• Logo
  • HamaraJournals

A Linear Study of the Spread of COVID19 in China and Iran

Mohammadjavad Sayadi, Fateme Moghbeli, Hafez Mehrjoo, Mohammadreza Mahaki
12

Views


Abstract

Introduction: Studying trends in observed rates provides valuable information in terms of need assessment, planning of programs and development indicators of each country. The purpose of the present study was to apply the regression model and the Fourier series in terms of predicting the trends in growth and mortality rate of corona virus disease.

Materials and methods: In this study, two linear analysis methods were used to predict the incidence and mortality rate of corona virus disease in Iran and China. The methods used are linear regression and Fourier transform. The data used were collected by referring to the official media of the mentioned countries, the general form of which is a time series of the incidence and mortality rate in recent days and the model implemented to estimate the incidence and mortality rate for the coming days. Python programming language version 3.7 is used to implement models.

Results: The results of this study show that the rates of corona virus disease incidence and mortality are still increasing. Meanwhile, the Fourier transform-based analytical method is more accurate than the linear regression method and on the other hand, the accuracy of both algorithms for predicting mortality was much higher than the prediction rate. This indicates that the mortality rate is higher than that of its linearity over time. The other point is that based on the results of this study, however, linear methods are very suitable for future prediction, due to the nature of epidemic diseases whose growth chart is nonlinear, linear methods cannot be used to predict the rate and mortality used in distant times.


References

Hu Z, Ge Q, Jin L, Xiong M. Artificial Intelligence Forecasting of Covid-19 in China. arXiv preprint arXiv:200207112. 2020.

Chen H, Guo J, Wang C, Luo F, Yu X, Zhang W, et al. Clinical characteristics and intrauterine vertical transmission potential of COVID-19 infection in nine pregnant women: a retrospective review of medical records. The Lancet. 2020.

Pan F, Ye T, Sun P, Gui S, Liang B, Li L, et al. Time course of lung changes on chest CT during recovery from 2019 novel coronavirus (COVID-19) pneumonia. Radiology. 2020:200370.

https://experience.arcgis.com/experience/685d0ace521648f8a5beeeee1b9125cd.

Hope TM. Linear regression. Machine Learning: Elsevier; 2020. p. 67-81.

Langarizadeh M, Moghbeli F. Applying naive bayesian networks to disease prediction: a systematic review. Acta Informatica Medica. 2016;24(5):364.

Moghbeli F, Langarizadeh M, Yoonesi A, Radmard AR, Rahmanian MS, Orooji A. A method for body fat composition analysis in abdominal magnetic resonance images via self-organizing map neural network. Iranian Journal of Medical Physics. 2018;15(2):108-16.

Bonakdari H, Pelletier J-P, Martel-Pelletier J. A reliable time-series method for predicting arthritic disease outcomes: new step from regression toward a nonlinear artificial intelligence method. Computer Methods and Programs in Biomedicine. 2020:105315.

Lau B, Gange SJ, Phair JP, Riddler SA, Detels R, Margolick JB. Rapid declines in total lymphocyte counts and hemoglobin concentration prior to AIDS among HIV-1-infected men. Aids. 2003;17(14):2035-44.

Kim H-J, Yu B, Feuer EJ. Selecting the number of change-points in segmented line regression. Statistica Sinica. 2009;19(2):597.

Zhang J, Lafta RL, Tao X, Li Y, Chen F, Luo Y, et al. Coupling a fast fourier transformation with a machine learning ensemble model to support recommendations for heart disease patients in a telehealth environment. IEEE Access. 2017;5:10674-85.

Younce JR, Campbell MC, Perlmutter JS, Norris SA. Thalamic and ventricular volumes predict motor response to deep brain stimulation for Parkinson's disease. Parkinsonism & related disorders. 2019;61:64-9.

Yan X, Su X. Linear regression analysis: theory and computing: World Scientific; 2009.

Ozaktas HM, Kutay MA, editors. The fractional Fourier transform. 2001 European Control Conference (ECC); 2001: IEEE.

Boche H, Pohl V. On approximations for functions in the space of uniformly convergent Fourier series. Journal of Approximation Theory. 2020;249:105307.




DOI: http://dx.doi.org/10.30699/fhi.v9i1.221

Refbacks

  • There are currently no refbacks.