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1.
Journal of Computational & Graphical Statistics ; : 1-4, 2022.
Article in English | Academic Search Complete | ID: covidwho-2037207

ABSTRACT

The COVID-19 pandemic has called international scientific efforts to address important aspects of the pandemic. Data science and scientific modeling are extensively used to provide assessments and predictions for policy-making purposes.However, result communications need to be supported by a proper uncertainty quantification to assess variability in model predictions, by the identification of the key-uncertainty drivers. This information can be provided by statisticians with sensitivity analysis methods. Knowing the drivers of uncertainty supports effective policy-making.Concerning the COVID-19 pandemic diffusion, two recent investigations reveal intervention-related parameters as more important than epidemiological parameters in two different modeling exercises. This result can help prioritize policy decisions. [ FROM AUTHOR] Copyright of Journal of Computational & Graphical Statistics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

2.
Lecture Notes on Data Engineering and Communications Technologies ; 142:499-508, 2023.
Article in English | Scopus | ID: covidwho-2035010

ABSTRACT

The outbreak of SARS-CoV-2 in November 2019 has been modeled into the Susceptible-Infectious-Recovery (SIR) model initially. The second wave outbreak of the mutant SARS-CoV-2 has statistically proved to hold an exponential growth of mortality rate, especially in India. The infection-recovery strategy observed could be easily modeled to the SIR(Susceptible-Infectious-Recovery) model. Also, the studies included the exposure to the virus resulting in SIER (Susceptible-Infection-Exposure-Recovery) model. The daily statistics published by the World Health Organization (WHO) also reveal unavoidable statistics that represent the count of deaths. As of May 30, 2021, India has reported 27 million total infected cases, and 32 hundred thousand deaths have been reported. This alarms that the spread of pandemics cannot be visualized as a simple SIR model. The significant ratio between the infected and the mortals leads to remodeling the SIR model as SIR-M(Susceptible-Infectious-Recovery-Mortality) model. This paper includes the death count into the model and remodels the SIR model as SIR-M(Susceptible-Infectious-Recovery-Mortality) model. Our proposed model includes a factor, namely δ, which primarily depends on the medical condition. This δ is the primary cause of the deaths in the SARS-Cov-2-affected patients. We have studied the causes of mortality in COVID-19 patients and have validated our model with the real-time COVID dataset. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

3.
Lecture Notes on Data Engineering and Communications Technologies ; 147:419-431, 2023.
Article in English | Scopus | ID: covidwho-2035000

ABSTRACT

Over the past decade, the emergence of new infectious diseases in the world has become a serious problem requiring special attention. These days the COVID-19 epidemic is affecting not only the health sector but also the economy. Therefore, it is of great importance to build models that appropriately derive and preside over the spread of the epidemic to improve the control of epidemics. As well as to adopt appropriate strategies to avoid or at least mitigate its spread faster, different modeling methods have been proposed to build epidemiological models, we find the use of an agent-based model which makes it possible to reproduce the real behavior of the daily course of individuals already seen in the previous article [1], However this article presents in the same context stimulates the spread of covid using stochastic SIR model (Susceptible - Infected – Recovered) and its extension SVIRD (Susceptible - Vaccinated - Infected - Recovered – Death) which takes into consideration the vaccination parameter. Results: For a sample of 50 citizens network, we used a combination of simulations for the 4 parameters in SVIRD model, The result of the simulation shows that: The more connected a population is, the higher vaccination rates need to be to effectively protect the population. Also, the relationship between vaccination and infection rates looks more like an exponential decay and infection rates scale linearly with death rates for very low and very high numbers of connections. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

4.
Am J Infect Control ; 2022 Aug 07.
Article in English | MEDLINE | ID: covidwho-2035672

ABSTRACT

In the midst of the COVID - 19 pandemic, a multidisciplinary team implemented evidence-based strategies to eliminate catheter associated urinary tract infections (CAUTI), as defined by the National Healthcare Safety Network (NHSN) surveillance definition for those units included in the NHSN standardized infection ratio (SIR). The team evaluated indwelling urinary catheters daily for indication, implemented a urinary catheter order set, established a urinary catheter insertion checklist, and promoted use of external urinary diversion devices. The facility NHSN SIR for CAUTI was 0.37 in 2019, 0.23 in 2020, and 0.00 in 2021. A collaborative approach decreasing hospital acquired infections may be effective even in a climate of increased acuity, increased length of stay, and staffing challenges.

