Compartmentalized mathematical model to predict future number of active cases and deaths of COVID-19

• Published in: Research on biomedical engineering Vol. 38; no. 1; pp. 1 - 14
• Main Authors: Pinto Neto, Osmar, Reis, José Clark, Brizzi, Ana Carolina Brisola, Zambrano, Gustavo José, de Souza, Joabe Marcos, Pedroso, Wellington, de Mello Pedreiro, Rodrigo Cunha, de Matos Brizzi, Bruno, Abinader, Ellysson Oliveira, Zângaro, Renato Amaro
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.03.2022
• Subjects: Biomaterials; Biomedical Engineering and Bioengineering; Biomedical Engineering/Biotechnology; Engineering; Original; Original Article; Special Issue: Emerging Technologies for Fighting COVID-19; COVID-19; Active cases; Deaths; Compartmental model; Epidemiological model predictions
• ISSN: 2446-4732; 2446-4740
• DOI: 10.1007/s42600-020-00084-6

Introduction In December 2019, China reported a series of atypical pneumonia cases caused by a new Coronavirus, called COVID-19. In response to the rapid global dissemination of the virus, on the 11th of Mars, the World Health Organization (WHO) has declared the outbreak a pandemic. Considering this situation, this paper intends to analyze and improve the current SEIR models to better represent the behavior of the COVID-19 and accurately predict the outcome of the pandemic in each social, economic, and political scenario. Methodology We present a generalized Susceptible-Exposed-Infected-Recovered (SEIR) compartmental model and test it using a global optimization algorithm with data collected from the WHO. Results The main results were: (a) Our model was able to accurately fit the either deaths or active cases data of all tested countries using optimized coefficient values in agreement with recent reports; (b) when trying to fit both sets of data at the same time, fit was good for most countries, but not all. (c) Using our model, large ranges for each input, and optimization we predict death values for 15, 30, 45, and 60 days ahead with errors in the order of 5, 10, 20, and 80%, respectively; (d) sudden changes in the number of active cases cannot be predicted by the model unless data from outside sources are used. Conclusion The results suggest that the presented model may be used to predict 15 days ahead values of total deaths with errors in the order of 5%. These errors may be minimized if social distance data are inputted into the model.