How can infectious medical waste be forecasted and transported during the COVID-19 pandemic? A hybrid two-stage method.

The COVID-19 pandemic has caused an unforeseen collapse of infectious medical waste (IMW) and an abrupt smite of the conveying chain. Hospitals and related treatment centers face great challenges during the pandemic because mismanagement may lead to more severe life threats and enlarge environmental pollution. Opportune forecasting and transportation route optimization, therefore, are crucial to coping with social stress meritoriously. All related hospitals and medical waste treatment centers (MWTCs) should make decisions in perspective to reduce the economic pressure and infection risk immensely. This study proposes a hybrid dynamic method, as follows: first to forecast confirmed cases via infectious disease modeling and analyze the association between IMW outflows and cases; next to construct a model through time-varying factors and the lagging factor to predict the waste quantity; and then to optimize the transportation network route from hospitals to MWTCs. For demonstration intentions, the established methodology is employed to an illustrative example. Based on the obtained results, in finding the process of decision making, cost becomes the common concern of decision-makers. Actually, the infection risk among publics has to be considered simultaneously. Therefore, realizing early warning and safe waste management has an immensely positive effect on epidemic stabilization and lifetime health.

Prediction of C&D, Grit, Asphalt and Treated Biomedical Wastes During COVID-19 Using Grey Model

Waste management has been recognized as a real issue in the current situation due to the COVID-19 impact on people’s lifestyles. Therefore, serious actions need to be taken to control and manage this impact on the environment. One of these important environmental programs is the investigation and research of generated wastes during the pandemic. Due to the COVID-19 pandemic, the types and amounts of waste generation have changed, therefore a way forward to reduce this impact is to investigate the data that coming from landfill to devise an appropriate approach. The goal of this study is to predict the amount of construction and demolition (C&D), Grit, Asphalt waste, and Treated Biomedical waste (TBW) before, during, and after pandemic using grey systems theory. The grey model is a relatively new forecasting method that has been employed for prediction in a small amount of data and is also used for uncertain systems. In this study, the data coming from Regina landfill is used to predict the amount of wastes generated during the pandemic using the grey model. The results will be compared with the existing regression-based waste model. Different measures like mean absolute percent error (MAPE) and root mean square error (RMSE) will be used to compare and evaluate the performance of these models. Finally, the best forecasting model will be chosen to predict the amount of waste generation for the future generation. © 2023, Canadian Society for Civil Engineering.