A risk-sharing-based resilient renewable energy supply network model under the COVID-19 pandemic.

Over the past few months, the COVID-19 pandemic has postponed many renewable energy projects because of disruptions in the technology and finance supply. Additionally, the existing power plants are inefficient because of a record drop in demand for goods and services caused by lockdowns in cities. This situation poses huge challenges to the resilience of renewable energy supply networks in the face of deeply hazardous events, such as the COVID-19 pandemic. Therefore, the purpose of this study was to design a resilient renewable energy supply network considering supply, demand, and payment risks caused by COVID-19. The objective of the proposed model was to determine the optimal amount of electric power generated and stored to meet the demands and the risk-sharing effort index to maximize the total resilient profit of the power plant and determine the optimal price adjustment index to minimize the cost to consumers. A government subsidy-based risk-sharing model was developed to enhance the resilience of the concerned renewable energy supply network under the pandemic. To overcome uncertainties in both random and risk events, a robust fuzzy-stochastic programming model was proposed to solve these research problems. Computational experiments were conducted on the test supply network in Vietnam. The results showed that the resilient energy supply network with the risk-sharing model tended to stabilize the total profit with the different impact levels of COVID-19 compared to the network without risk-sharing. The proposed model efficiently tackled both uncertainties in random and hazardous events and had a higher profit and shorter CPU time compared to the robust optimization mode.

Global dynamics and Impact of Gaussian noise intensity on the stochastic epidemic model with local fractional derivative

Society must understand, model, and forecast infectious disease transmission patterns in order to prevent pandemics. Mathematical models and computer technology may help us better understand the pandemic and create more systematic and effective infection management strategies. This study offers a novel perspective through a compartmental model that incorporates fractional calculus. The first scenario is based on proportional fractional definitions, considering compartmental individuals of susceptible, moving susceptible, exposed, infected, hospitalized, and recovered. Through an extension of this derivative, they decimated the model to integer order. We extended the deterministic model to a stochastic extension to capture the uncertainty or variance in disease transmission. It can develop an appropriate Lyapunov function to detect the presence and uniqueness of positive global solutions. Next, we discuss how the epidemic model might have become extinct. In our theoretical study, we demonstrated that a sufficiently outrageous amount of noise can cause a disease to become extinct. A modest level of noise, on the other hand, promotes the persistence of diseases and their stationary distribution. The Khasminskii method was used to determine the stationary distribution and ergodicity of the model.

Machine learning uncovers blood test patterns subphenotypes at hospital admission discerning increased 30-day ICU mortality rates in COVID-19 elderly patients

Introduction: Since March 2020, a number of SARS-CoV-2 patients have frequently required intensive care unit (ICU) admission, associated with moderate survival outcomes and an increasing economic burden. Elderly patients are among the most numerous, due to previous comorbidities and complications they develop during hospitalization [1]. For this reason, a reliable early risk stratification tool could help estimate an early prognosis and allow for an appropriate resources allocation in favour of the most vulnerable and critically ill patients. Method(s): This retrospective study includes data from two Spanish hospitals, HU12O (Madrid) and HCUV (Valencia), from 193 patients aged > 64 with COVID-19 between February and November 2020 who were admitted to the ICU. Variables include demographics, full-blood-count (FBC) tests and clinical outcomes. Machine learning applied a non-linear dimensionality reduction by t-distributed stochastic neighbor embedding (t-SNE) [2];then hierarchical clustering on the t-SNE output was performed. The number of clinically relevant subphenotypes was chosen by combining silhouette and elbow coefficients, and validated through exploratory analysis. Result(s): We identified five subphenotypes with heterogeneous interclustering age and FBC patterns (Fig. 1). Cluster 1 was the 'healthiest' phenotype, with 2% 30-day mortality and characterized by moderate leukocytes and eosinophils. Cluster 5, the severe phenotype, showed 44% 30-day mortality and was characterized by the highest leukocyte, neutrophil and platelet count and minimal monocytes and lymphocyte count. Clusters 2-4 displayed intermediate mortality rates (20-28%). Conclusion(s): The findings of this preliminary report of Eld-ICUCOV19 patients suggest the patient's FBC and age can display discriminative patterns associated with disparate 30-day ICU mortality rates.

