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Revista de Divulgación del INSTITUTO DE MEDICINA FORENSE de la UNIVERSIDAD VERACRUZANA

2022, Number 2

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Rev Mex Med Forense 2022; 7 (2)

Simulations of seasonal covid spread models: case study Mexico

Ortigoza G, Hermida G, Hernández M

Language: English
References: 32
Page: 147-161
PDF size: 902.45 Kb.


Text Extraction

In this work we propose some mathematical models to simulate seasonality behavior of Covid-19 spread; a periodic transmission rate is added to SEIR, SEIRS, SEIRS with vaccination (SEIRSV) ode systems and the models are fitted to reported Covid infected historical data 2021 in Mexico. Numerical simulations reproduce the qualitative seasonality behavior of covid spread and provide an insight to develop strategies to prevent the diseases spread. Nearly all discussed approaches show the possible appearance of a fourth covid wave in Mexico at the end of 2021. Our results suggest that it is mandatory to consider seasonal factors when planing intervention strategies.


REFERENCES

  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.




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