5.
Journal of Social Computing ; 3(2):182-189, 2022.
Article in English | Scopus | ID: covidwho-2026290

ABSTRACT

Compartmental pandemic models have become a significant tool in the battle against disease outbreaks. Despite this, pandemic models sometimes require extensive modification to accurately reflect the actual epidemic condition. The Susceptible-Infectious-Removed (SIR) model, in particular, contains two primary parameters: the infectious rate parameter ß and the removal rate parameter y, in addition to additional unknowns such as the initial infectious population. Adding to the complexity, there is an obvious challenge to track the evolution of these parameters, especially ß and y, over time which leads to the estimation of the reproduction number for the particular time window, RT. This reproduction number may provide better understanding on the effectiveness of isolation or control measures. The changing RT values (evolving over time window) will lead to even more possible parameter scenarios. Given the present Coronavirus Disease 2019 (COVID-19) pandemic, a stochastic optimization strategy is proposed to fit the model on the basis of parameter changes over time. Solutions are encoded to reflect the changing parameters of ßT and γt, allowing the changing RT to be estimated. In our approach, an Adaptive Differential Evolution (ADE) and Particle Swarm Optimization (PSO) are used to fit the curves into previously recorded data. ADE eliminates the need to tune the parameters of the Differential Evolution (DE) to balance the exploitation and exploration in the solution space. Results show that the proposed optimized model can generally fit the curves well albeit high variance in the solutions. © 2020 Tsinghua University Press.

6.
Mathematics ; 10(16):3008, 2022.
Article in English | ProQuest Central | ID: covidwho-2023884

ABSTRACT

Risk propagation is occurring as an exceptional challenge to supply chain management. Identifying which supplier has the greater possibility of interruptions is pivotal for managing the occurrence of these risks, which have a significant impact on the supply chain. Identifying and predicting how these risks propagate and understanding how these risks dynamically diffuse if control strategies are installed can help to better manage supply chain risks. Drawing on the complex systems and epidemiological literature, we research the impact of the global supply network structure on risk propagation and supply network health. The SIR model is used to dynamically identify and predict the risk status of the supply chain risk at different times. The results show that there is a significant relationship between network structure and risk propagation and supply network health. We demonstrate the importance of supply network visibility and of the extraction of the information of node firms. We build up an R package for geometric graphs and epidemics. This paper applies the R package to model the supply chain risk for an automotive manufacturing company. The R package provides a firm to construct the complicated interactions among suppliers and display how these interactions impact on risks. Theoretically, our study adapts a computational approach to contribute to the understanding of risk management and supply networks. Managerially, our study demonstrates how the supply chain network analysis approach can benefit the managers by developing a more holistic framework of system-wide risk propagation. This provides guidance for network governance policies, which will lead to healthier supply chains.

