Open Access
Math. Model. Nat. Phenom.
Volume 19, 2024
Article Number 13
Number of page(s) 17
Section Population dynamics and epidemiology
Published online 20 June 2024
  1. H. Lu, C.W. Stratton and Y.-W. Tang, Outbreak of pneumonia of unknown etiology in Wuhan, China: the mystery and the miracle. J. Med. Virol. 92 (2020) 401. [Google Scholar]
  2. H.A. Rothan and S.N. Byrareddy, The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak. J. Autoimmun. 109 (2020) 102433. [CrossRef] [Google Scholar]
  3. D. Cucinotta and M. Vanelli, WHO declares COVID-19 a pandemic. Acta Bio Medica: Atenei Parmensis 91 (2020) 157. [Google Scholar]
  4. A. Zumla, J.F.W. Chan, E.I. Azhar, D.S.C. Hui and K.-Y. Yuen, Coronaviruses—drug discovery and therapeutic options. Nat. Rev. Drug Discov. 15 (2016) 327–347. [CrossRef] [PubMed] [Google Scholar]
  5. J. Piret and G. Boivin, Pandemics throughout history. Front. Microbiol. 11 (2021) 631736. [CrossRef] [Google Scholar]
  6. Y.-C. Liu, R.-L. Kuo and S.-R. Shih, COVID-19: the first documented coronavirus pandemic in history. Biomed. J. 43 (2020) 328–333. [CrossRef] [Google Scholar]
  7. P.C.Y. Woo, S.K.P. Lau, C.S.F. Lam, C.C.Y. Lau, A.K.L. Tsang, J.H.N. Lau, R. Bai, J.L.L. Teng, C.C.C. Tsang, M. Wang, et al., Discovery of seven novel mammalian and avian coronaviruses in the genus deltacoronavirus supports bat coronaviruses as the gene source of alphacoronavirus and betacoronavirus and avian coronaviruses as the gene source of gammacoronavirus and deltacoronavirus. J. Virol. 86 (2012) 3995–4008. [CrossRef] [Google Scholar]
  8. G. Chowell, S.M. Bertozzi, M. Arantxa Colchero, H. Lopez-Gatell, C. Alpuche-Aranda, M. Hernandez and M.A. Miller, Severe respiratory disease concurrent with the circulation of h1n1 influenza. New Engl. J. Med. 361 (2009) 674–679. [CrossRef] [PubMed] [Google Scholar]
  9. N.D. Darling, D.E. Poss, M.P. Schoelen, M. Metcalf-Kelly, S.E. Hill and S. Harris, Retrospective, epidemiological cluster analysis of the middle east respiratory syndrome coronavirus (MERS-CoV) epidemic using open source data. Epidemiol. Infect. 145 (2017) 3106–3114. [Google Scholar]
  10. A. Chafekar and B.C. Fielding, MERS-CoV: understanding the latest human coronavirus threat. Viruses 10 (2018) 93. [Google Scholar]
  11. T. Dolinay, D. Jun, A. Maller, A. Chung, B. Grimes, L. Hsu, D. Nelson, B. Villagas, G.H.J. Kim and J. Goldin, Quantitative image analysis in COVID-19 acute respiratory distress syndrome: a cohort observational study. F1000Research 10 (2021). [Google Scholar]
  12. J.H Malenfant, C.N. Newhouse and A.A. Kuo, Frequency of coronavirus disease 2019 (COVID-19) symptoms in healthcare workers in a large health system. Infect. Control Hosp. Epidemiol. 42 (2021) 1403–1404. [CrossRef] [PubMed] [Google Scholar]
  13. B. Rajendran, G. Ramkumar, et al., COVID-19-associated neurological manifestations and complications: an observational study. J. Assoc. Physicians India 70 (2022) 11–12. [CrossRef] [Google Scholar]
  14. N. Wilson, A. Kvalsvig, L.T. Barnard and M.G. Baker, Case-fatality risk estimates for COVID-19 calculated by using a lag time for fatality. Emerg. Infect. Dis. 26 (2020) 1339. [Google Scholar]
  15. World Health Organization, WHO COVID-19 dashboard (2020). [Google Scholar]