On the Modeling of COVID-19 Spread via Fractional Derivative: A Stochastic Approach

: The coronavirus disease (COVID-19) pandemic has caused more harm than expected in developed and developing countries. In this work, a fractional stochastic model of COVID-19 which takes into account the random nature of the spread of disease, is formulated and analyzed. The existence and uniqueness of solutions were established using the fixed-point theory. Two different fractional operators', namely, power-law and Mittag–Leffler function, numerical schemes in the stochastic form, are utilized to obtain numerical simulations to support the theoretical results. It is observed that the fractional order derivative has effect on the dynamics of the spread of the disease. © 2023, Pleiades Publishing, Ltd.

Probability Analysis of a Stochastic Non-Autonomous SIQRC Model with Inference

When an individual with confirmed or suspected COVID-19 is quarantined or isolated, the virus can linger for up to an hour in the air. We developed a mathematical model for COVID-19 by adding the point where a person becomes infectious and begins to show symptoms of COVID-19 after being exposed to an infected environment or the surrounding air. It was proven that the proposed stochastic COVID-19 model is biologically well-justifiable by showing the existence, uniqueness, and positivity of the solution. We also explored the model for a unique global solution and derived the necessary conditions for the persistence and extinction of the COVID-19 epidemic. For the persistence of the disease, we observed that Rs0>1, and it was noticed that, for Rs<1, the COVID-19 infection will tend to eliminate itself from the population. Supplementary graphs representing the solutions of the model were produced to justify the obtained results based on the analysis. This study has the potential to establish a strong theoretical basis for the understanding of infectious diseases that re-emerge frequently. Our work was also intended to provide general techniques for developing the Lyapunov functions that will help the readers explore the stationary distribution of stochastic models having perturbations of the nonlinear type in particular.

CHANGES IN WATER USE IN DIFFERENT COVID-19 PANDEMIC RESTRICTION SCENARIOS

The COVID-19 pandemic has impacted in the lifestyle of people: it has changed habits, mobility and working dynamics, which has led to a rise in basic supply consumption in households. This paper aims to analyse the change in hot domestic water consumption during COVID-19 compared with pre-pandemic periods, in volume terms and the pattern change. For this purpose, an own-developed stochastic model to characterize domestic hot water use is modified to different restriction scenarios to adapt it to COVID-19 pandemic conditions. To validate the model, Urban Water consumption changes during 2020 in the cities of El Prat de Llobregat and Barberà del Vallès are analyzed in order to relate total use of water in residential homes and domestic hot water consumption. The mean increase for DHW found is around 7%. For good measure, daily water pattern consumption results increased from 10h to 13h and from 16h to 19h in this scenario, which is consistent with the literature review. Both results, volumetric and pattern, are considered valid. © 2022 by the authors. Licensee AEIPRO, Spain.

Performance evaluation of CNN architectures for COVID-19 detection from X-ray images

Early detection of the COVID-19 infection is the key to avoiding fatalities. Chest radiography has proven to be an effective and low-cost solution for detecting the virus. It is important to evaluate the potential of deep learning models for COVID-19 detection from the x-ray images for quick and early detection of COVID-19 with high accuracy. We conducted a study that evaluates the potential and performance of various Convolutional Neural Networks (CNN) architectures for detecting the COVID-19 on a dataset consisting of 5902 chest X-ray images having 2276 instances of X-ray images of COVID-19 patients and 3626 images of healthy and non-COVID-19 pneumonia X-rays. The performance of the models is assessed using metrics like accuracy, specificity, sensitivity, F1 Score, ROC curve, etc. The results suggest that the DenseNet-121 model proved to be the better choice among evaluated architectures for COVID-19 detection from X-ray images in terms of overall performance with an accuracy of 98.2%, sensitivity of 97.6%, and specificity of 98.4%. We conclude that there is a need for further evaluation of the CNN architectures on large, real-world, and diverse datasets for obtaining generalizable results for a reliable diagnosis.Copyright © 2022 Informa UK Limited, trading as Taylor & Francis Group.

