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1.
Ieee Access ; 10:106180-106190, 2022.
Article in English | Web of Science | ID: covidwho-2082950

ABSTRACT

Contacts between people are the main drivers of contagious respiratory infections. For this reason, limiting and tracking contacts is a key strategy for controlling the COVID-19 epidemic. Digital contact tracing has been proposed as an automated solution to scale up traditional contact tracing. However, the required penetration of contact tracing apps within a population to achieve a desired target in controlling the epidemic is currently under discussion within the research community. In order to understand the effects of digital contact tracing, several mathematical models have been studied. In this article, we propose a novel compartmental SEIR model with which it is possible, differently from the models in the related literature, to derive closed-form conditions regarding the control of the epidemic. These conditions are a function of the penetration of contact tracing applications and testing efficiency. Closed-form conditions are crucial for the understandability of models, and thus for decision makers (including digital contact tracing designers) to correctly assess the dependencies within the epidemic. Feeding COVID-19 data to our model, we find that digital contact tracing alone can rarely tame the epidemic: for unrestrained COVID-19, this would require a testing turnaround of around 1 day and app uptake above 80% of the population, which are very difficult to achieve in practice. However, digital contact tracing can still be effective if complemented with other mitigation strategies, such as social distancing and mask-wearing.

2.
Journal of the National Institute of Public Health ; 70(5):557-568, 2021.
Article in Japanese | GIM | ID: covidwho-2073723

ABSTRACT

Objectives: In Shiga Prefecture, the third wave of a novel coronavirus infection (COVID-IQ) caused an imminent tightness of hospital beds. In this study, we constructed a mathematical model of infectious disease to conduct a sensitivity analysis and evaluated the effectiveness of policy interventions and medical systems management to avoid the shortage of hospital beds.

3.
Ieee Access ; 10:98244-98258, 2022.
Article in English | Web of Science | ID: covidwho-2070260

ABSTRACT

Coronavirus disease (COVID-19) is one of the world's most challenging pandemics, affecting people around the world to a great extent. Previous studies investigating the COVID-19 pandemic forecast have either lacked generalization and scalability or lacked surveillance data. City administrators have also often relied heavily on open-loop, belief-based decision-making, preventing them from identifying and enforcing timely policies. In this paper, we conduct mathematical and numerical analyses based on closed-loop decisions for COVID-19. Combining epidemiological theories with machine learning models gives this study a more accurate prediction of COVID-19's growth, and suggests policies to regulate it. The Susceptible, Infectious, and Recovered (SIR) model was analyzed using a machine learning model to estimate the optimal constant parameters, which are the recovery and infection rates of the coupled nonlinear differential equations that govern the epidemic model. To modulate the optimized parameters that regulate pandemic suppression and mitigation, a systematically designed feedback-based strategy was implemented. We also used pulse width modulation to modify on-off signals in order to regulate policy enforcement according to established metrics, such as infection recovery ratios. It was possible to determine what type of policy should be implemented in the country, as well as how long it should be implemented. Using datasets from John Hopkins University for six countries, India, Iran, Italy, Germany, Japan, and the United States, we show that our 30-day prediction errors are almost less than 3%. Our model proposes a threshold mechanism for policy control that divides the policy implementation into seven states, for example, if Infection Recovery Ratio (IRR) >80, we suggest a complete lockdown, vs if 10 ¡IRR ¡20, we suggest encouraging people to stay at home and organizations to work at 50% capacity. All countries which implemented a policy control strategy at an early stage were accurately predicted by our model. Furthermore, it was determined that the implementation of closed-loop strategies during a pandemic at different times effectively controlled the pandemic.

4.
Progress in Fractional Differentiation and Applications ; 8(4):475-484, 2022.
Article in English | Scopus | ID: covidwho-2067444

ABSTRACT

2020 has witnessed a rapidly spread pandemic COVID-19 which is one of the worst in the history of mankind. Scientists believe that COVID-19 spreads mainly from a person to another. Recent researches consider bats as a vector for COVID-19. This paper suggests a variable fractional order model for COVID-19 to figure out how bats and hosts interact, and how the seafood market affect people. The proposed model assumes that infection cannot be recovered. The basic reproduction number R0 for real data on reported cases in Wuhan China was computed. Disease-free equilibrium points and proposed model stability are studied. © 2022 NSP