  16. I.O. Ayenigbara, COVID-19: an international public health concern. Central Asian J. Global Health 9 (2020). [CrossRef] [Google Scholar]
  17. A. Pak, O.A. Adegboye, A.I. Adekunle, K.M. Rahman, E.S. McBryde and D.P. Eisen, Economic consequences of the COVID-19 outbreak: the need for epidemic preparedness. Front. Public Health 8 (2020) 241. [CrossRef] [Google Scholar]
  18. Y. Shang, H. Li and R. Zhang, Effects of pandemic outbreak on economies: evidence from business history context. Front. Public Health 9 (2021) 146. [CrossRef] [Google Scholar]
  19. T. Grünheid and A. Hazem, Mental wellbeing of frontline health workers post-pandemic: lessons learned and a way forward. Front. Public Health 11 (2023) 1204662. [CrossRef] [Google Scholar]
  20. J. Xiong, O. Lipsitz, F. Nasri, L.M.W. Lii, H. Gill, L. Phan, D. Chen-Li, M. Iacobicci, R. Ho, A. Majeed, et al., Impact of COVID-19 pandemic on mental health in the general popilation: a systematic review. J. Affect. Disord. 277 (2020) 55–64. [CrossRef] [Google Scholar]
  21. B. Fatima, M.A. Alqidah, G. Zaman, F. Jarad and T. Abdeljawad, Modeling the transmission dynamics of middle eastern respiratory syndrome coronaviris with the impact of media coverage. Results Phys. 24 (2021) 104053. [CrossRef] [Google Scholar]
  22. Y. Gi, S. Ullah, M.A. Khan, M.Y. Alshahrani, M. Abohassan and M.B. Riaz, Mathematical modeling and stability analysis of the COVID-19 with qiarantine and isolation. Results Phys. 34 (2022) 105284. [CrossRef] [Google Scholar]
  23. R. Padmanabhan, H.S. Abed, N. Meskin, T. Khattab, M. Shraim and M.A. Al-Hitmi, A review of mathematical model-based scenario analysis and interventions for COVID-19. Comput. Methods Programs Biomed. 209 (2021) 106301. [CrossRef] [Google Scholar]
  24. G. Perone, Comparison of arima, ets, nnar, tbats and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy. Eur. J. Health Econ. (2021) 1–24. [Google Scholar]
  25. E.F. Sánchez-Úbeda, P. Sánchez-Martín, M. Torrego-Ellacuría, Á.D. Rey-Mejías, M.F. Morales-Contreras and J.-L. Pierta, Flexibility and bed margins of the comminity of Madrid’s hospitals diring the first wave of the SARS-CoV-2 pandemic. Int. J. Environ. Res. Public Health 18 (2021) 3510. [CrossRef] [Google Scholar]
  26. E.A. Hernandez-Vargas and J.X. Velasco-Hernandez, In-host mathematical modelling of COVID-19 in humans. Annu. Rev. Control 50 (2020) 448–456. [CrossRef] [Google Scholar]
  27. S. He, S. Tang, L. Rong, et al., A discrete stochastic model of the COVID-19 outbreak: forecast and control. Math. Biosci. Eng. 17 (2020) 2792–2804. [CrossRef] [MathSciNet] [Google Scholar]
  28. S.L. Khalaf and H.S. Flayyih, Analysis, predicting, and controlling the COVID-19 pandemic in Iraq through SIR model. Results Control Optim. 10 (2023) 100214. [CrossRef] [Google Scholar]
  29. X. Wang, T. Tang, L. Cao, K. Aihara and Q. Guo, Inferring key epidemiological parameters and transmission dynamics of COVID-19 based on a modified SEIR model. Math. Model. Natural Phenomena 15 (2020) 74. [CrossRef] [EDP Sciences] [Google Scholar]
  30. N. Crokidakis, Modeling the early evolution of the COVID-19 in Brazil: results from a susceptible–infectious–quarantined–recovered (SIQR) model. Int. J. Mod. Phys. C 31 (2020) 2050135. [CrossRef] [Google Scholar]
