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Mathematical models have been remarkable tools for knowing in advance the appropriate time to enforce population restrictions and distribute hospital resources. Here, we present a mathematical Susceptible-Exposed-Infectious-Recovered (SEIR) model to study the transmission dynamics of COVID-19 in Granada, Spain, taking into account the uncertainty of the phenomenon. In the model, the patients moving throughout the hospital's departments (intra-hospitalary circuit) are considered in order to help to optimize the use of a hospital's resources in the future. Two main seasons, September–April (autumn-winter) and May–August (summer), where the hospital pressure is significantly different, have been included. The model is calibrated and validated with data obtained from the hospitals in Granada. Possible future scenarios have been simulated. The model is able to capture the history of the pandemic in Granada. It provides predictions about the intra-hospitalary COVID-19 circuit over time and shows that the number of infected is expected to decline continuously from May without an increase next autumn–winter if population measures continue to be satisfied. The model strongly suggests that the number of infected cases will reduce rapidly with aggressive vaccination policies. The proposed study is being used in Granada to design public health policies and perform wise re-distribution of hospital resources in advance. [ABSTRACT FROM AUTHOR]

Abstract: Extreme behavior is produced by small groups, but affects a large amount of people. The fear, the usual strategy of these groups, influences the decisions of the whole population. In this paper we propose a dynamical mathematical model to study the ideological evolution of the population in a region where some groups want to get political goals through violence. After a classification of the subpopulations using data from votes obtained by political parties in general elections, model parameters have been estimated and future tendencies are studied. [Copyright &y& Elsevier]