7.
Frontiers in Physics ; 10, 2022.
Article in English | Web of Science | ID: covidwho-2022846

ABSTRACT

Identifying a set of critical nodes with high propagation in complex networks to achieve maximum influence is an important task in the field of complex network research, especially in the background of the current rapid global spread of COVID-19. In view of this, some scholars believe that nodes with high importance in the network have stronger propagation, and many classical methods are proposed to evaluate node importance. However, this approach makes it difficult to ensure that the selected spreaders are dispersed in the network, which greatly affects the propagation ability. The VoteRank algorithm uses a voting-based method to identify nodes with strong propagation in the network, but there are some deficiencies. Here, we solve this problem by proposing the DILVoteRank algorithm. The VoteRank algorithm cannot properly reflect the importance of nodes in the network topology. Based on this, we redefine the initial voting ability of nodes in the VoteRank algorithm and introduce the degree and importance of the line (DIL) ranking method to calculate the voting score so that the algorithm can better reflect the importance of nodes in the network structure. In addition, the weakening mechanism of the VoteRank algorithm only weakens the information of neighboring nodes of the selected nodes, which does not guarantee that the identified initial spreaders are sufficiently dispersed in the network. On this basis, we consider all the neighbors nodes of the node's nearest and next nearest neighbors, so that the crucial spreaders identified by our algorithm are more widely distributed in the network with the same initial node ratio. In order to test the algorithm performance, we simulate the DILVoteRank algorithm with six other benchmark algorithms in 12 real-world network datasets based on two propagation dynamics model. The experimental results show that our algorithm identifies spreaders that achieve stronger propagation ability and propagation scale and with more stability compared to other benchmark algorithms.

8.
Bmc Public Health ; 22(1), 2022.
Article in English | Web of Science | ID: covidwho-2021261

ABSTRACT

Background COVID-19 caused a worldwide outbreak leading the majority of human activities to a rough breakdown. Many stakeholders proposed multiple interventions to slow down the disease and number of papers were devoted to the understanding the pandemic, but to a less extend some were oriented socio-economic analysis. In this paper, a socio-economic analysis is proposed to investigate the early-age effect of socio-economic factors on COVID-19 spread. Methods Fifty-two countries were selected for this study. A cascade algorithm was developed to extract the R0 number and the day J*;these latter should decrease as the pandemic flattens. Subsequently, R0 and J* were modeled according to socio-economic factors using multilinear stepwise-regression. Results The findings demonstrated that low values of days before lockdown should flatten the pandemic by reducing J*. Hopefully, DBLD is only parameter to be tuned in the short-term;the other socio-economic parameters cannot easily be handled as they are annually updated. Furthermore, it was highlighted that the elderly is also a major influencing factor especially because it is involved in the interactions terms in R0 model. Simulations proved that the health care system could improve the pandemic damping for low elderly. In contrast, above a given elderly, the reproduction number R0 cannot be reduced even for developed countries (showing high HCI values), meaning that the disease's severity cannot be smoothed regardless the performance of the corresponding health care system;non-pharmaceutical interventions are then expected to be more efficient than corrective measures. Discussion The relationship between the socio-economic factors and the pandemic parameters R0 and J* exhibits complex relations compared to the models that are proposed in the literature. The quadratic regression model proposed here has discriminated the most influencing parameters within the following approximated order, DLBL, HCI, Elderly, Tav, CO2, and WC as first order, interaction, and second order terms. Conclusions This modeling allowed the emergence of interaction terms that don't appear in similar studies;this led to emphasize more complex relationship between the infection spread and the socio-economic factors. Future works will focus on enriching the datasets and the optimization of the controlled parameters to short-term slowdown of similar pandemics.

9.
Simulation-Transactions of the Society for Modeling and Simulation International ; 2022.
Article in English | Web of Science | ID: covidwho-2020838

ABSTRACT

Despite advances in clinical care for the coronavirus (COVID-19) pandemic, population-wide interventions are vital to effectively manage the pandemic due to its rapid spread and the emergence of different variants. One of the most important interventions to control the spread of the disease is vaccination. In this study, an extended Susceptible-Infected Healed (SIR) model based on System Dynamics was designed, considering the factors affecting the rate of spread of the COVID-19 pandemic. The model predicts how long it will take to reach 70% herd immunity based on the number of vaccines administered. The designed simulation model is modeled in AnyLogic 8.7.2 program. The model was performed for three different vaccine supply scenarios and for Turkey with similar to 83 million population. The results show that, with a monthly supply of 15 million vaccines, social immunity reached the target value of 70% in 161 days, while this number was 11 7 days for 30 million vaccines and 98 days for 40 million vaccines.