  31. Z. Xu, B. Wu and J. Topcu, Control strategies for COVID-19 epidemic with vaccination, shield immunity and quarantine: a metric temporal logic approach. PLoS One 16 (2021) e0247660. [CrossRef] [PubMed] [Google Scholar]
  32. J.E. Kim, H. Choi, Y. Choi and C.Hy. Lee, The economic impact of COVID-19 interventions: a mathematical modeling approach. Front. Public Health 10 (2022) 993745. [CrossRef] [Google Scholar]
  33. I.A. Baba, A. Yusuf, K.S. Nisar, A.-H. Abdel-Aty and T.A. Nofal, Mathematical model to assess the imposition of lockdown during COVID-19 pandemic. Results Phys. 20 (2021) 103716. [CrossRef] [Google Scholar]
  34. R. Prabakaran, S. Jemimah, P. Rawat, D. Sharma and M.M. Gromiha. A novel hybrid Seiqr model incorporating the effect of quarantine and lockdown regulations for COVID-19. Sci. Rep. 11 (2021) 24073. [Google Scholar]
  35. H. Zine, A. Boukhouima, E.M. Lotfi, M. Mahrouf, D.F.M. Torres and N. Yousfi, A stochastic time-delayed model for the effectiveness of moroccan COVID-19 deconfinement strategy. Math. Model. Natural Phenomena 15 (2020) 50. [CrossRef] [EDP Sciences] [Google Scholar]
  36. J.P. Arcede, R.L. Caga-Anan, C.Q. Mentuda and Y. Mammeri, Accounting for symptomatic and asymptomatic in a SEIR-type model of COVID-19. Math. Model. Natural Phenomena 15 (2020) 34. [CrossRef] [EDP Sciences] [Google Scholar]
  37. H.M. Paiva, R.J. Magalhães Afonso and D.G. Sanches, Forecast of the occupancy of standard and intensive care unit beds by COVID-19 in patients, in 2022 European Control Conference (ECC) (2022), 669–674. [CrossRef] [Google Scholar]
  38. S.H.A. Khoshnaw, R.H. Salih and S. Sulaimany, Mathematical modelling for coronavirus disease (COVID-19) in predicting future behaviours and sensitivity analysis. Math. Model. Nat. Phenom. 15 (2020) 33. [CrossRef] [EDP Sciences] [Google Scholar]
  39. S. Baas, S. Dijkstra, A. Braaksma, P. van Rooij, F.J. Snijders, L. Tiemessen and R.J. Boucherie, Real-time forecasting of COVID-19 bed occupancy in wards and intensive care units. Health Care Manage. Sci. 24 (2021) 402–419. [CrossRef] [PubMed] [Google Scholar]
  40. Q.J. Leclerc, N.M. Fuller, R.H. Keogh, K. Diaz-Ordaz, R. Sekula, M.G. Semple, K.E. Atkins, S.R. Procter, et al., Importance of patient bed pathways and length of stay differences in predicting COVID-19 hospital bed occupancy in England. BMC Health Serv. Res. 21 (2021) 566. [CrossRef] [Google Scholar]
  41. S. Gitto, C. Di Mauro, A. Ancarani and P. Mancuso, Forecasting national and regional level intensive care unit bed demand during COVID-19: the case of Italy. PLoS One 16 (2021) e0247726. [CrossRef] [PubMed] [Google Scholar]
  42. A. Remuzzi and G. Remuzzi, COVID-19 and Italy: what next? Lancet 395 (2020) 1225–1228. [CrossRef] [PubMed] [Google Scholar]
  43. F.J.R. Pelogia, V.S.T. Soares and H.M. Paiva, Short-term prediction of COVID-19 deaths in Argentina, in IX Latin American Congress on Biomedical Engineering and XXVIII Brazilian Congress on Biomedical Engineering, edited by J.L.B. Marques, C.R. Rodrigues, D.O.H. Suzuki, J. Marino Neto and R. García Ojeda. Springer Nature Switzerland, Cham (2024), 166–175. [CrossRef] [Google Scholar]