5.
Sustainability ; 14(19):12224, 2022.
Article in English | ProQuest Central | ID: covidwho-2066385

ABSTRACT

In recent years, with the rise of the Internet, e-commerce has become an important field of commodity sales. However, e-commerce is affected by many factors, and the wrong judgment of supply and marketing relationships will bring huge losses to operators. Therefore, it is of great significance to establish a model that can effectively achieve high precision sales prediction for ensuring the sustainable development of e-commerce enterprises. In this paper, we propose an e-commerce sales forecasting model that considers the features of many aspects of correlation. In the first layer of the model, the temporal convolutional network (TCN) is used to extract the deep temporal characteristics of univariate sales historical data, which ensures the integrity of temporal information of sales characteristics. In the second layer, the feature selection method based on reinforcement learning is used to filter the effective correlation feature set and combine it with the temporal feature after processing, which not only improves the amount of effective information input by the model, but also avoids the high feature dimension. The third layer of the reformer model learns all the features and pays different attention to the features with different degrees of importance, ensuring the stability of the sales forecast. In the experimental part, we compare the proposed model with the current advanced sales forecasting model, and we can find that the proposed model has higher stability and accuracy.

6.
Interfaces ; 52(5):395, 2022.
Article in English | ProQuest Central | ID: covidwho-2065084

ABSTRACT

The judges for the 2021 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research selected the five finalist papers featured in this special issue of the INFORMS Journal on Applied Analytics. The prestigious Wagner Prize-awarded for achievement in implemented operations research, management science, and advanced analytics-emphasizes the quality and originality of mathematical models along with clarity of written and oral exposition. This year's winning application describes the design and deployment of Eva, the Greek COVID-19 testing system used as Greece was opening up for tourism in 2020. The remaining four papers describe the stochastic modeling and mixed-integer programming system used to optimize the Atlanta police patrol zones for better police balance and reduced response time to emergency calls;Lyft's new priority dispatch system, which solves the ride-sharing productivity paradox whereby increases in efficiency do not benefit the drivers;the application of advanced analytics to assist local and federal law enforcement organizations in their efforts to disrupt sex-trafficking networks;and the development of a new after-sales service concept, which increases chip availability for ASML's customers.

7.
Journal of Mathematics ; 2022, 2022.
Article in English | ProQuest Central | ID: covidwho-2064324

ABSTRACT

The major goal of this study is to create an optimal technique for managing COVID-19 spread by transforming the SEIQR model into a dynamic (multistage) programming problem with continuous and discrete time-varying transmission rates as optimizing variables. We have developed an optimal control problem for a discrete-time, deterministic susceptible class (S), exposed class (E), infected class (I), quarantined class (Q), and recovered class (R) epidemic with a finite time horizon. The problem involves finding the minimum objective function of a controlled process subject to the constraints of limited resources. For our model, we present a new technique based on dynamic programming problem solutions that can be used to minimize infection rate and maximize recovery rate. We developed suitable conditions for obtaining monotonic solutions and proposed a dynamic programming model to obtain optimal transmission rate sequences. We explored the positivity and unique solvability nature of these implicit and explicit time-discrete models. According to our findings, isolating the affected humans can limit the danger of COVID-19 spreading in the future.

8.
BMJ : British Medical Journal (Online) ; 378, 2022.
Article in English | ProQuest Central | ID: covidwho-2064111

ABSTRACT

What should we do now to improve health in the future? For women with gestational diabetes, adherence to five healthy lifestyle habits over a quarter century of follow-up was associated with a 90% lower risk of developing type 2 diabetes, when compared with women who had none of these habits (doi:10.1136/bmj-2022-070312).1 Gestational diabetes is a strong marker of future illness, associated with a meaningful increase in later cardiovascular and cerebrovascular disease. Some though not all of the risk is due to the subsequent development of diabetes (doi:10.1136/bmj-2022-070244).2 Quite obviously, then, women with gestational diabetes are especially likely to benefit from public health measures aimed at helping them implement healthy habits.

9.
Industry 4.0 and Intelligent Business Analytics for Healthcare ; : 91-115, 2022.
Article in English | Scopus | ID: covidwho-2058238

