In Signal, image and video processing
The need for healthcare equipment has increased due to the COVID-19 outbreak. Forecasting of these demands allows states to use their resources effectively. Artificial intelligence-based forecasting models play an important role in the forecasting of medical equipment demand during infectious disease periods. In this study, a deep model approach is presented, which is based on a multilayer long short-term memory network for forecasting of medical equipment demand and outbreak spreading, during the coronavirus outbreak (COVID-19). The proposed model consists of stages: normalization, deep LSTM networks and dropout-dense-regression layers, in order of process. Firstly, the daily input data were subjected to a normalization process. Afterward, the multilayer LSTM network model, which was a deep learning approach, was created and then fed into a dropout layer and a fully connected layer. Finally, the weights of the trained model were used to predict medical equipment demand and outbreak spreading in the following days. In experimental studies, 77-day COVID-19 data collected from the statistics data put together in Turkey were used. In order to test the proposed system, the data belonging to last 9 days of this data set were used and the performance of the proposed system was calculated using statistical algorithms, MAPE and R2. As a result of the experiments carried out, it was observed that the proposed model could be used to estimate the number of cases and medical equipment demand in the future in relation to COVID-19 disease.
Koç Erdinç, Türkoğlu Muammer
COVID-19, Deep LSTM network, Demand forecasting, Pandemic plan, Supply chain