Prediction of Biomedical Waste Generation in Sanitary Emergencies for Urban Regions Using Multivariate Recurrent Neural Networks

Biomedical waste (BMW) generation is severely affected by generalized sanitary emergencies such as epidemics, as shown recently during the COVID-19 pandemic. These sanitary emergencies often increase plastic use in personal protection items, single-use plastics, and other healthcare elements. This increase might surpass the capacity of the waste management mechanism of a specific region, leading to a potential increase in its population health risks. Predicting the trends of BMW generation is not straightforward because it depends on several variables associated with the local health system and the health emergency status. However, a substantial amount of work has been done in epidemics modelling. Our main hypothesis is that BMW generation is strongly associated with sanitary emergencies dynamics. We propose a simulation framework that uses historical data from an ongoing sanitary emergency to build a model that can predict BMW generation trends in urban regions of developing countries.