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Original Article
National trends and projection of chronic kidney disease incidence according to etiology from 1990 to 2030 in Iran: a Bayesian age-period-cohort modeling study
Fatemeh Shahbazi, Amin Doosti-Irani, Alireza Soltanian, Jalal Poorolajal
Epidemiol Health. 2023;45:e2023027.   Published online February 17, 2023
DOI: https://doi.org/10.4178/epih.e2023027
  • 3,033 View
  • 146 Download
AbstractAbstract AbstractSummary PDFSupplementary Material
Abstract
OBJECTIVES
Chronic kidney disease (CKD) is a major public health problem worldwide. Predicting CKD incidence rates and case numbers at the national and global levels is vital for planning CKD prevention programs.
METHODS
Data on CKD incidence rates and case numbers in Iran from 1990 to 2019 were extracted from the Global Burden of Disease online database. The average annual percentage change was computed to determine the temporal trends in CKD age-standardized incidence rates from 1990 to 2019. A Bayesian age-period-cohort model was used to predict the CKD incidence rate and case numbers through 2030.
RESULTS
Nationally, CKD cases increased from 97,300 in 1990 to 315,500 in 2019. The age-specific CKD incidence rate increased from 168.52 per 100,000 to 382.98 per 100,000 during the same period. Between 2020 and 2030, the number of CKD cases is projected to rise to 423,300. The age-specific CKD incidence rate is projected to increase to 469.04 in 2030 (95% credible interval, 399.20 to 538.87). In all age groups and etiological categories, the CKD incidence rate is forecasted to increase by 2030.
CONCLUSIONS
CKD case numbers and incidence rates are anticipated to increase in Iran through 2030. The high level of CKD incidence in people with diabetes mellitus, hypertension, and glomerulonephritis, as well as in older people, suggests a deficiency of attention to these populations in current prevention plans and highlights their importance in future programs for the national control of CKD.
Summary
Key Message
Based on our findings, it is predicted that the number of chronic kidney patients in Iran will reach 423,300 people by 2030. Additionally, the age-specific incidence rate of chronic kidney disease (CKD) is projected to increase to 469.04 in the same year. The CKD incidence rate is forecasted to increase by 2030 in all age groups and etiological categories, including type 1 diabetes mellitus, type 2 diabetes mellitus, hypertension, glomerulonephritis, and other causes.
COVID-19: Health Statistics
Estimation of the reproduction number and early prediction of the COVID-19 outbreak in India using a statistical computing approach
Karthick Kanagarathinam, Kavaskar Sekar
Epidemiol Health. 2020;42:e2020028.   Published online May 9, 2020
DOI: https://doi.org/10.4178/epih.e2020028
  • 11,711 View
  • 292 Download
  • 12 Web of Science
  • 11 Crossref
AbstractAbstract PDF
Abstract
Coronavirus disease 2019 (COVID-19), which causes severe respiratory illness, has become a pandemic. The World Health Organization has declared it a public health crisis of international concern. We developed a susceptible, exposed, infected, recovered (SEIR) model for COVID-19 to show the importance of estimating the reproduction number (R<sub>0</sub>). This work is focused on predicting the COVID-19 outbreak in its early stage in India based on an estimation of R<sub>0</sub>. The developed model will help policymakers to take active measures prior to the further spread of COVID-19. Data on daily newly infected cases in India from March 2, 2020 to April 2, 2020 were to estimate R<sub>0</sub> using the earlyR package. The maximum-likelihood approach was used to analyze the distribution of R<sub>0</sub> values, and the bootstrap strategy was applied for resampling to identify the most likely R<sub>0</sub> value. We estimated the median value of R<sub>0</sub> to be 1.471 (95% confidence interval [CI], 1.351 to 1.592) and predicted that the new case count may reach 39,382 (95% CI, 34,300 to 47,351) in 30 days.
Summary

