Analysis of Covid-19 epidemic curves via generalized growth models: Case study for the cities of Recife and Teresina

• Autor(a) principal: Vasconcelos, Giovani L.
• Data de Publicação: 2020
• Outros Autores: Duarte-Filho, Gerson C., Brum, Arthur A., Ospina, Raydonal, Almeida, Francisco A. G., Macêdo, Antônio M. S.
• Tipo de documento: preprint
• Idioma: por
• Título da fonte: SciELO Preprints
• Texto Completo: https://preprints.scielo.org/index.php/scielo/preprint/view/690
• topic: Covid-19; Modelos epidemiológicos; Políticas de saúde pública; Modelos de crescimento; COVID-19; Epidemiological models; Growth model; Public health policies

Introduction: The Covid-19 pandemic is one of the biggest public health crises the world has ever faced. In this context, it is important to have effective models to describe the different stages of the epidemic’s evolution in order to guide the authorities in taking appropriate measures to fight the disease. Objective: To present an analysis of epidemic curves of Covid-19 based on phenomenological growth models, with applications to the curves for the cumulative numbers of confirmed cases of infection by the novel coronavirus (Sars-Cov-2) and deaths attributed to the disease (Covid-19) caused by the virus, for the Brazilian cities of Recife and Teresina. Methods: The Richards generalized model and the generalized growth model were used to make the numerical fits of the respective empirical curves. Results: The models used described very well the empirical curves against which they were tested. In particular, the generalized Richards model was able to identify the appearance of the inflexion point in the cumulative curves, which in turn represents the peak of the respective daily curves. A brief discussion is also presented on the relationship between the fitting parameters obtained from the model and the mitigation measures adopted in each of the municipalities considered. Conclusions: The generalized Richards model proved to be very effective in describing epidemic curves of Covid-19 and estimating important epidemiological parameters, such as the time of the peak of the curve for daily cases and deaths, thus allowing a practical and efficient monitoring of the epidemic evolution.