10.
International Journal of Modern Physics B ; 2022.
Article in English | Web of Science | ID: covidwho-2020346

ABSTRACT

The epidemic often spreads along social networks and shows the effect of memorability on the outbreak. But the dynamic mechanism remains to be illustrated in the fractional-order epidemic system with a network. In this paper, Turing instability induced by the network and the memorability of the epidemic are investigated in a fractional-order epidemic model. A method is proposed to analyze the stability of the fractional-order model with a network through the Laplace transform. Meanwhile, the conditions of Turing instability and Hopf bifurcation are obtained to discuss the role of fractional order in the pattern selection and the Hopf bifurcation point. These results prove the fractional-order epidemic model may describe dynamical behavior more accurately than the integer epidemic model, which provides the bridge between Turing instability and the outbreak of infectious diseases. Also, the early warning area is discussed, which can be treated as a controlled area to avoid the spread of infectious diseases. Finally, the numerical simulation of the fractional-order system verifies the academic results is qualitatively consistent with the instances of COVID-19.

11.
2022 IEEE International Symposium on Information Theory, ISIT 2022 ; 2022-June:2255-2260, 2022.
Article in English | Scopus | ID: covidwho-2018915

ABSTRACT

In this paper, we introduce a "discrete-time SIR stochastic block model"that also allows for group testing and interventions on a daily basis. Our model can be regarded as a discrete version of the well-known continuous-time SIR stochastic network model [1] and relies on a specific type of weighted graph to capture the underlying community spread. Given that infection model, we then formulate a dynamic group-testing problem by asking: (a) what is the minimum number of tests needed everyday to identify all infections? and (b) are there nonadaptive group testing strategies that achieve this with vanishing error probability? Our results show that one can leverage the knowledge of the community infection model to compute a lower bound on the number of tests and also inform nonadaptive group testing algorithms, so that they can achieve (almost) the same performance as complete individual testing with a much smaller number of tests. Moreover, these algorithms are order-optimal, under specific conditions. © 2022 IEEE.

12.
13th IEEE Control and System Graduate Research Colloquium, ICSGRC 2022 ; : 114-119, 2022.
Article in English | Scopus | ID: covidwho-2018870

ABSTRACT

The COVID-19 virus pandemic in Indonesia has been going on since March 2020 and is still ongoing with conditions that need to be watched out for. This can be seen from the distribution of the daily active cases addition in Indonesia which is still changing dynamically. An alternative solution that can help to analyze countermeasures for the virus spread is modeling and simulating the spread of cases to estimate pandemic conditions that may occur in Indonesia. A common and widely used epidemiological-based model is the SIR model, which groups individuals affected by a pandemic into several compartments. Using this modeling and utilizing the concept of optimization technology, the modeling process can be carried out more efficiently and accurately. A model is developed, one of the derivatives of SIR modeling, namely SIR-FV, based on the concept of optimization to estimate and simulate various virus spread scenarios. There are 2 scenarios developed for analysis, namely the vaccination program scenario and the contact rate scenario. Based on the scenario simulation, it was found that the vaccination program could have a positive impact on efforts to deal with the COVID-19 pandemic more effectively when compared to the scenario without vaccination. The contact rate scenario also has a significant impact. However, the simulation also shows that if the vaccination program is not supported by adequate health protocols, it will not have any impact on the prevention effort. These results apply to the results of the SIR-FV model. Overall, it can be concluded that the developed model can carry out all of its functions as needed, with the level of accuracy through the MAPE metric reaching 0.012 for the SIRFV model. © 2022 IEEE.

13.
Journal of Physics: Conference Series ; 2314(1):012007, 2022.
Article in English | ProQuest Central | ID: covidwho-2017573

ABSTRACT

In March 2020, the World Health Organization (WHO) declared COVID-19 as a global pandemic caused by severe acute respiratory syndrome. This virus is referred to as SARS-CoV-2 and the associated disease is COVID-19. It is an infectious disease that can easily be transmitted via respiratory droplets through direct or indirect contact. This paper presents an epidemiological model of COVID-19 in Malaysia by using Susceptible-Infected-Removed (SIR) as a forecasting model. Forecasting is a technique used to predict or estimate the trend or rate of change for future events. This method can provide a good forecasting result for evaluating public health and social measures in response to the COVID-19 epidemic and also to make timely plans.

