SARS-CoV-2: sir model limitations and predictive constraints

Charles Roberto Telles*, Henrique Lopes, Diogo Franco

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)
14 Downloads

Abstract

Background: The main purpose of this research is to describe the mathematical asymmetric patterns of susceptible, infectious, or recovered (SIR) model equation application in the light of coronavirus disease 2019 (COVID-19) skewness patterns worldwide. Methods: The research mod-eled severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) spreading and dissemination patterns sensitivity by redesigning time series data extraction of daily new cases in terms of deviation consistency concerning variables that sustain COVID-19 transmission. The approach opened a new scenario where seasonality forcing behavior was introduced to understand SARS-COV-2 non-linear dynamics due to heterogeneity and confounding epidemics scenarios. Results: The main research results are the elucidation of three birth-and death-forced seasonality persistence phases that can explain COVID-19 skew patterns worldwide. They are presented in the following order: (1) the environmental variables (Earth seasons and atmospheric conditions); (2) health policies and adult learning education (HPALE) interventions; (3) urban spaces (local indoor and outdoor spaces for transit and social-cultural interactions, public or private, with natural physical features (river, lake, terrain). Conclusions: Three forced seasonality phases (positive to negative skew) phases were pointed out as a theoretical framework to explain uncertainty found in the predictive SIR model equations that might diverge in outcomes expected to express the disease’s behaviour.
Original languageEnglish
Article number676
Number of pages14
JournalSymmetry
Volume13
Issue number4
DOIs
Publication statusPublished - Apr 2021

Keywords

  • Confounding variables
  • COVID-19 seasonality
  • Forced seasonality
  • Mathematical modeling
  • S.I.R. models
  • Uncertainty

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