Citation: Domínguez-Morales, M.; Civit, A. Special Issue “Fighting COVID-19: Emerging Techniques and Aid Systems for Prevention, Forecasting and Diagnosis”. Appl. Sci. 2023, 13, 467. https://doi.org/ 10.3390/app13010467 Received: 26 December 2022 Accepted: 27 December 2022 Published: 29 December 2022 Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). applied sciences Editorial Special Issue “Fighting COVID-19: Emerging Techniques and Aid Systems for Prevention, Forecasting and Diagnosis” Manuel Domínguez-Morales 1,2, * and Antón Civit 1,2 1 Robotics and Computer Technology Lab, Universidad de Sevilla, 41012 Seville, Spain 2 Computer Engineering Research Institute (I3US), E.T.S. Ingeniería Informática, Universidad de Sevilla, Avda. Reina Mercedes s/n, 41012 Seville, Spain * Correspondence: mjdominguez@us.es Since its emergence at the end of 2019, the pandemic caused by the COVID-19 virus has led to multiple changes in health protocols around the world. This event has also given a major boost to the development and evolution of techniques and systems to aid in the prevention, forecasting and diagnosis of this disease. All these advances, beyond being applied to COVID-19 itself, have a broad impact on the systems developed for other diseases. This special issue aims to collect and present cutting-edge work on the evolution and trend of COVID-19, the application of Machine Learning-based techniques for disease diagnosis (either through images or time series), experimental studies related to the virus, and systems focused on helping to contain and prevent the spread of the virus. A total of 18 articles in various fields related to the topics listed above are included. Of these, the vast majority (17 articles) are research articles, while the remaining one is a literature review. The papers presented will be briefly described below (sorted by publication date): • Civit-Masot et al. [1] present a novel diagnostic-aid system based on a Convolutional Neural Network classifier to distinguish between Healthy, Pneumonia and COVID-19 patients using pulmonary x-ray images. The work obtains high accuracy results and provides one of the first imaging diagnostic-aid systems for COVID-19 in the world. • Duran-Lopez et al. [2] provide another Deep Learning classifier system based on x-ray pulmonary images (in this case for two classes: healthy and COVID-19), with the novelty of a pre-processing mechanism and heatmap visualization. • Hernández-Orallo et al. [3] evaluate the effectiveness of recently developed contact tracing smartphone applications for COVID-19 that rely on Bluetooth to detect contacts, studying how they work in order to model the main aspects that can affect their performance, including precision, utilization, tracing speed and implementation model. • Rezaei and Azarmi [4] develop a hybrid Computer Vision and YOLOv4-based Deep Neural Network model for automated people detection in crowds in indoor and outdoor environments using common CCTV security cameras. • Koziol et al. [5] present a susceptible-infected-recovered epidemic model for predicting the spread of COVID-19, studying the impact of fractional orders of the model derivatives on the dynamic properties of the proposed model. • Rahmadani and Lee [6] predict the spread of COVID-19 among populations and regions by providing an expansion of the susceptible–exposed–infected–recovered compartment model that considers human mobility among a number of regions. • Born et al. [7] present a novel lung ultrasound dataset for COVID-19 alongside new methods and analyses that pave the way towards computer-vision-assisted differential diagnosis of COVID-19 from the US. • Muñoz-Saavedra et al. [8] try to answer the following question: When training an image classification system with only two classes (healthy and sick), does this system extract the specific features of this disease, or does it only obtain the features that Appl. Sci. 2023, 13, 467. https://doi.org/10.3390/app13010467 https://www.mdpi.com/journal/applsci