A aplicação de um modelo estocástico para o acompanhamento da evolução da Covid-19 no município de Macaé-RJ

Ricardo França Santos

doi:10.5585/exactaep.2021.18982

Authors

Ricardo França Santos Universidade Federal do Rio de Janeiro (UFRJ) / Rio de Janeiro (RJ) https://orcid.org/0000-0002-0581-7103

DOI:

https://doi.org/10.5585/exactaep.2021.18982

Keywords:

Stochastic Models, Covid-19, Social Distancing

Abstract

Few stochastic models are available in the literature for use in case of scenario estimates considering Covid-19. The aim of this research is to present four scenarios involving deterministic modeling and stochastic modeling for decision making by municipal administrators regarding the occupation of ICU beds. Parameters in epidemiological models were achieved and, through a case study, a simulation was carried out to validate the data and estimate the results. As a contribution to the theory, the significant difference between the deterministic estimate and the stochastic estimate stands out. As a practical contribution, we highlight the use of stochastic modeling for decision making by the municipal manager considering the measures of social distancing. As a contribution to society, the importance of social isolation in the fight against covid-19 is highlighted, as well as the need to comply with the municipal decrees issued by the Municipality of Macaé.

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Author Biography

Ricardo França Santos, Universidade Federal do Rio de Janeiro (UFRJ) / Rio de Janeiro (RJ)

Coordenador do Curso de Engenharia de Produção - Engenharia - UFRJ, Macaé

References

ALMEIDA, R.C. (2020) Nota Técnica - Estudo preliminar sobre a evolução de casos da covid-19 no Brasil: análises e projeções. Universidade Federal do Paraná. Recuperado em 20 agosto, 2020, de google.com/view/rcalmeida-ufpr/

FRASER, C., DONNELLY, C. A., CAUCHEMEZ, S., HANAGE, W. P., VAN KERKHOVE, M. D., HOLLINGSWORTH, T. D., GRIFFIN, J., BAGGALEY, R. F., JENKINS, H. E., LYONS, E. J., JOMBART, T., HINSLEY, W. R., GRASSLY, N. C., BALLOUX, F., GHANI, A. C., FERGUSON, N. M., RAMBAUT, A., PYBUS, O. G., LOPEZ-GATELL, H., ALPUCHE-ARANDA, C. M. WHO Rapid Pandemic Assessment Collaboration (2009). Pandemic potential of a strain of influenza A (H1N1): early findings. Science (New York, N.Y.), 324 (5934), 1557–1561. https://doi.org/10.1126/science.1176062

JARVIS, C.I., VAN ZANDVOORT, K., GIMMA, A., PREM, K. (2020). Quantifying the impact of physical distance measures on the transmission of COVID-19 in the UK. BMC Med 18, 124. https://doi.org/10.1186/s12916-020-01597-8

KIRKEBY, C., HALASA, T., GUSSMANN, M., TOFT, N. & GRÆSBØLLPR. , K. (2017). Methods for Estimating Disease Transmission Rates: Evaluating the Precision of Poisson Regression and Two Novel Methods, Scientific Reports, 7:9496. DOI: http://doi.org/10.1038/s41598-017-09209-x

MELLAN, T.A., HOELTGEBAUM, H.H., MISHRA, S. et al. (2020) Estimating COVID-19 cases and reproduction number in Brazil. Imperial College. Report number: 19.

MINISTÉRIO DA SAÚDE. (n.d.). Cadastro Nacional de Estabelecimentos de Saúde – CNES/DATASUS. 2020. Recuperado em 10 julho, 2020, de http://cnes2.datasus.gov.br/

MIZUMOTO, K., & CHOWELL, G.. (2020). Transmission potential of the novel coronavirus (COVID-19) onboard the diamond Princess Cruises Ship, 2020. Infectious Disease Modelling, 5, pp. 264-270.

PREFEITURA MUNICIPAL DE MACAÉ. (n.d.). Site Oficial. Recuperado em 11 julho, 2020, de http://www.macae.rj.gov.br/

RODA, W.C.; VARUGHESE, M.B.; HAN, D.; LI, M.Y.. (2020). Why is it difficult to accurately predict the COVID-19 epidemic ?. Infectious Disease Modelling, 5, pp. 271-281.

ROOSA K., VARUGUESE M. B., HAN, D., LI M.Y. (2020). Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020. Infectious Disease Modelling , 5, pp. 256-263.

SCARABEL, F., PELLIS, L. BRAGAZZI, N. L., WU, J. (2020). Canada needs to rapidly escalate public health interventions for its COVID-19 mitigation strategies. Infectious Disease Modelling, 5, pp. 316-322.

SHIL, P.. (2016). Mathematical Modeling of Viral Epidemics: A Review. Biomedical Research Journal , 3(2), pp. 195-215. DOI: http://doi.org/10.4103/2349-3666.240612

YANG, ZIHANG, DANG, ZHONGKAI, MENG, CUI, HUANG, JINGZE, MENG, HAOTIAN, WANG, DEYU, CHEN, GUANHUA, ZHANG, JIAXUAN, PENG, HAIPENG, SHAO, YIMING. (2020). Propagation analysis and prediction of the COVID-19. Infectious Disease Modelling. 5, 282-292. http://doi.org/10.1016/j.idm.2020.03.002