ABSTRACT

Most of the physical phenomena in the actual world exhibit non-linear character. In recent years, significant advances have occurred in the design, construction, and development of mathematical/analytical models for the solution of these physical problems. If appropriate initial and boundary condition(s) are associated with the models, the whole system generates a mathematical problem. In this way, a mathematical model establishes a relationship between mathematics and the rest of the world (the physical world). Analytical models are developed (or modified) based on mathematical concepts and using mathematical languages and symbolism. The three major advantages of using mathematical tools over others in modelling are: • Mathematics can provide well-ordered rules for manipulation -essential in modelling • Mathematics gives unique result for an investigation Mathematics is capable of proving general results from which the results for particular cases may be deduced assigning appropriate values to the parameters (in the admissible range) involved in the investigation. Information about the physical system represented by the model is estimated qualitatively and quantitatively by solving the mathematical problem by applying the best possible mathematical technique. The selection of appropriate mathematical methods for the solution will depend on the purpose for which the model may be applied for investigation. The models are validated by comparing the analytical results of the mathematical problem with the behavior exhibited by the physical problem. If the two do not match, the model is modified by including more parameters or leading to rejection of the model. As the physical models are based on experimental investigation, so they are superior to the mathematical models constructed on the theoretical investigation. But a mathematical model has the advantage of studying the role of key parameters controlling the system within a short period. Mathematical modeling is essential in studying human physiological problems (brain injury, blood flow, population growth, and spread of COVID-19 etc) where experimental investigations cannot be performed to obtain adequate data. So, improved mathematical models are developed (sometimes old models are modified) using suitable symbolism to define the operation of a physical system. As most of the phenomena in the physical world cannot be described by mathematical objects (due to their non-linear character), mathematicians and scientists throughout the globe are continuously developing and modifying mathematical models to solve the practical problems effectively in the domain of physical science, bio-science, social science, medicine, statistics, management, engineering, and technology. They are extensively utilized in making predictions when a particular parameter is varied in a range. The models play a convincing role in health science not only in the prediction of occurrence of critical health irregularities but they may reduce the risk in the treatment of fatal diseases like cardiovascular and neurological dysfunctions, brain injury due to vehicular accidents and soccer games, novel coronavirus infection, absorption of medicine in the human metabolic system leading to morbidity and even death. Our best endeavours is to construct some sophisticated mathematical models for the following types of problems: • in the study of Brain Injury Problems • in the study of Blood Flow through an Atherosclerotic (diseased) Arterial Segment • in Decaying of the absorption of a medicine in human rheological system• in estimating the spread of a disease (COVID 19) growing or decaying exponentially • in estimating the population growth in the coming years.We sincerely believe that the book chapter will throw light on the latest developments in the field of mathematical modelling and thus reinforce and solidify the understanding of this ever expanding domain. © 2022 Nova Science Publishers, Inc.

10.
South East Asian Journal of Mathematics and Mathematical Sciences ; 18(2):331-348, 2022.
Article in English | Scopus | ID: covidwho-2057252

ABSTRACT

In this investigation, we discussed the SARS-CoV-2 virus into a system of equations and we apply the Conformable Fractional Differential Transformation Method (CFDTM) to COVID-19 mathematical model described by the system of non-linear conformable fractional order differential equations. The aspire of this study is to estimate the effectiveness of preventive measures, predicting future outbreaks and potential control strategies using the mathematical model. The impacts of various biological parameters on transmission dynamics of COVID-19 is examined. These results are based on different values of the fractional parameter and serve as a control parameter to identify the significant strategies for the control of the disease. In the end, the obtained results are demonstrated graphically to justify our theoretical findings. © 2022, RAMANUJAN SOCIETY OF MATHEMATICS AND MATHEMATICAL SCIENCES. All rights reserved.

11.
Revista de Investigacion en Agroproduccion Sustentable ; 6(1):1-9, 2022.
Article in Spanish | CAB Abstracts | ID: covidwho-2056879

ABSTRACT

Efficiency in shrimp farming is due to the use of an extensive system that includes fewer larvae per pool, which increases productivity, improves financial results, and even the environmental impacts generated during these operations. The objective of this study was to identify the factors that affect supply in the Ecuadorian shrimp sector. To this end, some statistical techniques such as linear regression and hypothesis testing were used. A bibliographic study was carried out regarding shrimp production in Ecuador, taking as reference the data provided by the National Chamber of Aquaculture and public control entities and reviewing the unprecedented impact on the current COVID19 sanitary crisis and the reduction of shrimp demand affecting Ecuador's income. Finally, it was observed that the research variables considered directly impact crustacean production, and a mathematical model was established.

12.
African Journal of Infectious Diseases ; 16(2):80-96, 2022.
Article in English | CAB Abstracts | ID: covidwho-2056737

ABSTRACT

Background: The 2'-O-methyltransferase is responsible for the capping of SARS-CoV-2 mRNA and consequently the evasion of the host's immune system. This study aims at identifying prospective natural inhibitors of the active site of SARS-CoV-2 2'O-methyltransferase (2'-OMT) through an in silico approach. Materials and Method: The target was docked against a library of natural compounds obtained from edible African plants using PyRx - virtual screening software. The antiviral agent, Dolutegravir which has a binding affinity score of -8.5 kcal mol-1 with the SARS-CoV-2 2'-OMT was used as a standard. Compounds were screened for bioavailability through the SWISSADME web server using their molecular descriptors. Screenings for pharmacokinetic properties and bioactivity were performed with PKCSM and Molinspiration web servers respectively. The PLIP and Fpocket webservers were used for the binding site analyses. The Galaxy webserver was used for simulating the time-resolved motions of the apo and holo forms of the target while the MDWeb web server was used for the analyses of the trajectory data.

