Forecasting medical waste generation using short and extra short datasets: Case study of Lithuania

What is it about?

The aim of the study is to evaluate the performance of various mathematical modelling methods, while forecasting medical waste generation using Lithuania’s annual medical waste data. Only recently has a hazardous waste collection system that includes medical waste been created and therefore the study access to gain large sets of relevant data for its research has been somewhat limited. According to data that was managed to be obtained, it was decided to develop three short and extra short datasets with 20, 10 and 6 observations. Spearman’s correlation calculation showed that the influence of independent variables, such as visits at hospitals and other medical institutions, number of children in the region, number of beds in hospital and other medical institutions, average life expectancy and doctor’s visits in that region are the most consistent and common in all three datasets. Tests on the performance of artificial neural networks, multiple linear regression, partial least squares, support vector machines and four non-parametric regression methods were conducted on the collected datasets. The best and most promising results were demonstrated by generalised additive (R2 = 0.90455) in the regional data case, smoothing splines models (R2 = 0.98584) in the long annual data case and multilayer feedforward artificial neural networks in the short annual data case (R2 = 0.61103).

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Why is it important?

This type of research and mathematical modelling is mostly done with large data sets. These calculations and mathematical modelling are mostly done with solid municipal waste. It is rather new approach for hazardous (medical) waste.