Download This PaperCity-Scale Model for COVID-19 Epidemiology with Mobility and Social Activities Represented by a Set of Hidden Markov Models
23 Pages Posted: 18 Dec 2021
Abstract
Background and Objective: By the end of 2020 Sars-Cov-2 emerged and due to its rapid spread it has become a global pandemic. Various outbreaks of the disease in different parts of the world have been studied and their epidemiological analysis has been useful to develop models with the aim of tracking and predicting the spread of epidemics. 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.
Methods: An agent-based model has been developed. 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. 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 a proportion of asymptomatic infected people.
Results: The model predicts a daily evolution of the number of people hospitalized in intensive care due to COVID-19. In addition, the number of deaths, reported cases, asymptomatic individuals and other epidemiological variables of interest, discriminated by age range, are considered. As a case study, the model was applied to Paraná city in the second half of 2020 and the results are presented
Conclusions: It can be concluded that the model can be used to predict the most likely evolution of the number of cases and number of beds to be occupied in the short term. 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.
Note:
Funding: This work has been funded by the Agencia Nacional de Promoción de la Investigación, el Desarrollo y la Innovación (National Agency for the Promotion of Research, Development and Innovation), part of the Ministry of Science and Technology of Argentina through project IP 362 of the Coronavirus Priority line.
Declaration of Interests: The authors have no conflicts of interest to declare.
Keywords: Agent based model, Hidden Markov model, COVID-19, epidemiology, georeferencing, virus transmission, virus spread
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