An extended epidemic model with vaccination: Weak-immune SIRVI

Influence of waning immunity on vaccination decision-making: A multi-strain epidemic model with an evolutionary approach analyzing cost and efficacy

In this research, we introduce a comprehensive epidemiological model that accounts for multiple strains of an infectious disease and two distinct vaccination options. Vaccination stands out as the most effective means to prevent and manage infectious diseases. However, when there are various vaccines available, each with its costs and effectiveness, the decision-making process for individuals becomes paramount. Furthermore, the factor of waning immunity following vaccination also plays a significant role in influencing these choices. To understand how individuals make decisions in the context of multiple strains and waning immunity, we employ a behavioral model, allowing an epidemiological model to be coupled with the dynamics of a decision-making process. Individuals base their choice of vaccination on factors such as the total number of infected individuals and the cost-effectiveness of the vaccine. Our findings indicate that as waning immunity increases, people tend to prioritize vaccines with higher costs and greater efficacy. Moreover, when more contagious strains are present, the equilibrium in vaccine adoption is reached more rapidly. Finally, we delve into the social dilemma inherent in our model by quantifying the social efficiency deficit (SED) under various parameter combinations.

A non-autonomous time-delayed SIR model for COVID-19 epidemics prediction in China during the transmission of Omicron variant

<abstract><p>With the continuous evolution of the coronavirus, the Omicron variant has gradually replaced the Delta variant as the prevalent strain. Their inducing epidemics last longer, have a higher number of asymptomatic cases, and are more serious. In this article, we proposed a nonautonomous time-delayed susceptible-infected-removed (NATD-SIR) model to predict them in different regions of China. We obtained the maximum and its time of current infected persons, the final size, and the end time of COVID-19 epidemics from January 2022 in China. The method of the fifth-order moving average was used to preprocess the time series of the numbers of current infected and removed cases to obtain more accurate parameter estimations. We found that usually the transmission rate $ \beta(t) $ was a piecewise exponential decay function, but due to multiple bounces in Shanghai City, $ \beta(t) $ was approximately a piecewise quadratic function. In most regions, the removed rate $ \gamma(t) $ was approximately equal to a piecewise linear increasing function of (a*t+b)*H(t-k), but in a few areas, $ \gamma(t) $ displayed an exponential increasing trend. For cases where the removed rate cannot be obtained, we proposed a method for setting the removed rate, which has a good approximation. Using the numerical solution, we obtained the prediction results of the epidemics. By analyzing those important indicators of COVID-19, we provided valuable suggestions for epidemic prevention and control and the resumption of work and production.</p></abstract>

Endemic characteristics of SARS-CoV-2 infection

The fourth year of the COVID-19 pandemic without decreasing trends in the global numbers of new daily cases, high numbers of circulating SARS-CoV-2 variants and re-infections together with pessimistic predictions for the Omicron wave duration force studies about the endemic stage of the disease. The global trends were illustrated with the use the accumulated numbers of laboratory-confirmed COVID-19 cases and deaths, the percentages of fully vaccinated people and boosters (additional vaccinations), and the results of calculation of the effective reproduction number provided by Johns Hopkins University. A new modified SIR model with re-infections was proposed and analyzed. The estimated parameters of equilibrium show that the global numbers of new daily cases will range between 300 thousand and one million, daily deaths-between one and 3.3 thousand.

Risk factors associated with indoor transmission during home quarantine of COVID-19 patients

Purpose

The study aimed to identify potential risk factors for family transmission and to provide precautionary guidelines for the general public during novel Coronavirus disease 2019 (COVID-19) waves.

Methods

A retrospective cohort study with numerous COVID-19 patients recruited was conducted in Shanghai. Epidemiological data including transmission details, demographics, vaccination status, symptoms, comorbidities, antigen test, living environment, residential ventilation, disinfection and medical treatment of each participant were collected and risk factors for family transmission were determined.

Results

A total of 2,334 COVID-19 patients participated. Compared with non-cohabitation infected patients, cohabitated ones were younger (p = 0.019), more commonly unvaccinated (p = 0.048) or exposed to infections (p < 0.001), and had higher rates of symptoms (p = 0.003) or shared living room (p < 0.001). Risk factors analysis showed that the 2019-nCov antigen positive (OR = 1.86, 95%CI 1.40-2.48, p < 0.001), symptoms development (OR = 1.86, 95%CI 1.34-2.58, p < 0.001), direct contact exposure (OR = 1.47, 95%CI 1.09-1.96, p = 0.010) were independent risk factors for the cohabitant transmission of COVID-19, and a separate room with a separate toilet could reduce the risk of family transmission (OR = 0.62, 95%CI 0.41-0.92, p = 0.018).

