Prediction of the morbidity and mortality rates of COVID-19 in Egypt using non-extensive statistics

被引:1
|
作者
Yassin, Hayam [1 ]
Elyazeed, Eman R. Abo R. [1 ]
机构
[1] Ain Shams Univ, Fac Women Arts Sci & Educ, Phys Dept, Cairo 11577, Egypt
关键词
MODEL;
D O I
10.1038/s41598-023-36959-8
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Non-extenstive statistics play a significant role in studying the dynamic behaviour of COVID-19 to assist epidemiological scientists to take appropriate decisions about pandemic planning. Generic non-extensive and modified-Tsallis statistics are used to analyze and predict the morbidity and mortality rates in future. The cumulative number of confirmed infection and death in Egypt at interval from 4 March 2020 till 12 April 2022 are analyzed using both non-extensive statistics. Also, the cumulative confirmed data of infection by gender, death by gender, and death by age in Egypt at interval from 4 March 2020 till 29 June 2021 are fitted using both statistics. The best fit parameters are estimated. Also, we study the dependence of the estimated fit parameters on the people gender and age. Using modified-Tsallis statistic, the predictions of the morbidity rate in female is more than the one in male while the mortality rate in male is greater than the one in female. But, within generic non-extensive statistic we notice that the gender has no effect on the rate of infections and deaths in Egypt. Then, we propose expressions for the dependence of the fitted parameters on the age. We conclude that the obtained fit parameters depend mostly on the age and on the type of the statistical approach applied and the mortality risk increased with people aged above 45 years. We predict - using modified-Tsallis - that the rate of infection and death in Egypt will begin to decrease till stopping during the first quarter of 2025.
引用
收藏
页数:12
相关论文
共 50 条
  • [21] ARTIFICIAL INTELLIGENCE TECHNIQUES IN PREDICTION OF COVID-19 MORTALITY AND ITS RELATED FACTORS: A MULTI-CENTER STUDY
    Payandeh, Abolfazl
    Esmaily, Habibollah
    Salehi, Masoud
    Jahanshahi, Seyed Mahdi Amir
    Borzu, Zahra Arab
    Bolouri, Ahmad
    ADVANCES AND APPLICATIONS IN STATISTICS, 2023, 90 (01) : 71 - 87
  • [22] Mortality Prediction with Machine Learning in COVID-19 Patients in Intensive Care Units: A Retrospective and Prospective Longitudinal Study
    Yildirim, Suleyman
    Sunecli, Onur
    Kirakli, Cenk
    JOURNAL OF CRITICAL & INTENSIVE CARE, 2024, 15 (01): : 30 - 36
  • [23] Prognostic significance of chest CT severity score in mortality prediction of COVID-19 patients, a machine learning study
    Zakariaee, Seyed Salman
    Abdi, Aza Ismail
    Naderi, Negar
    Babashahi, Mashallah
    EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE, 2023, 54 (01)
  • [24] Prediction of mortality risk and duration of hospitalization of COVID-19 patients with chronic comorbidities based on machine learning algorithms
    Amiri, Parastoo
    Montazeri, Mahdieh
    Ghasemian, Fahimeh
    Asadi, Fatemeh
    Niksaz, Saeed
    Sarafzadeh, Farhad
    Khajouei, Reza
    DIGITAL HEALTH, 2023, 9
  • [25] COVID-19 and Non-COVID-19 Classification using Multi-layers Fusion From Lung Ultrasound Images
    Muhammad, Ghulam
    Hossain, M. Shamim
    INFORMATION FUSION, 2021, 72 : 80 - 88
  • [26] COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach
    Zivkovic, Miodrag
    Bacanin, Nebojsa
    Venkatachalam, K.
    Nayyar, Anand
    Djordjevic, Aleksandar
    Strumberger, Ivana
    Al-Turjman, Fadi
    SUSTAINABLE CITIES AND SOCIETY, 2021, 66 (66)
  • [27] An Efficient COVID-19 Disease Outbreak Prediction Using BI-SSOA-TMLPNN and ARIMA
    Sasikala, P.
    Sheela, L. Mary Immaculate
    INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS, 2024, 24 (06)
  • [28] Classifying the Mortality of People with Underlying Health Conditions Affected by COVID-19 Using Machine Learning Techniques
    Mohammad, RamiMustafa A.
    Aljabri, Malak
    Aboulnour, Menna
    Mirza, Samiha
    Alshobaiki, Ahmad
    APPLIED COMPUTATIONAL INTELLIGENCE AND SOFT COMPUTING, 2022, 2022
  • [29] Bayesian Spatio-Temporal Prediction and Counterfactual Generation: An Application in Non-Pharmaceutical Interventions in COVID-19
    Lawson, Andrew
    Rotejanaprasert, Chawarat
    VIRUSES-BASEL, 2023, 15 (02):
  • [30] Prediction of prognosis in COVID-19 patients using machine learning: A systematic review and meta-analysis
    Chen, Ruiyao
    Chen, Jiayuan
    Yang, Sen
    Luo, Shuqing
    Xiao, Zhongzhou
    Lu, Lu
    Liang, Bilin
    Liu, Sichen
    Shi, Huwei
    Xu, Jie
    INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS, 2023, 177