scholarly journals A NUMERICAL SIMULATION OF THE COVID-19 EPIDEMIC IN ARGENTINA USING THE SEIR MODEL

2021 ◽  
Vol 51 (3) ◽  
pp. 179-184
Author(s):  
Patricia Gauzellino ◽  
Gabriela Savioli ◽  
José Carcione ◽  
Juan Santos ◽  
Alejandro Ravecca ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Buenos Aires ◽  
Transmission Probability ◽  
Control Measures ◽  
Model Parameters ◽  
Fatality Rate ◽  
Reproduction Ratio ◽  
Disease Evolution ◽  
Seir Model ◽  
Rate Transmission

A pandemic caused by a new corona virus has spread worldwide, affecting Argentina. We implement an SEIR model to analyze the disease evolution in Buenos Aires and neighboring cities.The model parameters are calibrated using the number of casualties officially reported. Since infinite solutions honor the data, we show different cases. In all of them the reproduction ratio R0 decreasesafter early lockdown, but then raises, probably due to an increase in the community circulation of the virus. Therefore it is mandatory to reverse this growing trend in R0 by applying control strategiesto avoid a high number of infectious and dead individuals. The model provides an effective procedure to estimate epidemic parameters (fatality rate, transmission probability, infection and incubationperiods) and monitor control measures during the epidemic evolution.

scholarly journals A simulation of a COVID-19 epidemic based on a deterministic SEIR model

2020 ◽  
Author(s):  
José M. Carcione ◽  
Juan E. Santos ◽  
Claudio Bagaini ◽  
Jing Ba
Keyword(s):  
Incubation Period ◽  
Initial Conditions ◽  
Accurate Determination ◽  
Infectious Period ◽  
Fatality Rate ◽  
Epidemic Disease ◽  
Reproduction Ratio ◽  
Seir Model ◽  
Social Distancing ◽  
Infected People

AbstractAn epidemic disease caused by a new coronavirus has spread in Northern Italy with a strong contagion rate. We implement an SEIR model to compute the infected population and number of casualties of this epidemic. The example may ideally regard the situation in the Italian Region of Lombardy, where the epidemic started on February 24, but by no means attempts to perform a rigorous case study in view of the lack of suitable data and uncertainty of the different parameters, namely, the variation of the degree of home isolation and social distancing as a function of time, the number of initially exposed individuals and infected people, the incubation and infectious periods and the fatality rate.First, we perform an analysis of the results of the model, by varying the parameters and initial conditions (in order the epidemic to start, there should be at least one exposed or one infectious human). Then, we consider the Lombardy case and calibrate the model with the number of dead individuals to date (April 28, 2020) and constraint the parameters on the basis of values reported in the literature. The peak occurs at day 37 (March 31) approximately, when there is a rapid decrease, with a reproduction ratio R0 = 3 initially, 1.36 at day 22 and 0.78 after day 35, indicating different degrees of lockdown. The predicted death toll is almost 15325 casualties, with 2.64 million infected individuals at the end of the epidemic. The incubation period providing a better fit of the dead individuals is 4.25 days and the infectious period is 4 days, with a fatality rate of 0.00144/day [values based on the reported (official) number of casualties]. The infection fatality rate (IFR) is 0.57 %, and 2.36 % if twice the reported number of casualties is assumed. However, these rates depend on the initially exposed individuals. If approximately nine times more individuals are exposed, there are three times more infected people at the end of the epidemic and IFR = 0.47 %. If we relax these constraints and use a wider range of lower and upper bounds for the incubation and infectious periods, we observe that a higher incubation period (13 versus 4.25 days) gives the same IFR (0.6 % versus 0.57 %), but nine times more exposed individuals in the first case. Other choices of the set of parameters also provide a good fit of the data, but some of the results may not be realistic. Therefore, an accurate determination of the fatality rate and characteristics of the epidemic is subject to the knowledge of precise bounds of the parameters.Besides the specific example, the analysis proposed in this work shows how isolation measures, social distancing and knowledge of the diffusion conditions help us to understand the dynamics of the epidemic. Hence, the importance to quantify the process to verify the effectiveness of the lockdown.

scholarly journals Forecasting and Analysis of Time Variation of Parameters of COVID-19 Infection in China Using An Improved SEIR Model

2020 ◽  
Author(s):  
Guoping Zhang ◽  
Huaji Pang ◽  
Yifei Xue ◽  
Yu Zhou ◽  
Ruliang Wang
Keyword(s):  
Prevention And Control ◽  
Control Measures ◽  
World Health ◽  
Model Parameters ◽  
Cure Rate ◽  
Seir Model ◽  
The World ◽  
The Government ◽  
Prevention And Control Measures ◽  
And Control

