SARS-COV-2: SIR Model Limitations and Predictive Constraints

• Published in: Symmetry (Basel) Vol. 13; no. 4; p. 676
• Main Authors: Roberto Telles, Charles, Lopes, Henrique, Franco, Diogo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2021
• Subjects: Confounding (Statistics); confounding variables; Constraint modelling; Coronaviruses; COVID-19; COVID-19 seasonality; Disease transmission; Epidemics; forced seasonality; Heterogeneity; Infections; mathematical modeling; Nonlinear dynamics; Ordinary differential equations; Pandemics; Phases; Respiratory diseases; S.I.R. models; Seasonal variations; Severe acute respiratory syndrome coronavirus 2; Time series; uncertainty; Urban areas; Viral diseases
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym13040676

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 modeled 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.