City-scale model for COVID-19 epidemiology with mobility and social activities represented by a set of hidden Markov models.

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.

City-scale model for COVID-19 epidemiology with mobility and social activities represented by a set of Hidden Markov Models / Modelo a escala ciudad de la epidemiología de COVID-19 con movilidad de personas y sus actividades representadas por un conjunto de Modelos Ocultos de Márkov

In this paper, an agent-based model that predicts a daily evolution of the number of people hospitalized in intensive care due to COVID-19 is presented, including results for 2020. In addition, the number of deaths, reported cases, asymptomatic individuals and other epidemiological variables of interest, discriminated by age range, are considered. The most relevant characteristics of the climate in Paraná city (Entre Ríos,  Argentina), its social dynamics and public transportation are considered as inputs, taking also into account the different phases of isolation and social distancing. By means of a set of Hidden Markov Models, the system reproduces virus transmission associated with people's mobility and activities in the city. 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 a proportion of asymptomatic infected people. By adjusting the model to match the data on hospitalizations in intensive care units and deaths due to COVID-19 in the city under study, the system can be operated to analyze the impact of isolation and social distancing measures on the population dynamics. In addition, it allows simulating combinations of characteristics leading to a potential collapse in the health system due to lack of infrastructure, as well as predicting the impact of social events or the increase in people's mobility.

En este artículo se presenta un modelo que predice la evolución semanal de la cantidad de internados con COVID-19 en terapia intensiva, mostrando resultados durante el transcurso de 2020. Además devuelve la cantidad de fallecidos, casos reportados, asintomáticos y otras variables epidemiológicas de interés, discriminadas por rango etario. Para esto se tienen en cuenta como entradas las características más relevantes del clima de la ciudad de Paraná, su dinámica social y del transporte público de pasajeros, considerando las diferentes fases de aislamiento y distanciamiento. El modelo reproduce la transmisión del virus asociado a los desplazamientos y actividades de las personas dentro de la ciudad, mediante un conjunto de Modelos Ocultos de Markov. A su vez, se simula la propagación del virus en el huésped siguiendo las etapas de la enfermedad, asumiendo la existencia de comorbilidades y de una proporción de infectados asintomáticos. Al ajustar el modelo propuesto con los datos de internados en terapia intensiva y fallecidos por COVID-19 en la ciudad en estudio, el mismo permite ser operado para analizar el impacto de las características del aislamiento y distanciamiento social en la dinámica de la población y predecir el número de internados y muertes por COVID-19. Además, permite simular combinaciones de las características que llevarían a un potencial colapso del sistema de salud por falta de infraestructura, así como también predecir el impacto de eventos sociales o aumento de la movilidad de las personas.

Simplified sleep resistance test for daytime sleepiness detection.

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.

Recuperación de la frecuencia cardíaca post ejercicio en enfermos respiratorios crónicos

Las enfermedades respiratorias crónicas se asocian frecuentemente con disfunción autonómica cardíaca y esta se puede evaluar midiendo la recuperación de la frecuencia cardíaca (RFC) post ejercicio. Existen evidencias que la RFC calculada luego de un minuto de reposo después de la prueba de marcha de seis minutos (PM6M) puede predecir mortalidad y exacerbaciones agudas en pacientes con enfermedades respiratorias crónicas. El objetivo de este trabajo fue comparar la recuperación de la frecuencia cardíaca al finalizar una prueba del escalón de tres minutos de duración (PE3M) en enfermos respiratorios crónicos con la obtenida al finalizar la PM6M. La PE3M se realizó sin dificultad en un consultorio de reducidas dimensiones y todos los pacientes pudieron terminarlo. Aplicando el índice de correlación de Pearson se obtuvo como resultado 0,84 al comparar la RCF al minuto de detenerse en ambas pruebas lo que muestra una buena correlación entre ellas. En el gráfico de Bland Altman, se observa que los resultados están dentro de los límites de confianza mostrando concordancia a pesar de ser una muestra pequeña de pacientes. La PE3M se realiza con facilidad en un pequeño consultorio. Los resultados de la RCF después de esta prueba son comparables a los obtenidos al finalizar la PM6M.

Post-exercise Heart Rate Recovery in Patients with Chronic Respiratory Disease

Chronic respiratory diseases are frequently associated with cardiac autonomic dysfunction and this can be evaluated by measuring post-exercise heart rate recovery (HRR). There is evidence that the HRR calculated after one minute of rest following a Six-Minute Walk Test (6MWT) can predict mortality and acute exacerbations in patients with chronic respiratory diseases. The purpose of this study is to compare the heart rate recovery after finishing a Three-Minute Step Test (3MST) in chronic respiratory patients with that obtained after the 6MWT. The 3MST was performed without difficulty in a small doctor's office and all the patients were able to finish it. Applying the Pearson Correlation Index, the result was 0.84 when comparing the HRR one minute after stopping in both tests, showing a good correlation between them. In the Bland-Altman Plot, we can see that the results are within the confidence limits and show concordance despite being a small sample of patients. The 3MST is easily done in a small doctor's office. The results of the HRR after the 3MST are comparable to those obtained after finishing the 6MWT.