Forecasting of medical equipment demand and outbreak spreading based on deep long short-term memory network: the COVID-19 pandemic in Turkey

Improved LSTM-based deep learning model for COVID-19 prediction using optimized approach

Individuals in any country are badly impacted both economically and physically whenever an epidemic of infectious illnesses breaks out. A novel coronavirus strain was responsible for the outbreak of the coronavirus sickness in 2019. Corona Virus Disease 2019 (COVID-19) is the name that the World Health Organization (WHO) officially gave to the pneumonia that was caused by the novel coronavirus on February 11, 2020. The use of models that are informed by machine learning is currently a major focus of study in the field of improved forecasting. By displaying annual trends, forecasting models can be of use in performing impact assessments of potential outcomes. In this paper, proposed forecast models consisting of time series models such as long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), generalized regression unit (GRU), and dense-LSTM have been evaluated for time series prediction of confirmed cases, deaths, and recoveries in 12 major countries that have been affected by COVID-19. Tensorflow1.0 was used for programming. Indices known as mean absolute error (MAE), root means square error (RMSE), Median Absolute Error (MEDAE) and r2 score are utilized in the process of evaluating the performance of models. We presented various ways to time-series forecasting by making use of LSTM models (LSTM, BiLSTM), and we compared these proposed methods to other machine learning models to evaluate the performance of the models. Our study suggests that LSTM based models are among the most advanced models to forecast time series data.

A new RNN based machine learning model to forecast COVID-19 incidence, enhanced by the use of mobility data from the bike-sharing service in Madrid

As a respiratory virus, COVID-19 propagates based on human-to-human interactions with positive COVID-19 cases. The temporal evolution of new COVID-19 infections depends on the existing number of COVID-19 infections and the people's mobility. This article proposes a new model to predict upcoming COVID-19 incidence values that combines both current and near-past incidence values together with mobility data. The model is applied to the city of Madrid (Spain). The city is divided into districts. The weekly COVID-19 incidence data per district is used jointly with a mobility estimation based on the number of rides reported by the bike-sharing service in the city of Madrid (BiciMAD). The model employs a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) to detect temporal patterns for COVID-19 infections and mobility data, and combines the output of the LSTM layers into a dense layer that can learn the spatial patterns (the spread of the virus between districts). A baseline model that employs a similar RNN but only based on the COVID-19 confirmed cases with no mobility data is presented and used to estimate the model gain when adding mobility data. The results show that using the bike-sharing mobility estimation the proposed model increases the accuracy by 11.7% compared with the baseline model.

Combating Covid-19 using machine learning and deep learning: Applications, challenges, and future perspectives

COVID-19, a worldwide pandemic that has affected many people and thousands of individuals have died due to COVID-19, during the last two years. Due to the benefits of Artificial Intelligence (AI) in X-ray image interpretation, sound analysis, diagnosis, patient monitoring, and CT image identification, it has been further researched in the area of medical science during the period of COVID-19. This study has assessed the performance and investigated different machine learning (ML), deep learning (DL), and combinations of various ML, DL, and AI approaches that have been employed in recent studies with diverse data formats to combat the problems that have arisen due to the COVID-19 pandemic. Finally, this study shows the comparison among the stand-alone ML and DL-based research works regarding the COVID-19 issues with the combinations of ML, DL, and AI-based research works. After in-depth analysis and comparison, this study responds to the proposed research questions and presents the future research directions in this context. This review work will guide different research groups to develop viable applications based on ML, DL, and AI models, and will also guide healthcare institutes, researchers, and governments by showing them how these techniques can ease the process of tackling the COVID-19.

Applications of deep learning into supply chain management: a systematic literature review and a framework for future research

In today’s complex and ever-changing world, Supply Chain Management (SCM) is increasingly becoming a cornerstone to any company to reckon with in this global era for all industries. The rapidly growing interest in the application of Deep Learning (a class of machine learning algorithms) in SCM, has urged the need for an up-to-date systematic review on the research development. The main purpose of this study is to provide a comprehensive vision by reviewing a set of 43 papers about applications of Deep Learning (DL) methods to the SCM, as well as the trends, perspectives, and potential research gaps. This review uses content analysis to answer three research questions namely: 1- What SCM problems have been solved by the use of DL techniques? 2- What DL algorithms have been used to solve these problems? 3- What alternative algorithms have been used to tackle the same problems? And do DL outperform these methods and through which evaluation metrics? This review also responds to this call by developing a conceptual framework in a value-adding perspective that provides a full picture of areas on where and how DL can be applied within the SCM context. This makes it easier to identify potential applications to corporations, in addition to potential future research areas to science. It might also provide businesses a competitive advantage over their competitors by allowing them to add value to their data by analyzing it quickly and precisely.

On Comparing Cross-Validated Forecasting Models with a Novel Fuzzy-TOPSIS Metric: A COVID-19 Case Study

Time series cross-validation is a technique to select forecasting models. Despite the sophistication of cross-validation over single test/training splits, traditional and independent metrics, such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), are commonly used to assess the model’s accuracy. However, what if decision-makers have different models fitting expectations to each moment of a time series? What if the precision of the forecasted values is also important? This is the case of predicting COVID-19 in Amapá, a Brazilian state in the Amazon rainforest. Due to the lack of hospital capacities, a model that promptly and precisely responds to notable ups and downs in the number of cases may be more desired than average models that only have good performances in more frequent and calm circumstances. In line with this, this paper proposes a hybridization of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and fuzzy sets to create a similarity metric, the closeness coefficient (CC), that enables relative comparisons of forecasting models under heterogeneous fitting expectations and also considers volatility in the predictions. We present a case study using three parametric and three machine learning models commonly used to forecast COVID-19 numbers. The results indicate that the introduced fuzzy similarity metric is a more informative performance assessment metric, especially when using time series cross-validation.

Using a Process Approach to Pandemic Planning: A Case Study

The purpose of this article was to demonstrate the difference between a pandemic plan’s textual prescription and its effective processing using graphical notation. Before creating a case study of the Business Process Model and Notation (BPMN) of the Czech Republic’s pandemic plan, we conducted a systematic review of the process approach in pandemic planning and a document analysis of relevant public documents. The authors emphasized the opacity of hundreds of pages of text records in an explanatory case study and demonstrated the effectiveness of the process approach in reengineering and improving the response to such a critical situation. A potential extension to the automation and involvement of SMART technologies or process optimization through process mining techniques is presented as a future research topic.