Citations

Citations to this article as recorded by  
  • REDACS: Regional emergency-driven adaptive cluster sampling for effective COVID-19 management
    M. Stehlík, J. Kiseľák, A. Dinamarca, E. Alvarado, F. Plaza, F.A. Medina, S. Stehlíková, J. Marek, B. Venegas, A. Gajdoš, Y. Li, S. Katuščák, A. Bražinová, E. Zeintl, Y. Lu
    Stochastic Analysis and Applications.2023; 41(3): 474.     CrossRef
  • Application of Mathematical Modeling in Prediction of COVID-19 Transmission Dynamics
    Ali AlArjani, Md Taufiq Nasseef, Sanaa M. Kamal, B. V. Subba Rao, Mufti Mahmud, Md Sharif Uddin
    Arabian Journal for Science and Engineering.2022; 47(8): 10163.     CrossRef
  • Interval type-2 fuzzy computational model for real time Kalman filtering and forecasting of the dynamic spreading behavior of novel Coronavirus 2019
    Daiana Caroline dos Santos Gomes, Ginalber Luiz de Oliveira Serra
    ISA Transactions.2022; 124: 57.     CrossRef
  • Implementation of a Heart Disease Risk Prediction Model Using Machine Learning
    K. Karthick, S. K. Aruna, Ravi Samikannu, Ramya Kuppusamy, Yuvaraja Teekaraman, Amruth Ramesh Thelkar, Deepika Koundal
    Computational and Mathematical Methods in Medicine.2022; 2022: 1.     CrossRef
  • Using logistic regression to develop a diagnostic model for COVID-19: A single-center study
    Raoof Nopour, Mostafa Shanbehzadeh, Hadi Kazemi-Arpanahi
    Journal of Education and Health Promotion.2022; 11(1): 153.     CrossRef
  • Comparison of the Effective Reproduction Number (Rt) Estimation Methods of COVID-19 Using Simulation Data Based on Available Data from Iran, USA, UK, India, and Brazil
    Ali Karamoozian, Abbas Bahrampour
    Journal of Research in Health Sciences.2022; 22(3): e00559.     CrossRef
  • Computational Approach For Real-Time Interval Type-2 Fuzzy Kalman Filtering and Forecasting via Unobservable Spectral Components of Experimental Data
    Daiana Caroline dos Santos Gomes, Ginalber Luiz de Oliveira Serra
    Journal of Control, Automation and Electrical Systems.2021; 32(2): 326.     CrossRef
  • Determination of the Most Important Diagnostic Criteria for COVID-19: A Step forward to Design an Intelligent Clinical Decision Support System
    Mostafa Shanbehzadeh, Raoof Nopour, Hadi kazemi-arpanahi
    Journal of Advances in Medical and Biomedical Research.2021; 29(134): 176.     CrossRef
  • Machine Learning Model for Computational Tracking and Forecasting the COVID-19 Dynamic Propagation
    Daiana Caroline dos Santos Gomes, Ginalber Luiz de Oliveira Serra
    IEEE Journal of Biomedical and Health Informatics.2021; 25(3): 615.     CrossRef
  • Comprehensive Study, Design and Economic Feasibility Analysis of Solar PV Powered Water Pumping System
    K. Karthick, K. Jaiganesh, S. Kavaskar
    Energy Engineering.2021; 118(6): 1887.     CrossRef
  • Transmission Dynamics of the COVID-19 Epidemic at the District Level in India: Prospective Observational Study
    Suman Saurabh, Mahendra Kumar Verma, Vaishali Gautam, Nitesh Kumar, Akhil Dhanesh Goel, Manoj Kumar Gupta, Pankaj Bhardwaj, Sanjeev Misra
    JMIR Public Health and Surveillance.2020; 6(4): e22678.     CrossRef
Original Articles
Forecasting and prediction of scorpion sting cases in Biskra province, Algeria, using a seasonal autoregressive integrated moving average model
Schehrazad Selmane, Mohamed L’Hadj
Epidemiol Health. 2016;38:e2016044.   Published online October 14, 2016
DOI: https://doi.org/10.4178/epih.e2016044
  • 13,104 View
  • 281 Download
  • 4 Crossref
AbstractAbstract PDF
Abstract
OBJECTIVES
The aims of this study were to highlight some epidemiological aspects of scorpion envenomations, to analyse and interpret the available data for Biskra province, Algeria, and to develop a forecasting model for scorpion sting cases in Biskra province, which records the highest number of scorpion stings in Algeria.
METHODS
In addition to analysing the epidemiological profile of scorpion stings that occurred throughout the year 2013, we used the Box-Jenkins approach to fit a seasonal autoregressive integrated moving average (SARIMA) model to the monthly recorded scorpion sting cases in Biskra from 2000 to 2012.
RESULTS
The epidemiological analysis revealed that scorpion stings were reported continuously throughout the year, with peaks in the summer months. The most affected age group was 15 to 49 years old, with a male predominance. The most prone human body areas were the upper and lower limbs. The majority of cases (95.9%) were classified as mild envenomations. The time series analysis showed that a (5,1,0)×(0,1,1)12 SARIMA model offered the best fit to the scorpion sting surveillance data. This model was used to predict scorpion sting cases for the year 2013, and the fitted data showed considerable agreement with the actual data.
CONCLUSIONS
SARIMA models are useful for monitoring scorpion sting cases, and provide an estimate of the variability to be expected in future scorpion sting cases. This knowledge is helpful in predicting whether an unusual situation is developing or not, and could therefore assist decision-makers in strengthening the province’s prevention and control measures and in initiating rapid response measures.
Summary