13.
Disease Surveillance ; 37(6):802-806, 2022.
Article in Chinese | GIM | ID: covidwho-2055475

ABSTRACT

Objective: To introduce the principle and method ofa-Sutte model, establish a a-Sutte model by using software R, compare the fitting and prediction effects of thea-Sutte model and multiple seasonal autoregressive integrated moving average model, SARIMA model and provides reference for the application of thea-Sutte model in epidemic prediction.

14.
Advances in Mathematical Physics ; 2022, 2022.
Article in English | ProQuest Central | ID: covidwho-2053430

ABSTRACT

Vaccination is an effective way to prevent the spread of infectious diseases. In this study, we formulate a VSEIR mathematical model to explore the effects of vaccination rate, vaccine efficacy, and immune decline on the COVID-19 transmission. The existence and stability criteria of equilibrium states were determined by analyzing the model. Model analysis was performed. One of the interesting phenomena involved in this issue is that diseases may or may not die out when the basic reproduction number falls below unity (i.e., a backward bifurcation may exist and cause multistability). The disease eventually becomes endemic in the population when the basic reproduction number exceeds one. By comparing different vaccination rates, vaccine efficacy, and infection rate factors, the diseases can be eliminated, not only by vaccines but also by strict protective measures. In addition, we used the COVID-19 number of reported cases in Xiamen in September 2021 to fit the model, and the model and the reported data were well matched.

15.
Mathematical Problems in Engineering ; 2022, 2022.
Article in English | ProQuest Central | ID: covidwho-2053425

ABSTRACT

In the present paper, the SIR model with nonlinear recovery and Monod type equation as incidence rates is proposed and analyzed. The expression for basic reproduction number is obtained which plays a main role in the stability of disease-free and endemic equilibria. The nonstandard finite difference (NSFD) scheme is constructed for the model and the denominator function is chosen such that the suggested scheme ensures solutions boundedness. It is shown that the NSFD scheme does not depend on the step size and gives better results in all respects. To prove the local stability of disease-free equilibrium point, the Jacobean method is used;however, Schur–Cohn conditions are applied to discuss the local stability of the endemic equilibrium point for the discrete NSFD scheme. The Enatsu criterion and Lyapunov function are employed to prove the global stability of disease-free and endemic equilibria. Numerical simulations are also presented to discuss the advantages of NSFD scheme as well as to strengthen the theoretical results. Numerical simulations specify that the NSFD scheme preserves the important properties of the continuous model. Consequently, they can produce estimates which are entirely according to the solutions of the model.

16.
Doklady. Mathematics ; 106(1):230-235, 2022.
Article in English | ProQuest Central | ID: covidwho-2053147

ABSTRACT

A mathematical model is proposed that not only generates various scenarios of development, but also forms specific management measures aimed at suppressing the pandemic and restoring economic growth. The developed model of the mutual influence of the pandemic and the economy is not only a tool for effective and adequate forecasting, but is also capable of simulating various scenarios that may well correspond to real epidemiological processes. An advantage of the model is that the dynamics of the pandemic and GDP can be managed in practice in order to stabilize socioeconomic development.

17.
IEEE Transactions on Fuzzy Systems ; : 1-10, 2022.
Article in English | Scopus | ID: covidwho-2052094

ABSTRACT

In this manuscript we use triangular norms to model contact between susceptible and infected individuals in the susceptible-infected-recovered (SIR) epidemiological model. In the classical SIR model, the encounter between susceptible and infected individuals is traditionally modelled by the product of their densities (<inline-formula><tex-math notation="LaTeX">$SI$</tex-math></inline-formula>). That is, the encounter is modelled by the product t-norm. We use the COVID-19 data and extended versions of the SIR model whose encounters are modelled by four triangular norms, namely, product, minimum, Frank and Hamacher t-norms, to analyze the scenario in three countries: Germany, Italy, and Switzerland. We compare all versions of the SIR model based on these triangular norms, and we analyze their effectiveness in fitting data and determining important parameters for the pandemic, such as the basic and effective reproduction number. In addition, Frank and Hamacher triangular norms present an auxiliary parameter that can be interpreted as an indicator of control measure, which we show to be important in the current pandemic scenario. IEEE

