Comparative study of machine learning methods for COVID-19 transmission forecasting.

Within the recent pandemic, scientists and clinicians are engaged in seeking new technology to stop or slow down the COVID-19 pandemic. The benefit of machine learning, as an essential aspect of artificial intelligence, on past epidemics offers a new line to tackle the novel Coronavirus outbreak. Accurate short-term forecasting of COVID-19 spread plays an essential role in improving the management of the overcrowding problem in hospitals and enables appropriate optimization of the available resources (i.

A note on tools for prediction under uncertainty and identifiability of SIR-like dynamical systems for epidemiology.

We provide an overview of the methods that can be used for prediction under uncertainty and data fitting of dynamical systems, and of the fundamental challenges that arise in this context. The focus is on SIR-like models, that are being commonly used when attempting to predict the trend of the COVID-19 pandemic. In particular, we raise a warning flag about identifiability of the parameters of SIR-like models; often, it might be hard to infer the correct values of the parameters from data, even for very simple models, making it non-trivial to use these models for meaningful predictions.

Forecasting emergency department overcrowding: A deep learning framework.

As the demand for medical cares has considerably expanded, the issue of managing patient flow in hospitals and especially in emergency departments (EDs) is certainly a key issue to be carefully mitigated. This can lead to overcrowding and the degradation of the quality of the provided medical services. Thus, the accurate modeling and forecasting of ED visits are critical for efficiently managing the overcrowding problems and enable appropriate optimization of the available resources.

Self-organization in aggregating robot swarms: A DW-KNN topological approach.

In certain swarm applications, where the inter-agent distance is not the only factor in the collective behaviours of the swarm, additional properties such as density could have a crucial effect. In this paper, we propose applying a Distance-Weighted K-Nearest Neighbouring (DW-KNN) topology to the behaviour of robot swarms performing self-organized aggregation, in combination with a virtual physics approach to keep the robots together. A distance-weighted function based on a Smoothed Particle Hydrodynamic (SPH) interpolation approach, which is used to evaluate the robot density in the swarm, is applied as the key factor for identifying the K-nearest neighbours taken into account when aggregating the robots.