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Humanity has suffered throughout its history from different types of pandemics caused by deadly infectious diseases. Mathematical models are very useful tools to understand the dynamics of the behavior and propagation of these. Among these models are those known as SIS, SIR and SEIR. This paper presents a brief explanation of the structure of these models and its use in some pandemics. In particular, some SIR-type models without and with delays are analyzed with special interest, as well as their applications to the current pandemic caused by the SARS-CoV-2 virus. To determine the correspondence between some of the models analyzed and the data of Infectious and Recovered from Mexico reported by the World Health Organization (WHO), simulations are presented in three different time periods between the years 2020-2021. In addition, an unpublished SIR-type mathematical model with three delays is proposed as a main contribution. Finally, a discussion is given on the application of numerical algorithms to identify some parameters of mathematical models, such as ordinary least squares. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the publication of datasets collected via Google Dataset Search, specialized in families of RNA viruses, whose terminology was obtained from the National Cancer Institute (NCI) thesaurus developed by the US Department of Health and Human Services. The objective is to determine the scope and reuse capacity of the available data, determine the number of datasets and their free access, the proportion in reusable download formats, the main providers, their publication chronology, and to verify their scientific provenance. On the other hand, we also define possible relationships between the publication of datasets and the main pandemics that have occurred during the last 10 years. The results obtained highlight that only 52% of the datasets are related to scientific research, while an even smaller fraction (15%) are reusable. There is also an upward trend in the publication of datasets, especially related to the impact of the main epidemics, as clearly confirmed for the Ebola virus, Zika, SARS-CoV, H1N1, H1N5, and especially the SARS-CoV-2 coronavirus. Finally, it is observed that the search engine has not yet implemented adequate methods for filtering and monitoring the datasets. These results reveal some of the difficulties facing open science in the dataset field. [ABSTRACT FROM AUTHOR]

This paper analyses the news on Covid-19 and 2019-nCoV in the Spanish media. This is done by using news retrieved from My news database. The analysis includes the total amount of news in Spanish media by type of media; the evolution of the publication in digital media considering various periods of time, and the volume of news in the most used Spanish digital media. The results show that digital media are the most used among other types to publish on the topic, with a significant increase since the beginning of the alarm state. In this sense, some arguments on the function and utility of this type of media during the confinement are exposed. Also, the essential role of digital media covering the population’s information needs is discussed. The digital media with the largest number publications are abc.es, 20minutos. es, and elconfidencial.com. The value of this work is that it can serve as starting point for others in order to carry out content analysis on the subject considering different channels and digital media. [ABSTRACT FROM AUTHOR]