Abstract Background: Due to the emergency pandemic threat, the COVID-19 has attracted widespread attention around the world. Common symptoms of infection were fever, cough, and myalgia fatigue. On January 31, 2020, the World Health Organization (WHO) declares this outbreak a Public Health Emergency of International Concern (PHEIC).Methods: In order to study the spread of the novel coronavirus pneumonia, this paper proposed an improved SEIR model to simulate the spread of the virus, which includes the effect factor of government intervention. The model parameters are determined based on the daily reported statistical data (up to February 8) of confirmed, suspected, cured, and death. According to utilize the spread rate, the probability of infection of the suspected, the probability of the suspected becoming a confirmed one, the cure rate, the mortality rate, and the quarantine ratio, we performed simulations and parameter calibrations at three region levels, i.e., China, Hubei and non-Hubei respectively. In addition, considering that the government initiated effective prevention and control measures after the outbreak, this paper dynamically estimates all the parameters of the proposed model.Results: The simulation reveals that the parameters of non-Hubei region are not significantly different from Hubei’s. Hubei Province has a high transmission rate, low cure rate, high probability of infection, low effective quarantine rate. since January 31, with the continuous strengthening of epidemic prevention and control measures, all parameters of the model have changed significantly. The parameters of Hubei and non-Hubei regions have the same trend. The trend of all parameters is now moving in a direction that is conducive to reducing the number of confirmed, suspected and fatal cases.Conclusions: The number of infections of the virus initially showed a rapid increase in the trend, and the number of infectious case showed a clear downward shape. With the government to take a variety of prevention and control measures and the efforts of the general medical staff, the number of infection curve on February 22 appeared in the top of the arc pattern, indicating that the inflection point began to appear, but the decline in the number of infections slowly.

scholarly journals High COVID-19 transmission potential associated with re-opening universities can be mitigated with layered interventions

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ellen Brooks-Pollock ◽  
Hannah Christensen ◽  
Adam Trickey ◽  
Gibran Hemani ◽  
Emily Nixon ◽  
...  
Keyword(s):  
Prediction Interval ◽  
Mitigation Strategies ◽  
Control Measures ◽  
Transmission Model ◽  
Model Parameters ◽  
Infection Rates ◽  
First Year Students ◽  
Transmission Potential ◽  
Plausible Model ◽  
Effective Option

AbstractControlling COVID-19 transmission in universities poses challenges due to the complex social networks and potential for asymptomatic spread. We developed a stochastic transmission model based on realistic mixing patterns and evaluated alternative mitigation strategies. We predict, for plausible model parameters, that if asymptomatic cases are half as infectious as symptomatic cases, then 15% (98% Prediction Interval: 6–35%) of students could be infected during the first term without additional control measures. First year students are the main drivers of transmission with the highest infection rates, largely due to communal residences. In isolation, reducing face-to-face teaching is the most effective intervention considered, however layering multiple interventions could reduce infection rates by 75%. Fortnightly or more frequent mass testing is required to impact transmission and was not the most effective option considered. Our findings suggest that additional outbreak control measures should be considered for university settings.

scholarly journals Modifying the network-based stochastic SEIR model to account for quarantine: an application to COVID-19

2021 ◽  
Vol 10 (s1) ◽  
Author(s):  
Chris Groendyke ◽  
Adam Combs
Keyword(s):  
Network Model ◽  
Transmission Rate ◽  
Disease Spread ◽  
Contact Network ◽  
Model Parameters ◽  
Seir Model ◽  
Disease States ◽  
Potential Contact ◽  
The Mean ◽  
Infectious State

Abstract Objectives: Diseases such as SARS-CoV-2 have novel features that require modifications to the standard network-based stochastic SEIR model. In particular, we introduce modifications to this model to account for the potential changes in behavior patterns of individuals upon becoming symptomatic, as well as the tendency of a substantial proportion of those infected to remain asymptomatic. Methods: Using a generic network model where every potential contact exists with the same common probability, we conduct a simulation study in which we vary four key model parameters (transmission rate, probability of remaining asymptomatic, and the mean lengths of time spent in the exposed and infectious disease states) and examine the resulting impacts on various metrics of epidemic severity, including the effective reproduction number. We then consider the effects of a more complex network model. Results: We find that the mean length of time spent in the infectious state and the transmission rate are the most important model parameters, while the mean length of time spent in the exposed state and the probability of remaining asymptomatic are less important. We also find that the network structure has a significant impact on the dynamics of the disease spread. Conclusions: In this article, we present a modification to the network-based stochastic SEIR epidemic model which allows for modifications to the underlying contact network to account for the effects of quarantine. We also discuss the changes needed to the model to incorporate situations where some proportion of the individuals who are infected remain asymptomatic throughout the course of the disease.

