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

Abstract

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.