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

Estimating time-varying epidemiological parameters and underreporting of Covid-19 cases in Brazil using a mathematical model with fuzzy transitions between epidemic periods

Our study conducts a comprehensive analysis of the Covid-19 pandemic in Brazil, spanning five waves over three years. We employed a novel Susceptible-Infected-Recovered-Dead-Susceptible (SIRDS) model with a fuzzy transition between epidemic periods to estimate time-varying parameters and evaluate case underreporting. The initial basic reproduction number (R0) is identified at 2.44 (95% Confidence Interval (CI): 2.42-2.46), decreasing to 1.00 (95% CI: 0.99-1.01) during the first wave. The model estimates an underreporting factor of 12.9 (95% CI: 12.5-13.2) more infections than officially reported by Brazilian health authorities, with an increasing factor of 5.8 (95% CI: 5.2-6.4), 12.9 (95% CI: 12.5-13.3), and 16.8 (95% CI: 15.8-17.5) in 2020, 2021, and 2022 respectively. Additionally, the Infection Fatality Rate (IFR) is initially 0.88% (95% CI: 0.81%-0.94%) during the initial phase but consistently reduces across subsequent outbreaks, reaching its lowest value of 0.018% (95% CI: 0.011-0.033) in the last outbreak. Regarding the immunity period, the observed uncertainty and low sensitivity indicate that inferring this parameter is particularly challenging. Brazil successfully reduced R0 during the first wave, coinciding with decreased human mobility. Ineffective public health measures during the second wave resulted in the highest mortality rates within the studied period. We attribute lower mortality rates in 2022 to increased vaccination coverage and the lower lethality of the Omicron variant. We demonstrate the model generalization by its application to other countries. Comparative analyses with serological research further validate the accuracy of the model. In forecasting analysis, our model provides reasonable outbreak predictions. In conclusion, our study provides a nuanced understanding of the Covid-19 pandemic in Brazil, employing a novel epidemiological model. The findings contribute to the broader discourse on pandemic dynamics, underreporting, and the effectiveness of health interventions.

Finite-time stability analysis and control of stochastic SIR epidemic model: A study of COVID-19

Finite-time stability analysis is a powerful tool for understanding the long-term behavior of epidemiological models and has been widely used to study the spread of infectious diseases such as COVID-19. In this paper, we present a finite-time stability analysis of a stochastic susceptible-infected-recovered (SIR) epidemic compartmental model with switching signals. The model includes a linear parameter variation (LPV) and switching system that represents the impact of external factors, such as changes in public health policies or seasonal variations, on the transmission rate of the disease. We use the Lyapunov stability theory to examine the long-term behavior of the model and determine conditions under which the disease is likely to die out or persist in the population. By taking advantage of the average dwell time method and Lyapunov functional (LF) method, and using novel inequality techniques the finite-time stability (FTS) criterion in linear matrix inequalities (LMIs) is developed. The finite-time stability of the resultant closed-loop system, with interval and linear parameter variation (LPV), is then guaranteed by state feedback controllers. By analyzing the modified SIR model with these interventions, we are able to examine the efficiency of different control measures and determine the most appropriate response to the COVID-19 pandemic and demonstrate the efficacy of the suggested strategy through simulation results.

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

In this paper, a mathematical model of the COVID-19 pandemic is formulated by fitting it to actual data collected during the fifth wave of the COVID-19 pandemic in Coahuila, Mexico, from June 2022 to October 2022. The data sets used are recorded on a daily basis and presented in a discrete-time sequence. To obtain the equivalent data model, fuzzy rules emulated networks are utilized to derive a class of discrete-time systems based on the daily hospitalized individuals' data. The aim of this study is to investigate the optimal control problem to determine the most effective interventional policy including precautionary and awareness measures, the detection of asymptomatic and symptomatic individuals, and vaccination. A main theorem is developed to guarantee the closed-loop system performance by utilizing approximate functions of the equivalent model. The numerical results indicate that the proposed interventional policy can eradicate the pandemic within 1-8 weeks. Additionally, the results show that if the policy is implemented within the first 3 weeks, the number of hospitalized individuals remains below the hospital's capacity.

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

Due to the importance of control actions in spreading coronavirus disease, this paper is devoted to first modeling and then proposing an appropriate controller for this model. In the modeling procedure, we used a nonlinear mathematical model for the covid-19 outbreak to form a T-S fuzzy model. Then, for proposing the suitable controller, multiple optimization techniques including Linear Quadratic Regulator (LQR) and mixedH 2 - H ∞ are taken into account. The mentioned controller is chosen because the model of corona-virus spread is not only full of disturbances like a sudden increase in infected people, but also noises such as unavailability of the exact number of each compartment. The controller is simulated accordingly to validate the results of mathematical calculations, and a comparative analysis is presented to investigate the different situations of the problem. Comparing the results of controlled and uncontrolled situations, it can be observed that we can tackle the devastating hazards of the covid-19 outbreak effectively if the suggested approaches and policies of controlling interventions are executed, appropriately.

Epidemic model dynamics and fuzzy neural-network optimal control with impulsive traveling and migrating: Case study of COVID-19 vaccination

To suppress the epidemics caused by a virus such as COVID-19, three effective strategies listing vaccination, quarantine and medical treatments, are employed under suitable policies. Quarantine motions may affect the economic systems and pharmaceutical medications may be recently in the developing phase. Thus, vaccination seems the best hope of the current situation to control COVID-19 epidemics. In this work, the dynamic model of COVID-19 epidemic is developed when the quarantine factor and the antiviral factor are established as free variables. Moreover, the impulsive populations are comprehended for traveling and migrating of individuals. The proposed dynamics with impulsive distractions are employed to generate the online data. Thereafter, the equivalent model is developed by using only the daily data of symptomatic infectious individuals and the optimal vaccination policy is derived by utilizing the closed-loop control topology. The theoretical framework of the proposed schemes validates the reduction of symptomatic infectious individuals by using fewer doses of vaccines comparing with the scheduling vaccination.