Crowding effects on the dynamics of COVID-19 mathematical model

Modeling the stochastic within-host dynamics SARS-CoV-2 infection with discrete delay

In this paper, a new mathematical model that describes the dynamics of the within-host COVID-19 epidemic is formulated. We show the stochastic dynamics of Target-Latent-Infected-Virus free within the human body with discrete delay and noise. Positivity and uniqueness of the solutions are established. Our study shows the extinction and persistence of the disease inside the human body through the stability analysis of the disease-free equilibrium [Formula: see text] and the endemic equilibrium [Formula: see text], respectively. Moreover, we show the impact of delay tactics and noise on the extinction of the disease. The most interesting result is even if the deterministic system is inevitably pandemic at a specific point, extinction will become possible in the stochastic version of our model.

The fractional-order discrete COVID-19 pandemic model: stability and chaos

This paper presents and investigates a new fractional discrete COVID-19 model which involves three variables: the new daily cases, additional severe cases and deaths. Here, we analyze the stability of the equilibrium point at different values of the fractional order. Using maximum Lyapunov exponents, phase attractors, bifurcation diagrams, the 0-1 test and approximation entropy (ApEn), it is shown that the dynamic behaviors of the model change from stable to chaotic behavior by varying the fractional orders. Besides showing that the fractional discrete model fits the real data of the pandemic, the simulation findings also show that the numbers of new daily cases, additional severe cases and deaths exhibit chaotic behavior without any effective attempts to curb the epidemic.

A robust study of a piecewise fractional order COVID-19 mathematical model

In the current manuscript, we deal with the dynamics of a piecewise covid-19 mathematical model with quarantine class and vaccination using SEIQR epidemic model. For this, we discussed the deterministic, stochastic, and fractional forms of the proposed model for different steps. It has a great impact on the infectious disease models and especially for covid-19 because in start the deterministic model played its role but with time due to uncertainty the stochastic model takes place and with long term expansion the use of fractional derivatives are required. The stability of the model is discussed regarding the reproductive number. Using the non-standard finite difference scheme for the numerical solution of the deterministic model and illustrate the obtained results graphically. Further, environmental noises are added to the model for the description of the stochastic model. Then take out the existence and uniqueness of positive solution with extinction for infection. Finally, we utilize a new technique of piecewise differential and integral operators for approximating Caputo-Fabrizio fractional derivative operator for the purpose of constructing of the fractional-order model. Then study the dynamics of the models such as positivity and boundedness of the solutions and local stability analysis. Solved numerically fractional-order model used Newton Polynomial scheme and present the results graphically.

Modelling the impact of health care providers in transmission dynamics of COVID-19

In this paper, a mathematical model is proposed and analysed to assess the impacts of health care providers in transmission dynamics of COVID-19. The stability theory of differential equations is used to examine a mathematical model. The results of both local and global stability of disease-free equilibrium points were determined by using Routh-Hurwitz criteria and Metzler matrix method which verified that was locally and globally asymptotically stable. Also, the endemic equilibrium point was determined by the Lyapunov function which showed thatE ∗ was globally asymptotically stable under strict conditions. The findings revealed that non-diagnosed and undetected health care providers seems to contribute to high spread of COVID-19 in a community. Also, it illustrates that an increase in the number of non-diagnostic testing rates of health care providers may result in high infection rates in the community and contaminations of hospitals' equipment. Therefore, the particular study recommend that there is a necessity of applying early diagnostic testing to curtail the COVID-19 transmission in the health care providers' community and reduce contaminations of hospital's equipment.

The State of the Art of Data Mining Algorithms for Predicting the COVID-19 Pandemic

Current computer systems are accumulating huge amounts of information in several application domains. The outbreak of COVID-19 has increased rekindled interest in the use of data mining techniques for the analysis of factors that are related to the emergence of an epidemic. Data mining techniques are being used in the analysis and interpretation of information, which helps in the discovery of patterns, planning of isolation policies, and even predicting the speed of proliferation of contagion in a viral disease such as COVID-19. This research provides a comprehensive study of various data mining algorithms that are used in conjunction with epidemiological prediction models. The document considers that there is an opportunity to improve or develop tools that offer an accurate prognosis in the management of viral diseases through the use of data mining tools, based on a comparative study of 35 research papers.

