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.