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

Charles Roberto Telles,Diogo Franco,Henrique Lopes

doi:10.3390/sym13040676

Charles Roberto Telles, Diogo Franco + Show 1 more

Open Access

https://doi.org/10.3390/sym13040676

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Journal: Symmetry	Publication Date: Apr 14, 2021
Citations: 11	License type: CC BY 4.0

Affiliation: Universidade Católica Portuguesa

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 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.

Highlights

This research’s main focus is to point, as noted in Grassly and Fraser [1], to the consequences of seasonality for endemic R0 stability in order to understand and obtain an endemic equilibrium for coronavirus disease 2019 (COVID-19) involving mixing patterns such as environmental driving factors, policy interventions, and urban spaces [2,3,4,5,6,7,8]
The overall scenario of spreading and dissemination patterns skewness concerning the environment, health policies and adult and learning (HPALE) and urban spaces can be visualized in Figure 5, where seasonality forcing behavior assumes the following topological metric space
The approach opened a new scenario where seasonality forcing behavior was introduced to understand SARS-COV-2 skewness expressions due to heterogeneity and confounding epidemics scenarios where actual SIR models might find a high degree of uncertainty caused by oscillatory conditions found in the input of variables of the event

Summary

Introduction

This research’s main focus is to point, as noted in Grassly and Fraser [1], to the consequences of seasonality for endemic R0 stability in order to understand and obtain an endemic equilibrium for coronavirus disease 2019 (COVID-19) involving mixing patterns such as environmental driving factors, policy interventions, and urban spaces [2,3,4,5,6,7,8] These latter three variables might pose challenges for the outcomes of the SIR (susceptible, infectious, or recovered) predictive analysis [9] of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) spreading and dissemination patterns. These pre-assumptions were investigated theoretically stability to instability patterns generated by the variables that sustain the disease occurrence

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