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

Compartmentalized mathematical model to predict future number of active cases and deaths of COVID-19 IntroductionIn 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.MethodologyWe present a generalized Susceptible-Exposed-Infected-Recovered (SEIR) compartmental model and test it using a global optimization algorithm with data collected from the WHO.ResultsThe 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.ConclusionThe 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Research on Biomedical Engineering Springer Journals

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

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Publisher
Springer Journals
Copyright
Copyright © Sociedade Brasileira de Engenharia Biomedica 2020
ISSN
2446-4732
eISSN
2446-4740
DOI
10.1007/s42600-020-00084-6
Publisher site
See Article on Publisher Site

Abstract

IntroductionIn 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.MethodologyWe present a generalized Susceptible-Exposed-Infected-Recovered (SEIR) compartmental model and test it using a global optimization algorithm with data collected from the WHO.ResultsThe 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.ConclusionThe 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.

Journal

Research on Biomedical Engineering – Springer Journals

Published: Mar 1, 2022

Keywords: COVID-19; Compartmental model; Active cases; Deaths; Epidemiological model predictions

References

  • Data-based analysis, modelling and forecasting of the COVID-19 outbreak
    Anastassopoulou, C
  • Clinical characteristics of 2019 novel coronavirus infection in China
    Guan, W
  • Early spread of SARS-Cov-2 in the Icelandic population
    Gudbjartsson, DF; Helgason, A; Jonsson, AH
  • The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak – an update on the status
    Guo, YR
  • Modeling the effects of intervention strategies on COVID-19 transmission dynamics
    Kennedy, DM; Zambrano, GJ; Wang, Y; Neto, OP
  • The reproductive number of COVID-19 is higher compared to SARS coronavirus
    Liu, Y
  • Estimating the final epidemic size for COVID-19
    Ranjan, R