, a Journal of Mathematics, vol. 7, no. 6, pp. 751–767, 2014. http://dx.doi.org/http://dx.doi.org/10.2140/involve.2014.7.751
  19. B. W. Silverman, Density estimation for statistics and data analysis. Routledge, 2018. ISBN 9780412246203 http://dx.doi.org/http://dx.doi.org/10.1201/9781315140919
  20. S. J. Sheather and M. C. Jones, “A reliable data-based bandwidth selection method for kernel density estimation,” Journal of the Royal Statistical Society: Series B (Methodological), vol. 53, no. 3, pp. 683–690, 1991. http://dx.doi.org/http://dx.doi.org/0.1111/j.2517-6161.1991.tb01857.x
  21. Gurobi, “Gurobi Optimizer Reference Manual,” https://www.gurobi.com/documentation/9.1/refman/index.html, 2020, [Online; accessed 24-May-2021].
  22. P. Detti, G. Nicosia, A. Pacifici, and G. Zabalo Manrique de Lara, “Robust single machine scheduling with a flexible maintenance activity,” Computers and Operations Research, vol. 107, pp. 19–31, 2019. http://dx.doi.org/http://dx.doi.org/10.1016/j.cor.2019.03.001
  23. A. Parnianifard, A. S. Azfanizam, M. K. A. Ariffin, and M. I. S. Ismail, “An overview on robust design hybrid metamodeling: Advanced methodology in process optimization under uncertainty,” International Journal of Industrial Engineering Computations, vol. 9, no. 1, pp. 1–32, 2018. http://dx.doi.org/http://dx.doi.org/10.5267/j.ijiec.2017.5.003
  24. D. S. Yamashita, V. A. Armentano, and M. Laguna, “Robust optimization models for project scheduling with resource availability cost,” Journal of Scheduling, vol. 10, no. 1, pp. 67–76, 2007. http://dx.doi.org/http://dx.doi.org/10.1007/s10951-006-0326-4
  25. U. Pferschy, G. Nicosia, A. Pacifici, and J. Schauer, “On the Stackelberg knapsack game,” European Journal of Operational Research, vol. 291, no. 1, pp. 18–31, 2021. http://dx.doi.org/http://dx.doi.org/10.1016/j.ejor.2020.09.007
  26. U. Pferschy, G. Nicosia, and A. Pacifici, “A Stackelberg knapsack game with weight control,” Theoretical Computer Science, vol. 799, pp. 149–159, 2019. http://dx.doi.org/http://dx.doi.org/10.1016/j.tcs.2019.10.007
  27. M. Motta, S. Sylvester, T. Callaghan, and K. Lunz-Trujillo, “Encouraging COVID-19 Vaccine Uptake Through Effective Health Com- munication,” Frontiers in Political Science, vol. 3, p. 1, 2021. http://dx.doi.org/http://dx.doi.org/10.3389/fpos.2021.630133
  28. E. Zaitseva, J. Kostolny, M. Kvassay, V. Levashenko, and K. Pancerz, “Failure analysis and estimation of the healthcare system,” in Proceedings of the 2013 Federated Conference on Computer Science and Information Systems, M. P. M. Ganzha, L. Maciaszek, Ed. IEEE, 2013, pp. pages 235–240.