[en] Deaths from COVID-19 in Spain during the ‘first wave’: Quantitative data and its journalistic treatment

The COVID-19 pandemic has brought controversies regarding the quantification of deaths in many countries. Mainly, discussions were fuelled by the sudden change of the criteria being applied, the limited testing and tracing capacities, and the collapse of the healthcare system. This work analyzes the journalistic treatment for the case of Spain, which is one of the European countries with the highest number of cases and deaths during the ‘first wave’. Firstly, it provides a technical discussion about the coherence, traceability and limitations of available quantitative open data sources. Official data sources (in particular: the Ministry of Health, the Mortality Monitoring System (MoMo) of the National Epidemiology Center and the National Institute of Statistics (INE)) and nonofficial data sources are considered. Secondly, an analysis of the public discussion on these data is proposed through journalistic coverage by the main national newspapers. An amount of 700 informative pieces are considered and the most used data sources and the evolution of the number of pieces per newspaper are studied, in addition to offering a qualitative approach about the main topics of discussion. Finally, suggestions of improvement and future research are gathered, for the reliability of mortality data as a way to enhance learning and resilience for future crises, their journalistic treatment and their historical record. © 2022. Revista General de Informacion y Documentacion.All Rights Reserved

COVID-19 Severity Classification Using a Hierarchical Classification Deep Learning Model

One of the most important situations in recent years has been originated by the 2019 Coronavirus disease (COVID-19). Nowadays this disease continues to cause a large number of deaths and remains one of the main diseases in the world. In this disease is very important the early detection to avoid the spread, as well as to monitor the progress of the disease in patients, and techniques of artificial intelligence (AI) is very useful for this. This is where this work comes from, trying to contribute in the study to detect infected patients. Drawing inspiration from previous work, we studied the use of deep learning models to detect COVID-19 and classify the patients with this disease. The work was divided into three phases to detect, evaluate the percentage of infection and classify patients of COVID-19. The initial stage use CNN Densenet-161 models pre-trained to detects the COVID-19 using multi-class X-Ray images (COVID-19 vs. No-Findings vs. Pneumonia), obtaining 88.00% in accuracy, 91.3% in precision, 87.33% in recall, and 89.00% in F1-score. The next stage also use CNN Densenet-161 models pre-trained to evidenced the percentage of infection COVID-19 in the different CT-scans slices belonging to a patient, obtaining in the evaluation metrics a result of 0.95 in PC, 5.14 in MAE and 8.47 in RMSE. The last stage creates a database of histograms of different patients using their lung infections and classifies them into different degrees of severity using K-Means unsupervised learning algorithms with PCA. © 2022, Springer Nature Switzerland AG.

Survey data as a way to estimate the prevalence of COVID-19. a pilot study in the city of Madrid

Background: This research (EP-Covid19-Madrid) was inspired on the lack in the middle of March 2020 of data on COVID-19 produced from representative samples. Its goal was to evaluate the potential of interviewing such type of samples in order to assess the incidence and prevalence of epidemics as COVID-19.