Modeling between-farm transmission dynamics of porcine epidemic diarrhea virus: Characterizing the dominant transmission routes.

The role of transportation vehicles, pig movement between farms, proximity to infected premises, and feed deliveries has not been fully considered in the dissemination dynamics of porcine epidemic diarrhea virus (PEDV). This has limited efforts for disease prevention, control and elimination restricting the development of risk-based resource allocation to the most relevant modes of PEDV dissemination. Here, we modeled nine pathways of between-farm transmission represented by a contact network of pig movements between sites, farm-to-farm proximity (local transmission), four distinct contact networks of transportation vehicles (trucks that transport pigs from farm-to-farm and farm-to-markets, as well as trucks transporting feed and staff), the volume of animal by-products in feed diets (e.g., fat and meat-and-bone-meal) to reproduce PEDV transmission dynamics. The model was calibrated in space and time with weekly PEDV outbreaks. We investigated the model performance to identify outbreak locations and the contribution of each route in the dissemination of PEDV. The model estimated that 42.7% of the infections in sow farms were related to vehicles transporting feed, 34.5% of infected nurseries were associated with vehicles transporting pigs between farms, and for both farm types, local transmission or pig movements were the next most relevant transmission routes. On the other hand, finishers were most often (31.4%) infected via local transmission, followed by the vehicles transporting feed and pigs between farms. Feed ingredients did not significantly improve model calibration metrics, sensitivity, and specificity; therefore, it was considered to have a negligible contribution in the dissemination of PEDV. The proposed modeling framework provides an evaluation of PEDV transmission dynamics, ranking the most important routes of PEDV dissemination and granting the swine industry valuable information to focus efforts and resources on the most important transmission routes.

Global asymptotic stability, extinction and ergodic stationary distribution in a stochastic model for dual variants of SARS-CoV-2

Several mathematical models have been developed to investigate the dynamics SARS-CoV-2 and its different variants. Most of the multi-strain SARS-CoV-2 models do not capture an important and more realistic feature of such models known as randomness. As the dynamical behavior of most epidemics, especially SARS-CoV-2, is unarguably influenced by several random factors, it is appropriate to consider a stochastic vaccination co-infection model for two strains of SARS-CoV-2. In this work, a new stochastic model for two variants of SARS-CoV-2 is presented. The conditions of existence and the uniqueness of a unique global solution of the stochastic model are derived. Constructing an appropriate Lyapunov function, the conditions for the stochastic system to fluctuate around endemic equilibrium of the deterministic system are derived. Stationary distribution and ergodicity for the new co-infection model are also studied. Numerical simulations are carried out to validate theoretical results. It is observed that when the white noise intensities are larger than certain thresholds and the associated stochastic reproduction numbers are less than unity, both strains die out and go into extinction with unit probability. More-over, it is observed that, for weak white noise intensities, the solution of the stochastic system fluctuates around the endemic equilibrium (EE) of the deterministic model. Frequency distributions are also studied to show random fluctuations due to stochastic white noise intensities. The results presented herein also reveal the impact of vaccination in reducing the co-circulation of SARS-CoV-2 variants within a given population. © 2022 International Association for Mathematics and Computers in Simulation (IMACS)

Towards increasing synergistic effects of resilience strategies in supply chain network design

