medigraphic.com
ISSN 2448-8011 (Digital)
Revista de Divulgación del INSTITUTO DE MEDICINA FORENSE de la UNIVERSIDAD VERACRUZANA

2022, Número 2

<< Anterior Siguiente >>

Rev Mex Med Forense 2022; 7 (2)


Simulaciones de modelos estacionales de propagación del covid: estudio de caso en México

Ortigoza G, Hermida G, Hernández M

Idioma: Ingles.
Referencias bibliográficas: 32
Paginas: 147-161
Archivo PDF: 902.45 Kb.


FRAGMENTO

Sin resumen.


REFERENCIAS (EN ESTE ARTÍCULO)

  1. AL-QANESS, Mohammed AA, et al. Optimization method for fore casting confirmed cases of COVID-19 in China. Journal of Clinical Medicine, 2020, vol. 9, no 3, p. 674.

  2. Altizer S., Dobson A., Hosseini P., Hudson P., Pascual M and Rohani P., Seasonality and the dynamics of infectious diseases, (2006), Ecology letters, vol 9, pp 467-484.

  3. Axelsen J.B., Yaari R., Grenfell B. T., and Stone L.,Multiannual fore- casting of seasonal influenza dynamics reveals climatic and evolutionary drivers, (2014), PNAS, https://doi.org/10.1073/pnas.1321656111

  4. Bertozzi, A. L., Franco, E., Mohler, G., Short, M. B., Sledge, D. (2020). The challenges of modeling and forecasting the spread of COVID-19. Proceedings of the National Academy of Sciences 117(29), 16732-16738. https://doi.org/10.1073/ pnas.2006520117

  5. Brauer F., Castillo-Chavez C., Zhilan Feng. (2019), Mathematical Models in Epidemiology, Springer

  6. Buonomo B., Chitnis N., d’Onofrio A., (2018), Seasonality in epidemic models: a literature review, Ricerche di Matematica volume 67, pages 7–25

  7. Dos Santos-Gomes DC, de Oliveira-Serra GL. Machine learning model for computational tracking and forecasting the COVID-19 dynamic propagation. IEEE J Biomed Health Inf 2021;25(3):615–22. https://doi.org/10.1109/ JBHI.622102010.1109/JBHI.2021.3052134.

  8. Engelbrecht, F. A., Scholes, R. J. (2021), Test for Covid-19 seasonality and the risk of second waves, One health (Amsterdam, Netherlands), 12, 100202. https://doi.org/10.1016/j.onehlt.2020.100202

  9. Fisman DN. ,Seasonality of infectious diseases, (2007), Annu Rev Public Health.;28:127-43. doi: 10.1146/annurev.publhealth.28.021406.144128. PMID: 17222079.

  10. Franco E. ,(2020). A feedback SIR (fSIR) model highlights advantages and limitations of infection-based social distancing. arXiv preprint arXiv:2004.13216.

  11. Garces-Ayala F, Araiza-Rodr ́ıguez A, Mendieta-Condado E, Rodr ́ıguez- Maldonado AP, Wong-Arambula C, Landa-Flores M, et al. Full genoma sequence of the first SARS-CoV-2 detected in Mexico. Arch Virol 2020;165(9):2095–8. https:// doi.org/10.1007/s00705-020-04695-3.

  12. Ghafouri-Fard S., Mohammad-Rahimi H., Motie P., Minabi M., Taheri M., Nateghinia S., (2021),Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review, Heliyon, https://doi.org/10.1016/j.heliyon.2021.e08143

  13. Hazarika BB, Gupta D. Modelling and forecasting of COVID-19 spreadusing wavelet-coupled random vector functional link networks. Appl Soft Comput 2020; 96:1. https://doi.org/10.1016/j.asoc.2020.10662606626

  14. Kronfeld-Schor N., Stevenson T., Nickbakhsh S., Schernhammer E., Dopico X., Dayan T., Martinez M. and B. Helm B., (2021), Drivers of Infectious Disease Seasonality: Potential Implications for COVID-19, Journal of Biological Rhythms, Vol. 36 No. 1, pp 35 –54

  15. Offical mexican goverment health agency (2021) available at https://datos.covid-19.conacyt.mx/

  16. Lalmuanawma S., Hussain J., Chhakchhuak L.,Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review, Chaos, Solitons & Fractals,Vol 139, 2020, https://doi.org/10.1016/j.chaos.2020.110059.

  17. Y Li, Wang X, and Nair H (2020),Global seasonality of human seasonal coronaviruses: a clue for postpandemic circulating season of severe acute respiratory syndrome coronavirus 2, J Infect Dis 222:1090-1097.

  18. Liu QH, Zhang J, Peng C, Litvinova M, Huang S, Poletti P, et al. Model-based evaluation of alternative reactive class closure strategies against COVID-19 2021. https://doi.org/10.1101/2021.04.18.21255683. medRXiv: 2021.04.18.21255683.

  19. Loli Piccolomini E, Zama F. Monitoring italian COVID-19 spread by a forced SEIRD model. PLoS ONE 2020;15(8):e0237417. https://doi.org/10.1371/journal. pone.0237417.

  20. Mandal P.,Arinaminpathy N., Bhargava B.Panda S., (2021), Plausibility of a third wave of COVID-19 in India: A mathematical modelling based analysis, Indian J Med Res 153, pp 522-532

  21. Mummert, Anna et al. (2013) A perspective on multiple waves of influenza pandemics, PloS one vol. 8-4, doi:10.1371/journal.pone.0060343

  22. Nickbakhsh S, Ho A, Marques DF, McMenamin J, Gunson RN, and Murcia PR (2020), Epidemiology of seasonal coronaviruses: establishing the context for the emergence of coronavirus disease 2019, J Infect Dis 222: 17-25.

  23. Olinky R., Huppert A., Stone L., (2008), Seasonal dynamics and thresh-olds governing recurrent epidemics, Mathematical Biology, 56:827–839

  24. Our world in data, Accessed 17 november 2021, https://ourworldindata.org/grapher/share-people-vaccinated-covid

  25. Predict, Wolfram Language & System Documentation Center , https://reference.wolfram.com/language/ref/Predict.html Accessed 10 november 2021.

  26. Reiner RC, Barber RM, Collins JK. Modeling COVID-19 scenarios for the United States. Nat Med 2020;27:94–105.

  27. Rock K, Brand S, Moir J, Keeling MJ. Dynamics of infectious diseases, Rep Prog Phys. 2014;77(2):026602. doi: 10.1088/00344885/77/2/026602. Epub 2014 Jan 20. PMID: 24444713.

  28. Suarez V, Quezada MS, Ruiz SO, De Jes ́us ER. Epidemiolog ́ıa de COVID-19 en Mexico: del 27 de febrero al 30 de abril de 2020. Revista Cl ́ınica Espanola 2020; 220(8):463–71.

  29. https://doi.org/10.1016/j.rce.2020.05.007.

  30. WORLD HEALTH ORGANIZATION, et al. Coronavirus disease 2019 (COVID-19): situation report, 85. 2020

  31. Xie G. A novel Monte Carlo simulation procedure for modelling COVID-19 spread over time. Sci Rep 2020;10(1):1–9.

  32. YANG, Xiaobo, et al. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. The Lancet Respiratory Medicine, 2020.




2020     |     www.medigraphic.com