We review epidemiological models for the propagation of the COVID-19 pandemic during the early months of the outbreak: from February to May 2020. The aim is to propose a methodological review that highlights the following characteristics: (i) the epidemic propagation models, (ii) the modeling of intervention strategies, (iii) the models and estimation procedures of the epidemic parameters and (iv) the characteristics of the data used. We finally selected 80 articles from open access databases based on criteria such as the theoretical background, the reproducibility, the incorporation of interventions strategies, etc. It mainly resulted to phenomenological, compartmental and individual-level models. A digital companion including an online sheet, a Kibana interface and a markdown document is proposed. Finally, this work provides an opportunity to witness how the scientific community reacted to this unique situation.
This paper considers predictive maintenance, which is the task of predicting rare and anomalous events (typically, system failures) using event logs data, which are series of timestamped symbolic codes emitted at regular or irregular intervals by a monitored system. Our objective is to find small sets of codes (called itemsets or patterns) that occur shortly before failures. Current prediction methods either produce patterns at a high computational cost or resort to kernel approaches which are often difficult to interpret. We introduce Bayesian Pattern Feature Discovery (BPFD), a new generic algorithm for pattern discovery. Our method, based on a pattern mining technique, produces informative and explainable features and is computationally efficient. The performance of BPFD is highlighted on real-world data sets, showing that enriching the feature space with the discovered patterns improves significantly the prediction power of a broad range of predictors and offers useful insight on the predictive maintenance task.
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