The recent COVID-19 pandemic showed that supply chain resilience is essential for continuity of many businesses, especially retail chains. However, there are still some challenges that have received little attention in the resilient supply chain network design (RSCND) literature. While numerous resilience strategies have been proposed to make supply chain networks resilient against disruptions, very few papers have discussed why and how those resilience strategies are selected out of many potential candidates given various sources of disruption, i.e., natural, man-made, and pandemic-oriented disruptions. The aim of this paper is to propose a multi-methodological approach, based on resource dependence theory and two-stage stochastic programming, for choosing the right resilience strategies in a RSCND problem considering their positive and negative synergistic effects under resource constraints. These interactions among resilience strategies can be referred to as supply chain dynamics. We then present a novel approach for determining the most suitable combination of candidate strategies with respect to these synergistic effects. The criticality of nodes and the susceptibility of the network in different echelons are also examined via simulating the disruptive risks in hidden and unexpected places. We provide a case study from the retail industry that illustrates the potentially significant impacts of network disruptions. Via extensive stress-testing, we show the benefits of applying multiple resilience capabilities simultaneously. Our findings demonstrate the importance of considering synergistic effects among resilience strategies under budget limitations for supply chain resilience. © 2022 Elsevier Ltd

Two-Stage Multi-Objective Stochastic Model on Patient Transfer and Relief Distribution in Lockdown Area of COVID-19.

The outbreak of an epidemic disease may cause a large number of infections and a slightly higher death rate. In response to epidemic disease, both patient transfer and relief distribution are significant to reduce corresponding damage. This study proposes a two-stage multi-objective stochastic model (TMS-PTRD) considering pre-pandemic preparedness measures and post-pandemic relief operations. The proposed model considers the following four objectives: the total number of untreated infected patients, the total transfer time, the overall cost, and the equity distribution of relief supplies. Before an outbreak, the locations of temporary relief distribution centers (TRDCs) and the inventory levels of established TRDCs should be determined. After an outbreak, the locations of temporary hospitals (THs), the locations of designated hospitals (DHs), the transfer plans for patients, and the relief distribution should be determined. To solve the TMS-PTRD model, we address an improved preference-inspired co-evolutionary algorithm named the PICEA-g-AKNN algorithm, which is embedded with a novel similarity distance and three different tailored evolutionary strategies. A real-world case study of Hunan of China and 18 test instances are randomly generated to evaluate the TMS-PTRD model. The finding shows that the PICEA-g-AKNN algorithm is better than some most widely used multi-objective algorithms.

Extinction and persistence of a stochastic delayed Covid-19 epidemic model.

We formulated a Coronavirus (COVID-19) delay epidemic model with random perturbations, consisting of three different classes, namely the susceptible population, the infectious population, and the quarantine population. We studied the proposed problem to derive at least one unique solution in the positive feasweible region of the non-local solution. Sufficient conditions for the extinction and persistence of the proposed model are established. Our results show that the influence of Brownian motion and noise on the transmission of the epidemic is very large. We use the first-order stochastic Milstein scheme, taking into account the required delay of infected individuals.

Dynamic Modeling and Identification of the COVID-19 Stochastic Dispersion

In this work, the stochastic dispersion of novel coronavirus disease 2019 (COVID-19) at the borders between France and Italy has been considered using a multi-input multi-output stochastic model. The physical effects of wind, temperature and altitude have been investigated as these factors and physical relationships are stochastic in nature. Stochastic terms have also been included to take into account the turbulence effect, and the r and om nature of the above physical parameters considered. Then, a method is proposed to identify the developed model's order and parameters. The actual data has been used in the identification and prediction process as a reference. These data have been divided into two parts: The first part is used to calculate the stochastic parameters of the model which are used to predict the COVID-19 level, while the second part is used as a check data. The predicted results are in good agreement with the check data. © 2022 IEEE.

The 2010 HIV Outbreak Among People Who Inject Drugs in Athens, Greece Could Have Been Prevented If the 2009 Undetected HCV Outbreak Had Been Detected. What Would Have Been the Savings If There Was an Outbreak Detection System?

