Mathematical models for simulating Covid-19 contagion in Italy: first wave

  • David Carf`ı Department of Mathematics University of California Riverside, USA
  • Alessia Donato Eng., PhD student, Department of Economics University of Messina, Italy

Abstract




In this paper, we provide two approximating functions for some dynamics associated with the first wave of Covid-19 contagion in Italy. We consider also two particular cases of Sicily and Lombardy. We consider only the evolution of total infected cases and new daily cases. We show that the total infected cases need, in the time period considered, two different approximations. We approximate the daily infected curves by the first derivative of the above two functions. In the case of Lombardy, we consider a wider time interval to obtain an ultimate approximation.




References

[1]. Allenbach, Y; Saadoun, D; Maalouf, G; Vieira, M; Hellio, A; Boddaert, J; Gros, H; Salem, J E; Resche Rigon, M; Menyssa, C; Biard, L; Benveniste, O; Cacoub, P. Development of a multivariate prediction model of intensive care unit transfer or death: A French prospective cohort study of hospitalized COVID-19 patients. PLoS One;15(10): e0240711, 2020.
[2]. Singh, S; Murali Sundram, B; Rajendran, K; Boon Law, K; Aris, T; Ibrahim, H; Chandra Dass, S; Singh Gill, B. Forecasting daily confirmed COVID-19 cases in Malaysia using ARIMA models. J Infect Dev Ctries;14(9): 971-976, 2020.
[3]. Zhou, W; Qin, X; Hu, X; Lu, Y; Pan, J. Prognosis models for severe and critical COVID-19 based on the Charlson and Elixhauser comorbidity indices. Int J Med Sci;17(15): 2257-2263, 2020.
[4]. Barda, N; Riesel, D; Akriv, A; Levy, J; Finkel, U; Yona, G; Greenfeld, D; Sheiba, S; Somer, J; Bachmat, E; Rothblum, G N; Shalit, U; Netzer, D; Balicer, R; Dagan, N. Developing a COVID-19 mortality risk prediction model when individual-level data are not available. Nat Commun;11(1): 4439, 2020.
[5]. Wu, G; Zhou, S; Wang, Y; Lv, W; Wang, S; Wang, T; Li, X. A prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings. Sci Rep;10(1): 14042, 2020.
[6]. Jehi, L; Ji, X; Milinovich, A; Erzurum, S; Merlino, A; Gordon, S; Young, J B; Kattan, M W. Development and validation of a model for individualized prediction of hospitalization risk in 4,536 patients with COVID-19. PLoS One;15(8): e0237419, 2020.
[7]. Maxmen, A; Tollefson, J. Two decades of pandemic war games failed to account for Donald Trump. Nature;584(7819): 26-29, 2020.
[8]. Yang, H M; Lombardi Junior, L P; Castro, F F M; Yang, A C. Mathematical model describing CoViD-19 in Sao Paulo, Brazil - evaluating isolation as control mechanism and forecasting epidemiological scenarios of release. Epidemiol Infect;148: e155, 2020.
[9]. Zhang, S; Guo, M; Duan, L; Wu, F; Hu, G; Wang, Z; Huang, Q; Liao, T; Xu, J; Ma, Y; Lv, Z; Xiao, W; Zhao, Z; Tan, X; Meng, D; Zhang, S; Zhou, E; Yin, Z; Geng, W; Wang, X; Zhang, J; Chen, J; Zhang, Y; Jin, Y. Development and validation of a risk factor-based system to predict short-term survival in adult hospitalized patients with COVID-19: a multicenter, retrospective, cohort study. Crit Care;24(1): 438, 2020.
[10]. Zuo, M; Khosa, S K; Ahmad, Z; Almaspoor, Z. Comparison of COVID-19 Pandemic Dynamics in Asian Countries with Statistical Modeling. Comput Math Methods Med;2020: 4296806, 2020.
[11]. Braun, P; Haffner, S; Woodcock, B G. COVID-19 pandemic predictions using the modified Bateman SIZ model and observational data for Heidelberg, Germany: Effect of vaccination with a SARS-CoV-2 vaccine, coronavirus testing and application of the Corona-Warn-App. Int J Clin Pharmacol Ther;58(8): 417-425, 2020.
[12]. Turk, P J; Chou, S-H; Kowalkowski, M A; Palmer, P P; Priem, J S; Spencer, M D; Taylor, Y J; McWilliams, A D. Modeling COVID-19 Latent Prevalence to Assess a Public Health Intervention at a State and Regional Scale: Retrospective Cohort Study. JMIR Public Health Surveill;6(2): e19353, 2020.
[13]. Merle, U; Lassmann, A; Dressel, A R; Braun, P. Evaluation of the COVID-19 pandemic using an algorithm based on the Bateman function: Prediction of disease progression using observational data for the city of Heidelberg, Germanyâ©. Int J Clin Pharmacol Ther;58(7): 366-374, 2020.
[14]. Goswami, K; Bharali, S; Hazarika, J. Projections for COVID-19 pandemic in India and effect of temperature and humidity. Diabetes Metab Syndr;14(5): 801- 805, 2020.
[15]. Ting, D S W; Carin, L; Dzau, V;Wong, T Y. - Digital technology and COVID-19. Nat Med;26(4): 459-461, 2020.
[16]. Anastassopoulou, C; Russo, L; Tsakris, A; Siettos, C. Data-based analysis, modelling and forecasting of the COVID-19 outbreak. PLoS One;15(3): e0230405, 2020.
[17]. Lai, A; Bergna, A; Acciarri, C; Galli, M; Zehender, G. Early phylogenetic estimate of the effective reproduction number of SARS-CoV-2. J Med Virol;92(6): 675-679, 2020.
Published
2020-06-30
How to Cite
CARF`I, David; DONATO, Alessia. Mathematical models for simulating Covid-19 contagion in Italy: first wave. Journal of Mathematical Economics and Finance, [S.l.], v. 6, n. 1, p. 39-56, june 2020. ISSN 2458-0813. Available at: <https://journals.aserspublishing.eu/jmef/article/view/5770>. Date accessed: 11 may 2021. doi: https://doi.org/10.14505/jmef.v6.1(10).03.