Citations

Citations to this article as recorded by  
  • Epidemiological aspects of scorpion stings in Algeria: A monocentric retrospective study
    Mohamed Amine Kerdoun
    Toxicologie Analytique et Clinique.2022; 34(1): 4.     CrossRef
  • Terrestrial venomous animals, the envenomings they cause, and treatment perspectives in the Middle East and North Africa
    Timothy P. Jenkins, Shirin Ahmadi, Matyas A. Bittenbinder, Trenton K. Stewart, Dilber E. Akgun, Melissa Hale, Nafiseh N. Nasrabadi, Darian S. Wolff, Freek J. Vonk, Jeroen Kool, Andreas H. Laustsen, Jean-Philippe Chippaux
    PLOS Neglected Tropical Diseases.2021; 15(12): e0009880.     CrossRef
  • Time Series Analysis of Tuberculosis in Medea Province in Algeria
    Mohamed L'HADJ, Schehrazad SELMANE
    Journal of Engineering Technology and Applied Sciences.2019; 4(2): 85.     CrossRef
  • Predictive determinants of scorpion stings in a tropical zone of south Iran: use of mixed seasonal autoregressive moving average model
    Vahid Ebrahimi, Esmael Hamdami, Mohammad Djaefar Moemenbellah-Fard, Shahrokh Ezzatzadegan Jahromi
    Journal of Venomous Animals and Toxins including Tropical Diseases.2017;[Epub]     CrossRef
Long-term prediction of gastric cancer mortality in Korea.
Jin Gwack, Yunhee Choi, Hai Rim Shin, Yun Chul Hong, Keun Young Yoo
Korean J Epidemiol. 2005;27(1):163-172.
  • 65,535 View
  • 23 Download
AbstractAbstract PDF
Abstract
PURPOSE
This study was carried out to predict the mortality rate for gastric cancer up to 2020 in Korea with forecasting model.
METHODS
The trends of the age-adjusted mortality rate was calculated from 1983 to 2003 using the mortality data of the past 20 years in Korea, and projected up to the year of 2020 with log-linear models for each gender. The number of deaths from gastric cancer was calculated from the predicted mortality rate.
RESULTS
Age-adjusted mortality rates for gastric cancer per 100,000 persons were 32.13 in 1983, 23.95 in 1990, and 15.99 in 2003 for women, and 70.37, 58.74, 41.04 for men, respectively. The expected age-adjusted mortality rates for gastric cancer were 16.50 in 2005, 14.27 in 2010, and 10.66 in 2020 for women, and 39.14, 33.83, 25.28 for men, respectively. In contrast to this decreasing trend, it is predicted that mortality rates for those aged 75 or over would increase steadily. The predicted number of deaths from gastric cancer was 6,519 for women and 13,743 for men in 2020.
CONCLUSIONS
This study suggests that gastric cancer mortality rate would decrease continuously except for some aged groups. The declining trends in gastric cancer mortality are regarded as a result of lifestyle changes, improvements in screening methods and treatments. Strategies for aged groups should be developed in order to control increasing mortality rates.
Summary
A Forecasting Model for the Epidemic of Nationally Notifiable Communicable Diseases in Korea.
Yonggyu Park, Hyoung Ah Kim, Kyung Hwan Cho, Euichul Shin, Kwang Ho Meng
Korean J Epidemiol. 2000;22(2):108-115.
  • 5,531 View
  • 25 Download
AbstractAbstract PDF
Abstract
PURPOSE
S: The authors derived two forecasting models which can be used as objective tools for detecting epidemics and predicting the future frequencies of communicable diseases.
METHODS
In this study, regression analysis using trigonometric functions, Box and Jenkins's seasonal ARIMA model were applied to the monthly accumulated data of five nationally notifiable communicable diseases from January 1987 to December 1998 in Korea.
RESULTS
Between two forecasting models, seasonal ARIMA model gives more precise predicted frequencies than regression model in the neighborhood of the current time points and future time, but the regression model is better in overall agreement between the predicted and observed frequencies during 7 years(1992-1998).
CONCLUSIONS
These forecasting models can be usefully applied in deciding and carrying out a national policy in preventing epidemics in the future, and graphic program is much helpful to understand the present status of disease occurrence.
Summary

Epidemiol Health : Epidemiology and Health