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BACKGROUND AND OBJECTIVE: SARS-CoV-2 emerged by the end of 2019 and became a global pandemic due to its rapid spread. Various outbreaks of the disease in different parts of the world have been studied, and epidemiological analyses of these outbreaks have been useful for developing models with the aim of tracking and predicting the spread of epidemics. In this paper, an agent-based model that predicts the local daily evolution of the number of people hospitalized in intensive care due to COVID-19 is presented. METHODS: An agent-based model has been developed, taking into consideration the most relevant characteristics of the geography and climate of a mid-size city, its population and pathology statistics, and its social customs and mobility, including the state of public transportation. In addition to these inputs, the different phases of isolation and social distancing are also taken into account. By means of a set of hidden Markov models, the system captures and reproduces virus transmission associated with the stochastic nature of people's mobility and activities in the city. The spread of the virus in the host is also simulated by following the stages of the disease and by considering the existence of comorbidities and the proportion of asymptomatic carriers. RESULTS: As a case study, the model was applied to Paraná city (Entre Ríos, Argentina) in the second half of 2020. The model adequately predicts the daily evolution of people hospitalized in intensive care due to COVID-19. This adequacy is reflected by the fact that the prediction of the model (including its dispersion), as with the data reported in the field, never exceeded 90% of the capacity of beds installed in the city. In addition, other epidemiological variables of interest, with discrimination by age range, were also adequately reproduced, such as the number of deaths, reported cases, and asymptomatic individuals. CONCLUSIONS: The model can be used to predict the most likely evolution of the number of cases and hospital bed occupancy in the short term. By adjusting the model to match the data on hospitalizations in intensive care units and deaths due to COVID-19, it is possible to analyze the impact of isolation and social distancing measures on the disease spread dynamics. In addition, it allows for simulating combinations of characteristics that would lead to a potential collapse in the health system due to lack of infrastructure as well as predicting the impact of social events or increases in people's mobility.

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OBJECTIVES: Excessive daytime sleepiness (EDS) is a highly prevalent symptom that increases the risk of traffic accidents and deteriorates the quality of life. The diagnosis of EDS is difficult because of the complex infrastructure that is required. The new test here proposed assesses the ability of a simple test of simplify the detection of daytime sleepiness compared with the OSLER test. MATERIAL AND METHODS: In the new test, during 20 minute subjects were asked to pass a finger by a groove in response to a light emitting diode, inside dark glasses, which was lit for 1s in every three, with headphones that reduce the ambient noise and was compared with the OSLER test on each subject in random order. RESULTS: The proposed method showed a sensitivity of 100% and a specificity of 61%, with a positive predictive value of 67% and negative predictive value of 100% when compared with the OSLER test. The value of area under the ROC curve was 0.81 (0.62-0.99), p=0.013. In a Bland-Altman plot, most of the latency times differences are in the 95% agreement interval (p=0.05). In addition, the confidence interval of the mean and most of the positive results are above the zero line. The Cohens Kappa coefficient obtained is 0.58 (95% CI 0.29-0.88). CONCLUSION: In this sample of patients, the proposed method detects EDS in a similar way as OSLER test and can be performed in different environments without requiring special infrastructure or expert personnel.