City-scale model for COVID-19 epidemiology with mobility and social activities represented by a set of Hidden Markov Models

Abstract

In this paper, a model that predicts the weekly evolution of the number of people hospitalized with COVID-19 in intensive care during 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. For this purpose, the most relevant characteristics of the climate of the city of Paraná, its social dynamics and public transport are taken into account as inputs, considering the different phases of isolation and social distancing. By means of a set of Hidden Markov Models, the system reproduces the virus transmission associated with the movements and activities of people within the city. The spread of the virus in the host is also simulated by following the stages of the disease, assuming the existence of comorbidities and a proportion of asymptomatic infected. By adjusting the model to match the data on hospitalizations in intensive care and deaths from COVID-19 in the city under study, the system can be operated to analyze the impact of the characteristics of isolation and social distancing on the dynamics of the population and simulate the number of hospitalizations and deaths due to COVID-19. Further, it allows studying combinations of characteristics, so that the health system does not collapse due to lack of infrastructure, as well as predicting the impact of social events or increased mobility of people.