Socio-demographic, lifestyle and health characteristics as predictors of self-reported Covid-19 history among older adults: 2006-2020 Health and Retirement Study

BACKGROUND

To identify key socio-demographic, lifestyle, and health predictors of self-reported coronavirus disease 2019 (Covid-19) history, examine cardiometabolic health characteristics as predictors of self-reported Covid-19 history and compare groups with and without a history of Covid-19 on trajectories in cardiometabolic health and blood pressure measurements over time, among United States (U.S.) older adults.

METHODS

Nationally representative longitudinal data on U.S. older adults from the 2006-2020 Health and Retirement Study were analyzed using logistic and mixed-effects logistic regression models.

RESULTS

Based on logistic regression, number of household members (OR=1.26, 95% CI: 1.05, 1.52), depressive symptoms score (OR = 1.21, 95% CI: 1.04, 1.42) and number of cardiometabolic risk factors or chronic conditions ("1-2" vs "0") (OR = 0.27, 95% CI: 0.11, 0.67) were significant predictors of self-reported Covid-19 history. Based on mixed-effects logistic regression, several statistically significant predictors of Covid-19 history were identified, including female sex (OR = 3.06, 95% CI: 1.57, 5.96), other race (OR = 5.85, 95% CI: 2.37, 14.43), Hispanic ethnicity (OR = 2.66, 95% CI: 1.15, 6.17), number of household members (OR = 1.25, 95% CI: 1.10, 1.42), moderate-to-vigorous physical activity (1-4 times per month vs never) (OR = 0.38, 95% CI: 0.18, 0.78) and number of cardiometabolic risk factors or chronic conditions ("1-2" vs "0") (OR = 0.34, 95% CI: 0.19, 0.60).

CONCLUSIONS

Number of household members, depressive symptoms and number of cardiometabolic risk factors or chronic conditions may be key predictors for self-reported Covid-19 history among U.S. older adults. In-depth analyses are needed to confirm preliminary findings.

A transmission dynamics model of Covid-19: Case of Cameroon

In this work, we propose and investigate an ordinary differential equations model describing the spread of COVID-19 in Cameroon. The model takes into account the asymptomatic, unreported symptomatic, quarantine, hospitalized individuals and the amount of virus in the environment, for evaluating their impact on the transmission of the disease. After establishing the basic properties of the model, we compute the control reproduction number Rc and show that the disease dies out whenever Rc≤1 and is endemic whenever Rc>1. Furthermore, an optimal control problem is derived and investigated theoretically by mainly relying on Pontryagin's maximum principle. We illustrate the theoretical analysis by presenting some graphical results.

Global proprieties of a delayed epidemic model with partial susceptible protection

In the case of an epidemic, the government (or population itself) can use protection for reducing the epidemic. This research investigates the global dynamics of a delayed epidemic model with partial susceptible protection. A threshold dynamics is obtained in terms of the basic reproduction number, where for R₀<1 the infection will extinct from the population. But, for R₀>1 it has been shown that the disease will persist, and the unique positive equilibrium is globally asymptotically stable. The principal purpose of this research is to determine a relation between the isolation rate and the basic reproduction number in such a way we can eliminate the infection from the population. Moreover, we will determine the minimal protection force to eliminate the infection for the population. A comparative analysis with the classical SIR model is provided. The results are supported by some numerical illustrations with their epidemiological relevance.

A vigorous study of fractional order COVID-19 model via ABC derivatives

The newly arose irresistible sickness known as the Covid illness (COVID-19), is a highly infectious viral disease. This disease caused millions of tainted cases internationally and still represent a disturbing circumstance for the human lives. As of late, numerous mathematical compartmental models have been considered to even more likely comprehend the Covid illness. The greater part of these models depends on integer-order derivatives which cannot catch the fading memory and crossover behavior found in many biological phenomena. Along these lines, the Covid illness in this paper is studied by investigating the elements of COVID-19 contamination utilizing the non-integer Atangana-Baleanu-Caputo derivative. Using the fixed-point approach, the existence and uniqueness of the integral of the fractional model for COVID is further deliberated. Along with Ulam-Hyers stability analysis, for the given model, all basic properties are studied. Furthermore, numerical simulations are performed using Newton polynomial and Adams Bashforth approaches for determining the impact of parameters change on the dynamical behavior of the systems.

Analysis of a discrete mathematical COVID-19 model

To describe the main propagation of the COVID-19 and has to find the control for the rapid spread of this viral disease in real life, in current manuscript a discrete form of the SEIR model is discussed. The main aim of this is to describe the viral disease in simplest way and the basic properties that are related with the nature of curves for susceptible and infected individuals are discussed here. The elementary numerical examples are given by using the real data of India and Algeria.