Linear parameter varying model of COVID-19 pandemic exploiting basis functions

Abstract

Current outbreaks of the COIVD-19 pandemic demonstrate a global threat. In this paper, a conceptual model is developed for the COVID-19 pandemic, in which the people in society are divided into Susceptible, Exposed, Minor infected (Those who need to be quarantined at home), Hospitalized (Those who are in need of hospitalization), Intensive infected (ventilator-in-need infected), Recovered and Deceased. In this paper, first, the model that is briefly called SEMHIRD for a sample country (Italy as an example) is considered. Then, exploiting the real data of the country, the parameters of the model are obtained by assuming some basis functions on the collected data and solving linear least square problems in each window of data to estimate the time-varying parameters of the model. Thus, the parameters are updated every few days, and the system behavior is modeled according to the changes in the parameters. Then, the Linear Parameter Varying (LPV) Model of COVID19 is derived, and its stability analysis is presented. In the end, the influence of different levels of social distancing and quarantine on the variation of severely infected and hospitalized people is studied.

Data‐driven model prediction and optimal control for interventional policy of a class of susceptible‐infectious‐removed dynamics with COVID‐19 data

“…where q is a positive constant, 0 < 𝛾 ≤ 1 is a discount parameter, and R ∈ R 4×4 is a positive diagonal matrix such that R i > 0 for i = 1, 2, … , 4. Let's rearrange the cost function (16), thus, we have…”

“… 9 , 10 According to the world's databased systems, all data and information are collected in a discrete‐time manner with daily intervals. 11 , 12 , 13 On the other hand, the general models based on SEIR 14 , 15 and current COVID‐19 dynamics 16 , 17 are derived by the continuous‐time differential equations. Thus, the discrete‐time version of COVID‐19 dynamics seems sufficient regrading to the existence data.…”

Multi-objective T-S fuzzy control of Covid-19 spread model: An LMI approach

“…The basic model for the epidemic disease is Susceptible-Infected-Recovered (SIR) compartmental model [14] . The more detailed the model is, the more one learns about the system's behavior [1] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] . Several models have been developed for the covid-19 epidemic recently.…”

Optimal interventional policy based on discrete-time fuzzy rules equivalent model utilizing with COVID-19 pandemic data

“…One of the commonly used models is the SEIR (Susceptible-Exposed-Infected-Recovered) model [3,4], which divides the population into four categories. However, the general models based on SEIR and the current COVID-19 dynamics [5,6] have been derived from continuous-time differential equations, which may not accurately reflect the reality. Nevertheless in some databases such as [7][8][9], data and information are collected on a daily interval in a discrete-time manner.…”