Warning: fopen(/home/virtual/epih/journal/upload/ip_log/ip_log_2024-12.txt): failed to open stream: Permission denied in /home/virtual/lib/view_data.php on line 95 Warning: fwrite() expects parameter 1 to be resource, boolean given in /home/virtual/lib/view_data.php on line 96 A Forecasting Model for the Epidemic of Nationally Notifiable Communicable Diseases in Korea.
Skip Navigation
Skip to contents

Epidemiol Health : Epidemiology and Health

OPEN ACCESS
SEARCH
Search

Articles

Page Path
HOME > Epidemiol Health > Volume 22(2); 2000 > Article
Original Article 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
Epidemiol Health 2000;22(2):108-115
DOI: https://doi.org/
1Department of Biostatistics, The Catholic University ofKorea College of Medicine.
2Department of Preventive Medicine, The Catholic Universityof Korea College of Medicine.
3Department of Family Medicine, Korea University College ofMedicine.
prev next
  • 6,362 Views
  • 25 Download
  • 0 Crossref
  • 0 Scopus

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.


Epidemiol Health : Epidemiology and Health
TOP