Mass Vaccine Administration under Uncertain Supply Scenarios

• Autorzy: Foderaro, Salvatore , Naldi, Maurizio , Nicosia, Gaia , Pacifici, Andrea
• Konferencja: Federated Conference on Computer Science and Information Systems (16 ; 02-05.09.2021 ; online)
• Języki publikacji: EN

Abstrakty

EN

The insurgence of COVID-19 requires fast mass vaccination, 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 strategy 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.

Słowa kluczowe

PL

choroba; harmonogram; akcja szczepień

EN

disease; schedule; vaccination campaign

• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2021
• Tom: Vol. 25
• Strony: 393--402
• Opis fizyczny: Bibliogr. 28 poz., wz., wykr.

Twórcy

autor

Foderaro, Salvatore

• Università di Tor Vergata, via del Politecnico 1, Roma, Italy,

autor

Naldi, Maurizio

• Università LUMSA, via Marcantonio Colonna 19, Roma, Italy,

autor

Nicosia, Gaia

• Università Roma Tre, via della Vasca Navale 79, Roma, Italy,

autor

Pacifici, Andrea

• Università di Tor Vergata, via del Politecnico 1, Roma, Italy,

Bibliografia

• 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/10.1023/10.1007/978-0-387-87458-6_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.

Uwagi

1. Track 3: Advances in Information Systems and Technology

2. Session: 3rd Special Session on Data Science in Health, Ecology and Commerce

Identyfikatory

DOI

10.15439/2021F78