14.
2022 3rd International Conference on Computer Information and Big Data Applications, CIBDA 2022 ; : 84-89, 2022.
Article in English | Scopus | ID: covidwho-2012685

ABSTRACT

Considering the transmission mechanism of COVID-19 and the isolation measures adopted, a SIR model with isolation measures and nonlinear infection rate was established, and the transmission trend of COVID-19 was obtained by simulation. The comparison of simulation results with COVID-19 data suggests that isolation measures have played a key role in controlling the outbreak. Different execution times of isolation measures were set in the model and multiple sets of simulation experiments were performed. The results showed that isolation measures should be implemented as soon as possible in order to control the epidemic as soon as possible. At the same time, in order to better control the development of the epidemic, the control in the later stages of the epidemic should not be reduced in strength. The research results can provide theoretical basis and guidance for the scientific prevention and control of COVID-19 and other large-scale infectious diseases in the future. © VDE VERLAG GMBH - Berlin - Offenbach.

15.
Chaos, Solitons & Fractals ; : 112671, 2022.
Article in English | ScienceDirect | ID: covidwho-2007582

ABSTRACT

The level of unpredictability of the COVID-19 pandemics poses a challenge to effectively model its dynamic evolution. In this study we incorporate the inherent stochasticity of the SARS-CoV-2 virus spread by reinterpreting the classical compartmental models of infectious diseases (SIR type) as chemical reaction systems modelled via the Chemical Master Equation and solved by Monte Carlo Methods. Our model predicts the evolution of the pandemics at the level of municipalities, incorporating for the first time (i) a variable infection rate to capture the effect of mitigation policies on the dynamic evolution of the pandemics (ii) SIR-with-jumps taking into account the possibility of multiple infections from a single infected person and (iii) data of viral load quantified by RT-qPCR from samples taken from Wastewater Treatment Plants. The model has been successfully employed for the prediction of the COVID-19 pandemics evolution in small and medium size municipalities of Galicia (Northwest of Spain).

16.
Mathematical Biosciences and Engineering ; 19(12):11854-11867, 2022.
Article in English | Web of Science | ID: covidwho-2006289

ABSTRACT

Infectious diseases generally spread along with the asymmetry of social network propagation because the asymmetry of urban development and the prevention strategies often affect the direction of the movement. But the spreading mechanism of the epidemic remains to explore in the directed network. In this paper, the main effect of the directed network and delay on the dynamic behaviors of the epidemic is investigated. The algebraic expressions of Turing instability are given to show the role of the directed network in the spread of the epidemic, which overcomes the drawback that undirected networks cannot lead to the outbreaks of infectious diseases. Then, Hopf bifurcation is analyzed to illustrate the dynamic mechanism of the periodic outbreak, which is consistent with the transmission of COVID-19. Also, the discrepancy ratio between the imported and the exported is proposed to explain the importance of quarantine policies and the spread mechanism. Finally, the theoretical results are verified by numerical simulation.

17.
Systems Research and Behavioral Science ; 2022.
Article in English | Web of Science | ID: covidwho-2003644

ABSTRACT

This study systematically reviews applications of three simulation approaches, that is, system dynamics model (SDM), agent-based model (ABM) and discrete event simulation (DES), and their hybrids in COVID-19 research and identifies theoretical and application innovations in public health. Among the 372 eligible papers, 72 focused on COVID-19 transmission dynamics, 204 evaluated both pharmaceutical and non-pharmaceutical interventions, 29 focused on the prediction of the pandemic and 67 investigated the impacts of COVID-19. ABM was used in 275 papers, followed by 54 SDM papers, 32 DES papers and 11 hybrid model papers. Evaluation and design of intervention scenarios are the most widely addressed area accounting for 55% of the four main categories, that is, the transmission of COVID-19, prediction of the pandemic, evaluation and design of intervention scenarios and societal impact assessment. The complexities in impact evaluation and intervention design demand hybrid simulation models that can simultaneously capture micro and macro aspects of the socio-economic systems involved.