Objectives: Wars, pandemics, and disasters that cause societal instability are referred to as "Big Events" and have been associated with outbreaks of infectious diseases. At the initiation of the 2008 severe economic recession in Greece (the last Big-Event before the pandemic), a hepatitis C virus (HCV) outbreak emerged among people who inject drugs (PWID) in 2009, which was the root of the 2010 Human Immunodeficiency Virus (HIV) outbreak. The HCV-outbreak was not detected, while that of HIV was identified in mid-2011. Given that HCV and HIV share common transmission routes, the HIV-interventions directly reduced the HIV-incidence by 78% and indirectly HCV-incidence by 64.8%. This study aims to assess what would have been the course of the two outbreaks, and their economic consequences if the 2009 HCV-outbreak had been timely detected. Method(s): A published, stochastic, dynamic model was used to simulate HCV and HIV transmission among PWID (Gountas et al. IJDP 2020, Gountas et al. PLOS 2021). The model was calibrated to reproduce the observed epidemiological parameters among PWID of Athens, Greece. The time-horizon of the analysis was 2002-2019 to capture second-order transmission effects. Result(s): Under the status-quo scenario, the cumulative HCV and HIV cases were 6480 (95% CrI: 6000, 6900) and 1360 (95% CrI: 400, 2600), respectively. If the HCV outbreak had been detected and integrated interventions had been initiated in 2009 or 2010, 440 and 970 new HCV cases and 740 and 1110 new HIV cases could have been averted by 2019, respectively. Concerning the costs of treating the new cases for both diseases, the existence of an efficient notification system would have saved 40.9-65.8 million by 2019. Conclusion(s): An accurate automated outbreak detection system among PWID is a cost-saving investment. During the COVID-19 pandemic, which is the current Big Event, HIV/HCV surveillance should be more intense to timely detect new outbreaks. Copyright © 2022

Stochastic dynamical behavior of COVID-19 model based on secondary vaccination.

This paper mainly studies the dynamical behavior of a stochastic COVID-19 model. First, the stochastic COVID-19 model is built based on random perturbations, secondary vaccination and bilinear incidence. Second, in the proposed model, we prove the existence and uniqueness of the global positive solution using random Lyapunov function theory, and the sufficient conditions for disease extinction are obtained. It is analyzed that secondary vaccination can effectively control the spread of COVID-19 and the intensity of the random disturbance can promote the extinction of the infected population. Finally, the theoretical results are verified by numerical simulations.

Comparison between Deterministic and Stochastic Model for Interaction (COVID-19) with Host Cells in Humans

In this paper, the deterministic and the stochastic models are proposed to study the interaction of the Coronavirus (COVID-19) with host cells inside the human body. In the deterministic model, the value of the basic reproduction number R0 determines the persistence or extinction of the COVID-19. If R0 < 1, one infected cell will transmit the virus to less than one cell, as a result, the person carrying the Coronavirus will get rid of the disease .If R0 > 1, the infected cell will be able to infect all cells that contain ACE receptors. The stochastic model proves that if α1 & α2 are sufficiently large then α1 & α2 maybe give us ultimate disease extinction although R0 > 1, and this facts also proved by computer simulation. © 2022 University of Baghdad. All rights reserved.

Deterministic and Stochastic Analysis of a Covid-19 Spread Model

This paper deals with the global dynamics of deterministic-stochastic COVID-19 mathematical model with quarantine class and incorporating a preventive vaccination. Lyapunov functions are utilized for the global stability of disease free equilibrium point and the graph theoretic method is used for the construction of Lyapunov function for positive equilibrium point. The stability of model is discussed regarding the reproductive number. Utilizing the non-standard finite difference scheme for the numerical solution of the deterministic model, the obtained results are shown graphically. Further, environmental noises are added to the model for description of stochastic model. Then we take out the existence and uniqueness of positive solution with extinction for infection. Finally, we solve numerically the stochastic model using Newton Polynomial scheme and present the results graphically.

