Journal of the Korean Data and Information Science Society

한국데이터정보과학회 (The Korean Data and Information Science Society)
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식중독 발생 예측모형

Models for forecasting food poisoning occurrences

  • Yeo, In-Kwon (Department of Statistics, Sookmyung Women's University)
  • 투고 : 2012.09.26
  • 심사 : 2012.11.06
  • 발행 : 2012.11.30
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초록

식중독 발생에 대한 기존 연구에서는 기온과 습도와 같은 기후변수가 주된 설명변수로 취급되어 왔다. 이 논문에서는 주별 식중독 발생건수와 기후변수 간에 관계를 고찰하고 식중독 발생건수를 예측하기 위한 모형으로 포아송 회귀모형과 자기회귀이동평균모형을 비교한다. 비교결과 우리나라 식중독 발생은 시차를 두고 기후 변수에 영향을 많이 받고 있으나 식중독 발생 예측은 이들 변수보다 이전 시점의 식중독 발생 건수에 더 많이 영향을 받는 것으로 나타났으며 포아송 회귀모형은 예측의 관점에서 문제가 있음을 보였다.

The occurrence of food poisoning is usually modeled by meteorological variables like the temperature and the humidity. In this paper, we investigate the relationship between food poisoning occurrence and climate variables in Korea and compare Poisson regression and autoregressive moving average model to select the forecast model. We confirm that lagged climate variables affect the food poisoning occurrences. However, it turns out that, from the viewpoint of the prediction, the number of previous occurrences is more influential to the current occurrence than meteorological variables and Poisson regression model is less reliable.

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참고문헌

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피인용 문헌

  1. Prediction of the Number of Food Poisoning Occurrences by Microbes vol.26, pp.6, 2013, https://doi.org/10.5351/KJAS.2013.26.6.923