18.
IEEE Control Systems Letters ; : 1-1, 2022.
Article in English | Scopus | ID: covidwho-2052058

ABSTRACT

Until the approval of vaccines at the end of 2020, societies relied on non-pharmaceutical interventions (NPIs) in order to control the COVID-19 pandemic. Spontaneous changes in individual behavior might have contributed to or counteracted epidemic control due to NPIs. For example, the population compliance to NPIs may have varied over time as people developed “epidemic fatigue" or altered their perception of the risk and severity of COVID-19. Whereas official measures are well documented, the behavioral response of the citizens is harder to capture. We propose a mathematical model of the societal response, taking into account three main effects: the citizen response dynamics, the authorities’NPIs, and the occurrence of unpreventable events that significantly alter the virus transmission rate. A key assumption is that a society has a waning memory of the epidemic effects, which reflects on both the severity of the authorities’NPIs and on the citizens’compliance to the prescribed rules. This, in turn, feeds back onto the transmission rate of the disease, such that a higher number of hospitalizations decreases the probability of transmission. We show that the model is able to reproduce the COVID-19 dynamics in terms of hospital admissions for several European countries during 2020 over surprisingly long time scales. Also, it is capable of capturing the effects of disturbances (for example the emergence of new virus variants) and can be exploited for implementing control actions to limit such effects. A possible application, illustrated in the paper, consists of exploiting the estimations based on the data of one country, to predict and control the evolution in another country, where the virus spreading is still in an earlier phase. IEEE

19.
International Journal of System Assurance Engineering and Management ; 13:828-841, 2022.
Article in English | ProQuest Central | ID: covidwho-2048611

ABSTRACT

Traditional statistical as well as artificial intelligence techniques are widely used for stock market forecasting. Due to the nonlinearity in stock data, a model developed using the traditional or a single intelligent technique may not accurately forecast results. Therefore, there is a need to develop a hybridization of intelligent techniques for an effective predictive model. In this study, we propose an intelligent forecasting method based on a hybrid of an Artificial Neural Network (ANN) and a Genetic Algorithm (GA) and uses two US stock market indices, DOW30 and NASDAQ100, for forecasting. The data were partitioned into training, testing, and validation datasets. The model validation was done on the stock data of the COVID-19 period. The experimental findings obtained using the DOW30 and NASDAQ100 reveal that the accuracy of the GA and ANN hybrid model for the DOW30 and NASDAQ100 is greater than that of the single ANN (BPANN) technique, both in the short and long term.

20.
Signa Vitae ; 18(5):86-94, 2022.
Article in English | CAB Abstracts | ID: covidwho-2040592

ABSTRACT

A few months after the onset of the coronavirus Disease 2019 (COVID-19) pandemic, the worse prognoses of acute myocardial infarction, ischemic and hemorrhagic stroke, and cardiac arrest were reported. This study aimed to investigate the changes in the characteristics and prognoses of these diseases in the emergency department (ED) over a year after pandemic's onset. This was a retrospective observational study. The year 2019 was defined as the pre-period, while the year from February 2020 to January 2021 was defined as the post-period. Adult patients diagnosed with acute myocardial infarction, ischemic stroke, hemorrhagic stroke, or cardiac arrest during the study period were included. The primary outcome was in-hospital mortality. Time series analyses using autoregressive integrated moving average (ARIMA)(p,d,q) model were performed to evaluate the changes between periods. A multivariable logistic regression analysis of factors affecting in-hospital mortality was performed. The proportions of patients with acute myocardial infarction (0.8% vs. 1.1%, p < 0.001), hemorrhagic stroke (1.0%vs. 1.2%, p = 0.011), and cardiac arrest (0.9% vs. 1.1%, p = 0.012) increased in the post-period. The post-period was independently associated with in-hospital mortality in acute myocardial infarction (adjusted odds ratio (aOR) 2.54, 95% confidence interval (95% CI) 1.06-6.08, p = 0.037) and hemorrhagic stroke (aOR 1.74, 95% CI 1.11-2.73, p = 0.016), but not for ischemic stroke or cardiac arrest. Over a year after onset of the COVID-19 pandemic in Korea, the number of patients with acute myocardial infarction, hemorrhagic stroke, and cardiac arrest in the ED increased. An independent association between the post-period and mortality was observed for acute myocardial infarction, and hemorrhagic stroke. This study provides important information for future studies and policies.

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