  44. H.M. Paiva, R.J. Magalhaes Afonso, D.G. Sanches and F.J. Ribeiro Pelogia, COVID-19 trend analysis in Mexican states and cities, in 2021 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE Engineering in Medicine and Biology Society Conference Proceedings. IEEE Eng. Med. & Biol. Soc., IEEE, Elsevier, Inst. Eng. & Technol. (2021), 1820–1823. [Google Scholar]
  45. H.M. Paiva, R.J. Magalhaes Afonso, D.G. Sanches and F.J. Ribeiro Pelogia, Study of the COVID-19 pandemic trending behavior in Israeli cities. IFAC PAPERSONLINE 54 (2021) 133–138. [CrossRef] [Google Scholar]
  46. F.J. Richards, A flexible growth function for empirical use. J. Exp. Bot. 10 (1959) 290–300. [CrossRef] [Google Scholar]
  47. A. Smirnova and G. Chowell, A primer on stable parameter estimation and forecasting in epidemiology by a problem-oriented regularized least squares algorithm. Infect. Dis. Model. 2 (2017) 268–275. [Google Scholar]
  48. H.M. Paiva, R. Junqueira Magalhães Afonso, F.M.S. de Lima Alvarenga, E. de Andrade Velasquez, et al., A computational tool for trend analysis and forecast of the COVID-19 pandemic. Appl. Soft Comput. 105 (2021) 107289. [CrossRef] [Google Scholar]
  49. D. Kraft, A software package for sequential quadratic programming. Forschungsbericht- Deutsche Forschungs- und Versuchsanstalt fur Luft- und Raumfahrt (1988). [Google Scholar]
  50. J. Nocedal and S.J. Wright, Quasi-Newton Methods. Springer New York, New York, NY (2006) 135–163. [Google Scholar]
  51. P. Virtanen, R. Gommers, T.E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, S.J. van der Walt, M. Brett, J. Wilson, K.J. Millman, N. Mayorov, A.R.J. Nelson, E. Jones, R. Kern, E. Larson, C.J. Carey, İ. Polat, Y. Feng, E.W. Moore, J. VanderPlas, D. Laxalde, J. Perktold, R. Cimrman, I. Henriksen, E.A. Quintero, C.R. Harris, A.M. Archibald, A.H. Ribeiro, F. Pedregosa, P. van Mulbregt and SciPy 1.0 Contributors, SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17 (2020) 261–272. [NASA ADS] [CrossRef] [Google Scholar]
  52. Italian Civil Protection Department, M. Morettini, A. Sbrollini, I. Marcantoni and L. Burattini, COVID-19 in Italy: dataset of the Italian Civil Protection Department. Data Brief 30 (2020) 105526. [CrossRef] [PubMed] [Google Scholar]
  53. Italian National Institute of Statistics (ISTAT), Popolazione residente al 1° gennaio 2023 per età, sesso e stato civile (2024). [Google Scholar]
  54. I. Bosa, A. Castelli, M. Castelli, O. Ciani, A. Compagni, M.M. Galizzi, M. Garofano, S. Ghislandi, M. Giannoni, G. Marini, et al., Response to COVID-19: was Italy (un)prepared? Health Econ. Policy Law 17 (2022) 1–13. [CrossRef] [PubMed] [Google Scholar]
  55. G.P. Pisano, R. Sadun and M. Zanini, Lessons from Italy’s response to coronavirus (2020). [Google Scholar]
  56. H. Secon, 2 regions of Italy took different approaches to fighting the coronavirus. Their results show that widespread testing and early social distancing really work (2020). [Google Scholar]
  57. G. Sebastiani, M. Massa and E. Riboli, Covid-19 epidemic in Italy: evolution, projections and impact of government measures. Eur. J. Epidemiol. 35 (2020) 341–345. [CrossRef] [PubMed] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.