Global asymptotic stability, extinction and ergodic stationary distribution in a stochastic model for dual variants of SARS-CoV-2

Several mathematical models have been developed to investigate the dynamics SARS-CoV-2 and its different variants. Most of the multi-strain SARS-CoV-2 models do not capture an important and more realistic feature of such models known as randomness. As the dynamical behavior of most epidemics, especially SARS-CoV-2, is unarguably influenced by several random factors, it is appropriate to consider a stochastic vaccination co-infection model for two strains of SARS-CoV-2. In this work, a new stochastic model for two variants of SARS-CoV-2 is presented. The conditions of existence and the uniqueness of a unique global solution of the stochastic model are derived. Constructing an appropriate Lyapunov function, the conditions for the stochastic system to fluctuate around endemic equilibrium of the deterministic system are derived. Stationary distribution and ergodicity for the new co-infection model are also studied. Numerical simulations are carried out to validate theoretical results. It is observed that when the white noise intensities are larger than certain thresholds and the associated stochastic reproduction numbers are less than unity, both strains die out and go into extinction with unit probability. More-over, it was observed that, for weak white noise intensities, the solution of the stochastic system fluctuates around the endemic equilibrium (EE) of the deterministic model. Frequency distributions are also studied to show random fluctuations due to stochastic white noise intensities. The results presented herein also reveal the impact of vaccination in reducing the co-circulation of SARS-CoV-2 variants within the given population.

Development Of an Android Mobile Phone Application for Finding Closed-Loop, Analytical Solutions to Dense Linear, Algebraic Equations for The Purpose of Mathematical Modelling in Healthcare and Neuroscience Research

Closed-loop, analytical solution of Linear, algebraic equation containing many unknown variables are found in many mathematical modeling equations and network analysis involved in healthcare and neuroscience research. Finding unique, analytical solution for linear, algebraic equations has diverse application in many fields. Computation of unique, non-trivial solutions to large array containing several variables is a computationally overwhelming task. The paper begins by introducing the concept of network analysis in modelling for healthcare and neuroscience research. A simple example of network modelling is the logistics of delivering vaccine like COVID19 from a company to Primary health center while maintaining cold-storage of the vaccine. This is followed by explanation of the working of computationally efficient, best-practice LU Decomposition with partial pivoting algorithm to solve dense linear equations. This is followed by narration of building, testing, obfuscation, compiling and release of an Android Graphic user interface application implementing the above methods. The final part of the paper examines the exceptional accuracy and efficiency of solving, dense matrix equations on Android Run Time machine using this approach. The calculated Poisson modelling of probability of Stochastic, Singularity event is = 3. 33 × 33−9per floating point operation a number derived after running 33, 333, 333, 333 floating-point operation runs. The mean execution time was 88.534 seconds, for solving matrix equation [A]withN=60 variables in performing N9=216000 computations. The whole working Android application containing many other tools is hosted on the GitHub and Figshare platforms along with additional graphs, dataset, Java, and Python programs used to complete this study.

A stochastic computing procedure to solve the dynamics of prevention in HIV system

The motive of this work is to find the numerical simulations of a dynamical HIV model along with the effects of prevention, i.e., HIPV nonlinear mathematical system. An advance computational framework using the procedures of Meyer neural networks (MNNs) together with the compotnecies of local/global search approaches is presented to solve the HIPV nonlinear mathematical system. The global and local operators will be used as genetic algorithm (GA) and interor-point algorithm (IPA), i.e., GAIPA. The dynamicis of HIPV mathematical system is classified into four categories, ‘T-cells attentiveness’, ‘Infected from disease, ‘Prevention actions’ and ‘Virus free particles. An error function is constructed using the differential system and its boundary conditions. The optimization of this function is presented through the hybridization computing paradigms of MWNNs-GAIPA. The correctness of the designed MWNNs-GAIPA is obtained by using the comparion of the obtained and reference solutions. The performance of this scheme is also acheived through the overlapping of the results with the accuracy of order 5 t 7 in the plots of absolute error. The reliability of the proposed MWNNs-GAIPA solver is observed by providing the statistical analysis by using different operators.