In December 2019, a group of people in Wuhan, China, were hospitalized and diagnosed with pneumonia of unknown etiology [1, 2]. Sequencing lower respiratory tract samples revealed an outbreak of the SARS-COV-2 virus worldwide. The World Health Organization (WHO) classified February 11, 2020, as an outbreak of Covid-19, and on March 11, WHO declared Coronavirus disease (COVID-19) as a pandemic [2, 3].
The disease caused by the severe acute respiratory syndrome novel coronavirus (SARS-CoV-2) has infected 511,479,320 people worldwide [3, 4]. According to the WHO, on May 2, 2022, the disease killed 6,238,832 people [4, 5]. Despite efforts to stop the transmission of COVID-19, the infection has spread across mainland China, and in less than months, the infection has spread to at least 114 countries, causing over 4,000 deaths [5, 6].
The coronavirus is one of the primary pathogens that target the human respiratory system and could pose a more significant threat to those living in a dearth of infrastructure and housing [5]. Then, this outbreak causes a greater risk to the population residing in cities with inadequate conditions of housing, sanitation, transport, and precariousness in health ser- vices [6, 7].
During the period that COVID-19 lasted, there was an excess of scientific and non- scientific information [8]. The temporal evolution of the number of infected individuals is not linear, whereas epidemiological dynamics have the probability of a susceptible individual ac- quiring infection, depending on the number of infections [7, 8, 9]. Furthermore, the prediction of disease epidemics is essential for controlling and relating contagious diseases in urban areas [10].
Previous studies have built mathematical models that describe the dynamics of infectious disease transmission, known as the Susceptible-Infectious-Remove (SIR) model, and fit the model to time-series data of the number of infected individuals [11, 12]. Diseases such as COVID-19 demonstrate the need for predictive model applications to implement early and precisely tuned responses to their profound impact on society and the city [13].
The city is a large human settlement defined as a permanent and densely established place by administrative boundaries. Cities generally have extensive housing, transportation, sanitation, utilities, land use, and communication systems [14, 15, 16]. However, the poor conditions of housing, sanitation, transport, and infrastructure in the urban environment of cities can generate several negative consequences for the health of the population [15, 16, 17].
Urban health is the study of environmental, social, physical, and infrastructure characteristics of urban resources, in which city-specific factors are incidentally related to
health, as shown in figure S1. Such factors can influence the individual's health and disease in urban areas [14, 16, 17].
The mathematical models of population dynamics, such as the differential equation, help understand physical phenomena. These models often generate an equation containing some derivatives of an unknown function [18].
Moreover, the Infection Dynamics and Health Remodelling Model, i.e., the SEIR in COVID-19 with peak infectivity before and at the onset of symptoms, explains the hidden accumulation of exposed individuals that challenges containment strategies due to delayed epidemic responses to non-pharmaceutical interventions. For this study, the objective is to use a mathematical model based on ordinary differential equations (SEIR), which models the dynamics of susceptible, exposed, infected, and removed between urban regions between Belo Horizonte and Rio de Janeiro.