Quantitative forecast of listeriosis - Forecast of swiss listeriosis incidence based on food categories-relationship by arima and artificial neuronal network models
2006
Kunzi, S. | Muehlemann, M. | Schaellibaum, M. (Agroscope Liebefeld-Posieux (ALP), Posieux (Switzerland))
The aim of the present study was to forecast Swiss listeriosis incidence based on food categories contamination. Health authorities might use forecasting as early alert system and become aware of potential health threats in time. Preventive control measures might be implemented before the expected onset of an epidemic. Technically, the cases of listeriosis were forecasted with Artificial Neuronal Network and ARIMA models from static (relative distribution of contamination within food categories is assumed to remain constant) distributions of contamination within four food categories: dairy, meat, fish and other products. However, in reality distributions of contamination are not constant trough time and space. Therefore, the forecasted figures were subsequently simulated in connection to the dynamic relative distributions of contamination within food categories generated by means of Monte Carlo simulation (@Risk software, Palisade Corp.) from Beta distributions. The confidence interval of listeriosis cases related to each food category and age group will depend on their relative distributions from Beta distribution. Results showed that the Swiss population enters a decreasing phase of listeriosis cases 2004 (-30%) and 2005 (-9%) at the basis of 2003. Consumption of food of categories others and dairy products represents the highest and lowest risk, respectively. Population groups at high risk (reference is the age group population) are elderly people and perinatals (0 year old). At most, negligible risk might mathematically be related to the consumption of Swiss hard cheese.
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