A simple mathematical model for the evaluation of the long first wave of the COVID-19 pandemic in Brazil

Abstract

We propose herein a mathematical model to predict the COVID-19 evolution and evaluate the impact of governmental decisions on this evolution, attempting to explain the long duration of the pandemic in the 26 Brazilian states and their capitals well as in the Federative Unit. The prediction was performed based on the growth rate of new cases in a stable period, and the graphics plotted with the significant governmental decisions to evaluate the impact on the epidemic curve in each Brazilian state and city. Analysis of the predicted new cases was correlated with the total number of hospitalizations and deaths related to COVID-19. Because Brazil is a vast country, with high heterogeneity and complexity of the regional/local characteristics and governmental authorities among Brazilian states and cities, we individually predicted the epidemic curve based on a specific stable period with reduced or minimal interference on the growth rate of new cases. We found good accuracy, mainly in a short period (weeks). The most critical governmental decisions had a significant temporal impact on pandemic curve growth. A good relationship was found between the predicted number of new cases and the total number of inpatients and deaths related to COVID-19. In summary, we demonstrated that interventional and preventive measures directly and significantly impact the COVID-19 pandemic using a simple mathematical model. This model can easily be applied, helping, and directing health and governmental authorities to make further decisions to combat the pandemic.

Poor prognosis indicators of type-2 diabetic COVID-19 patients

“…In late February 2020, the disease was first detected in São Paulo, SP, Brazil, and spread throughout the country after a few weeks ( 1 , 2 ). Brazil became the pandemic center in Latin America in May 2020 ( 3 - 7 ). The country had more than 29.1 million confirmed cases and 652,829 deaths (2.24% mortality rate) on March 10, 2022.…”

Stability Analysis of Delayed COVID-19 Models

“…We know that the characteristic Equation ( 10) has three real negative roots y 1 = −b, y 2 = −α − b, and y 3 = −λ − b. Thus, we need to check if the remaining roots of q(y) := y 2 + Λ 1 y + Λ 2 (y) (13) have negative real parts. It is easy to see that q(0) = Λ 2 (0) < 0 because we are assuming R 0 > 1.…”

“…Since the beginning of the COVID-19 pandemic, researchers proposed different and complementary mathematical models that describe, approximately, the spread of SARS-CoV-2 in different regions of the world and with alternative modeling techniques; see, e.g., [8][9][10][11][12][13][14]. Although the literature dealing with models of COVID-19 is now huge, with special issues [15], books [16], and review papers [17] on this topic, deterministic models of COVID-19 with delay differential equations and vaccination are relatively scarce [18].…”

Comparativo De Óbitos, Internações E Letalidade Entre 2019 E 2020 Para Causas Selecionadas No Brasil: Um Estudo Do Possível Impacto Da Pandemia Pelo Novo Coronavírus

“…A pandemia de Covid-19 foi decretada pela Organização Mundial da Saúde (OMS) em março de 2020 (OMS, 2020), e seus efeitos devastadores têm impactado o mundo em múltiplos setores. No Brasil, suas consequências, bem como sua longa duração comparativamente a outros países, perpassam aspectos econômicos, políticos, sociais, educacionais e de saúde (Tang et al, 2021). Lidar com o vírus SARS-CoV-2 e a doença por ele causada, trouxe inúmeros desafios ao sistema de saúde, desde a vigilância e atenção básica até os serviços de alta complexidade.…”