Conclusion

Patients showing negative 2019-nCov antigen tests, being asymptomatic, living in a separate room with a separate toilet, or actively avoiding direct contact with cohabitants were at low risk of family transmission, and the study recommended that avoiding direct contact and residential disinfection could reduce the risk of all cohabitants within the same house being infected with COVID-19.

Analysis of SIRVI model with time dependent coefficients and the effect of vaccination on the transmission rate and COVID-19 epidemic waves

COVID-19 epidemic models with constant transmission rate cannot capture the patterns of the infection data in the presence of pharmaceutical and non-pharmaceutical interventions during a pandemic. Because of this, a new modification of SIR model that contain the vaccination compartment with time dependent coefficients and weak/loss-immunity is explored. Literature review confirms that the effect of vaccination on the time dependent transmission rate is still an open problem. This study answers this open problem. In this study, we first prove the well-posedness and investigate the model dynamics to show their continuous dependence on the model parameters. We then provide an algorithm to derive the time-dependent transmission function for the epidemiologic model and the data of the infected cases. The derived coupled nonlinear differential equations show the effect of vaccination on the transmission rate. Unlike previous studies, we first filter the published data and solve the nonlinear coupled differential equations using the finite difference technique, where the coefficient of the coupled nonlinear differential equations is a function of given data. We then show that time-dependent transmission function can be represented by linear combinations of Gaussian radial base function. We then validate the prediction of our models using numerical simulations, where we used the published data of COVID-19 confirmed cases by the Ministries of Health in Saudi Arabia and Poland. Finally, the numerical solutions of a SIRVI model with time dependent transmission rate show that the waves for currently active cases are in good agreement with the data of Saudi Arabia and Poland.

Machine learning models for prediction of HF and CKD development in early-stage type 2 diabetes patients

Chronic kidney disease (CKD) and heart failure (HF) are the first and most frequent comorbidities associated with mortality risks in early-stage type 2 diabetes mellitus (T2DM). However, efficient screening and risk assessment strategies for identifying T2DM patients at high risk of developing CKD and/or HF (CKD/HF) remains to be established. This study aimed to generate a novel machine learning (ML) model to predict the risk of developing CKD/HF in early-stage T2DM patients. The models were derived from a retrospective cohort of 217,054 T2DM patients without a history of cardiovascular and renal diseases extracted from a Japanese claims database. Among algorithms used for the ML, extreme gradient boosting exhibited the best performance for CKD/HF diagnosis and hospitalization after internal validation and was further validated using another dataset including 16,822 patients. In the external validation, 5-years prediction area under the receiver operating characteristic curves for CKD/HF diagnosis and hospitalization were 0.718 and 0.837, respectively. In Kaplan-Meier curves analysis, patients predicted to be at high risk showed significant increase in CKD/HF diagnosis and hospitalization compared with those at low risk. Thus, the developed model predicted the risk of developing CKD/HF in T2DM patients with reasonable probability in the external validation cohort. Clinical approach identifying T2DM at high risk of developing CKD/HF using ML models may contribute to improved prognosis by promoting early diagnosis and intervention.

Caputo Fractal Fractional Order Derivative of Soil Pollution Model Due to Industrial and Agrochemical

This paper narrates a non-linear and non-local Caputo fractal fractional operator of eco epidemic model with the advance of soil pollution considered in five compartments. The qualitative analysis of solutions such as existence and uniqueness of the model is carried out by using the standard condition of Schauder’s fixed point theorem and Banach Contraction principle. The local and global stability are characterized with the help of basic reproduction number. The Ulam-Hyer stability is analyzed for the small perturbation. The Power law kernel is used to get a reliable result for the soil pollution model. Analytical solution studied by means of Modified Euler method. Numerical simulation of Euler scheme algorithm is performed to show the effects of various fractional orders \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(0.5<\eta <1)$$\end{document}(0.5<η<1) and validating the theoretical parameter values of real time data by the support of MATLAB.

Stability and bifurcation control analysis of a delayed fractional-order eco-epidemiological system

Considering the factor of artificial intervention in biological control, a delayed fractional eco-epidemiological system with an extended feedback controller is proposed. By using the digestion delay as bifurcation parameter, the stability and Hopf bifurcation are investigated, and the branching conditions are given. The system undergoes Hopf bifurcation, when the parameter τ passes through the critical value. In addition, it can be pointed out that the negative feedback gain and the feedback delay could affect the bifurcation critical value of the system. Therefore, the Hopf bifurcation can also be induced by taking the feedback delay as a bifurcation parameter. Finally, by plotting the solution curve of the system, the significance of the controller to the stability of the eco-epidemiological system is verified.