18.
Philos Trans A Math Phys Eng Sci ; 380(2233): 20210301, 2022 Oct 03.
Article in English | MEDLINE | ID: covidwho-1992459

ABSTRACT

We present a method for rapid calculation of coronavirus growth rates and [Formula: see text]-numbers tailored to publicly available UK data. We assume that the case data comprise a smooth, underlying trend which is differentiable, plus systematic errors and a non-differentiable noise term, and use bespoke data processing to remove systematic errors and noise. The approach is designed to prioritize up-to-date estimates. Our method is validated against published consensus [Formula: see text]-numbers from the UK government and is shown to produce comparable results two weeks earlier. The case-driven approach is combined with weight-shift-scale methods to monitor trends in the epidemic and for medium-term predictions. Using case-fatality ratios, we create a narrative for trends in the UK epidemic: increased infectiousness of the B1.117 (Alpha) variant, and the effectiveness of vaccination in reducing severity of infection. For longer-term future scenarios, we base future [Formula: see text] on insight from localized spread models, which show [Formula: see text] going asymptotically to 1 after a transient, regardless of how large the [Formula: see text] transient is. This accords with short-lived peaks observed in case data. These cannot be explained by a well-mixed model and are suggestive of spread on a localized network. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.


Subject(s)
Coronavirus , Epidemics , Epidemics/prevention & control , Reproduction , United Kingdom/epidemiology
19.
Ima Journal of Applied Mathematics ; : 16, 2022.
Article in English | Web of Science | ID: covidwho-1978230

ABSTRACT

A simple and explicit expression of the solution of the SIR epidemiological model of Kermack and McKendrick is constructed in the asymptotic limit of large basic reproduction numbers R-0. The proposed formula yields good qualitative agreement already when R-0 >= 3 and rapidly becomes quantitatively accurate as larger values of R-0 are assumed. The derivation is based on the method of matched asymptotic expansions, which exploits the fact that the exponential growing phase and the eventual recession of the outbreak occur on distinct time scales. From the newly derived solution, an analytical estimate of the time separating the first inflexion point of the epidemic curve from the peak of infections is given. Finally, we use the same method on the SEIR model and find that the inclusion of the 'exposed' population in the model can dramatically alter the time scales of the outbreak.

20.
Science and Technology Indonesia ; 7(3):400-408, 2022.
Article in English | Scopus | ID: covidwho-1975737

ABSTRACT

Analysis of data on COVID-19 cases in Indonesia is shown by using the Susceptible Vaccine Infected Removed (SVIR) in this article. In the previous research, cases in the period March-May 2021 were studied, and the reproduction number was computed based on the Susceptible Infected Removed (SIR) model. The prediction did not agree with the real data. Therefore the objective of this article is to improve the model by adding the vaccine variable leading to the new model called the SVIR model as the novelty of this article. The used data are collected from COVID-19 cases of the Indonesian population published by the Indonesian government from March 2020-April 2022. However, the vaccinated persons with COVID-19 cases have been recorded since January 2022. Therefore the models rely on the period January 2021-March 2022, where the parameters in the SIR and SVIR models are determined in this period. The method used is discretizing the models into linear systems, and these systems are solved by Ordinary Least Square (OLS) for time-dependent parameters. It is assumed that the birth rate and death rate in the considered period are constant. Additionally, individuals who have recovered from COVID-19 will not be infected again, and vaccination is not necessarily twice. Furthermore, individuals who have been vaccinated will not be infected with the COVID-19 virus. The SVIR model has captured 3 waves of COVID-19 cases that are appropriate to the real situation in Indonesia from January 2021-March 2022. Additionally, the reproduction numbers as functions of time have been generated. The fluctuations of reproduction numbers agree with the real data. For further research, different regions such as districts in Java and other islands will also be analyzed as the implication of this research. © 2022, Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya. All rights reserved.

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