Estudo de critérios de distribuição de vacinas com doses limitadas - uma abordagem baseada em redes complexas
Resumo
A escolha assertiva de grupos de indivíduos a serem imunizados pode impactar diretamente na dinâmica de contaminação, tanto na proteção de uma parte frágil da população quanto na minimização da velocidade de contágio. Tal tarefa é importante e desafiadora, especialmente em cenários com número reduzido de doses disponíveis, por envolver inúmeras combinações possíveis para tomada de decisão. O objetivo deste trabalho é propor uma metodologia para a escolha de indivíduos a serem imunizados utilizando uma abordagem em rede. Nós propusemos uma metodologia baseada em algoritmo genético, tendo como linha de base medidas de centralidade para definição dos indivíduos a serem vacinados. Os resultados indicaram que a escolha sistematizada de indivíduos é uma decisão correta para este tipo de problema.
Palavras-chave:
modelo epidemiológico SIR estocático, centralidades em redes, vacinação, SIR
Referências
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Newman, M. E. (2002). Assortative mixing in networks. Physical review letters, 89(20):208701.
Oliveira, R. S., Xavier, C. R., Vieira, V. d. F., Reis, R. F., dos Santos, Quintela, B. d. M., Rocha, Martins, B., andWeber, R. (2021). How fast vaccination can control the covid-19 pandemic in brazil? In International Conference on Computer Science 2021.
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Rouquayrol, M. Z. and Filho, N. d. A. (2003). Epidemiologia & saúde. MEDSI, 6th edition.
Shulgin, B., Stone, L., and Agur, Z. (1998). Pulse vaccination strategy in the sir epidemic model. Bulletin of Mathematical Biology, 60(6):1123–1148.
Tanner, M. W., Sattenspiel, L., and Ntaimo, L. (2008). Finding optimal vaccination strategies under parameter uncertainty using stochastic programming. Mathematical Biosciences, 215(2):144–151.
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Bonacich, P. (1987). Power and centrality: A family of measures the american journal of sociology, vol. 92. American Journal of Sociology.
Bresee, J. S., Fiore, A. E., Fry, A., Gubareva, L. V., Shay, D. K., and Uyeki, T. M. (2011). Antiviral agents for the treatment and chemoprophylaxis of influenza: Recommendations of the advisory committee on immunization practices (acip). Morbidity and Mortality Weekly Report, 60:1–24.
Coburn, B. J., Wagner, B. G., and Blower, S. (2009). Modeling influenza epidemics and pandemics: Insights into the future of swine flu (h1n1). BMC Medicine, 7:30.
Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social Networks, 1(3):215–239.
Holland, J. H. (1975). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. University of Michigan Press, Ann Arbor.
Li, X., Mukandavire, C., Cucunubá, Z. M., Londono, S. E., Abbas, K., Clapham, H. E., Jit, M., Johnson, H. L., Papadopoulos, T., and Vynnycky, E. (2021). Estimating the health impact of vaccination against ten pathogens in 98 low-income and middle-income countries from 2000 to 2030: A modelling study. The Lancet, 397(10272):398–408.
Li, Z., Zhu, P., Zhao, D., Deng, Z., and Wang, Z. (2019). Suppression of epidemic spreading process on multiplex networks via active immunization. Chaos: An Interdisciplinary Journal of Nonlinear Science, 29(7):073111.
Liu, L., Luo, X., and Chang, L. (2017). Vaccination strategies of an sir pair approximation model with demographics on complex networks. Chaos, Solitons & Fractals, 104:282– 290.
Ndiaye, B. M., Tendeng, L., and Seck, D. (2020). Comparative prediction of confirmed cases with covid-19 pandemic by machine learning, deterministic and stochastic sir models. arXiv:2004.13489 [math, q-bio, stat].
Newman, M. E. (2002). Assortative mixing in networks. Physical review letters, 89(20):208701.
Oliveira, R. S., Xavier, C. R., Vieira, V. d. F., Reis, R. F., dos Santos, Quintela, B. d. M., Rocha, Martins, B., andWeber, R. (2021). How fast vaccination can control the covid-19 pandemic in brazil? In International Conference on Computer Science 2021.
Page, L., Brin, S., Motwani, R., and Winograd, T. (1999). The pagerank citation ranking: Bringing order to the web. Technical report, Stanford InfoLab.
Peixoto, T. P. (2020). The netzschleuder network catalogue and repository.
Rodrigues, R. F., da Silva, A. R., da Fonseca Vieira, V., and Xavier, C. R. (2018). Optimization of the choice of individuals to be immunized through the genetic algorithm in the sir model. In ICCSA, pages 62–75. Springer.
Rouquayrol, M. Z. and Filho, N. d. A. (2003). Epidemiologia & saúde. MEDSI, 6th edition.
Shulgin, B., Stone, L., and Agur, Z. (1998). Pulse vaccination strategy in the sir epidemic model. Bulletin of Mathematical Biology, 60(6):1123–1148.
Tanner, M. W., Sattenspiel, L., and Ntaimo, L. (2008). Finding optimal vaccination strategies under parameter uncertainty using stochastic programming. Mathematical Biosciences, 215(2):144–151.
Webber,W., Moffat, A., and Zobel, J. (2010). A similarity measure for indefinite rankings. ACM Transactions on Information Systems (TOIS), 28(4):1–38.
Publicado
18/07/2021
Como Citar
M. JÚNIOR, Ronaldo A.; VIEIRA, Vinícius F.; XAVIER, Carolina R..
Estudo de critérios de distribuição de vacinas com doses limitadas - uma abordagem baseada em redes complexas. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 10. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 103-114.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2021.16129.