scholarly journals COVID-19 in Sudan

2021 ◽  
Vol 15 (02) ◽  
pp. 204-208
Author(s):  
Ayman Ahmed ◽  
Nouh Saad Mohamed ◽  
Sarah Misbah EL-Sadig ◽  
Lamis Ahmed Fahal ◽  
Ziad Bakri Abelrahim ◽  
...  
Keyword(s):  
Health Systems ◽  
Case Fatality Rate ◽  
Case Fatality ◽  
Control Measures ◽  
Religious Practices ◽  
Mass Gathering ◽  
Fatality Rate ◽  
High Case Fatality Rate ◽  
The Government ◽  
And Control

The steadily growing COVID-19 pandemic is challenging health systems worldwide including Sudan. In Sudan, the first COVID-19 case was reported on 13th March 2020, and up to 11 November 2020 there were 14,401 confirmed cases of which 9,535 cases recovered and the rest 3,750 cases were under treatment. Additionally, 1,116 deaths were reported, indicating a relatively high case fatality rate of 7.7%. Several preventive and control measures were implemented by the government of Sudan and health partners, including the partial lockdown of the country, promoting social distancing, and suspending mass gathering such as festivals and performing religious practices in groups. However, new cases still emerging every day and this could be attributed to the noncompliance of the individuals to the advocated preventive measurements.

Robustness Analysis of a Simple and Augmented Proportional Plus Derivative Controller in Trajectory Following Robots Using the Floquet Theory

2018 ◽  
Vol 13 (7) ◽  
Author(s):  
B. Sandeep Reddy ◽  
Ashitava Ghosal
Keyword(s):  
Numerical Simulation ◽  
Degrees Of Freedom ◽  
Floquet Theory ◽  
Robustness Analysis ◽  
Periodic Trajectory ◽  
Model Parameters ◽  
Asymptotically Stable ◽  
Pd Controller ◽  
Simulation Results ◽  
Trajectory Following

This paper deals with the issue of robustness in control of robots using the proportional plus derivative (PD) controller and the augmented PD controller. In the literature, a variety of PD and model-based controllers for multilink serial manipulator have been claimed to be asymptotically stable for trajectory tracking, in the sense of Lyapunov, as long as the controller gains are positive. In this paper, we first establish that for simple PD controllers, the criteria of positive controller gains are insufficient to establish asymptotic stability, and second that for the augmented PD controller the criteria of positive controller gains are valid only when there is no uncertainty in the model parameters. We show both these results for a simple planar two-degrees-of-freedom (2DOFs) robot with two rotary (R) joints, following a desired periodic trajectory, using the Floquet theory. We provide numerical simulation results which conclusively demonstrate the same.

Thermal–Mechanical Coupling Evaluation of the Panel Performance of a Prefabricated Cabin-Type Substation Based on Machine Learning

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 93
Author(s):  
Xiangsheng Lei ◽  
Jinwu Ouyang ◽  
Yanfeng Wang ◽  
Xinghua Wang ◽  
Xiaofeng Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Numerical Simulation ◽  
Fire Resistance ◽  
Bp Neural Network ◽  
Stress Test ◽  
Normal Operation ◽  
Model Parameters ◽  
Mechanical Coupling ◽  
The Impact

The panel performance of a prefabricated cabin-type substation under the impact of fires plays a vital role in the normal operation of the substation. However, current evaluations of the panel performance of substations under fire still focus on fire resistance tests, which seldom consider the relationship between fire behavior and the mechanical load of the panel under the impact of fires. Aiming at the complex and uncertain relationship between the thermal and mechanical performance of the substation panel under impact of fires, this paper proposes a machine learning method based on a BP neural network. First, the fire resistance test and the stress test of the panel is carried out, then a machine learning model is established based on the BP neural network. According to the collected data, the model parameters are obtained through a series of training and verification processes. Meanwhile, the correlation between the panel performance and fire resistance was obtained. Finally, related parameters are input into the thermal–mechanical coupling evaluation model for the substation panel performance to evaluate the fire resistance performance of the substation panel. To verify the correctness of the established model, numerical simulation of the fire test and stress test of the panel is conducted, and numerical simulation samples are predicted by the trained model. The results show that the prediction curve of neural network is closer to the real results compared with the numerical simulation, and the established model can accurately evaluate the thermal–mechanical coupling performance of the substation panel under fire.

scholarly journals A field survey of hand–arm vibration exposure in the UK utilities sector

2020 ◽  
Vol 27 (9) ◽  
pp. 2179-2198 ◽  
Author(s):  
David John Edwards ◽  
Iain Rillie ◽  
Nicholas Chileshe ◽  
Joesph Lai ◽  
M. Reza Hosseini ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Well Being ◽  
Senior Management ◽  
Control Measures ◽  
Model Parameters ◽  
Content Type ◽  
Study Results ◽  
Exposure Levels ◽  
The Uk ◽  
Health And Well Being

PurposeExcessive exposure to HAV can lead to hand–arm vibration syndrome (HAVS) which is a major health and well-being issue that can irreparably damage the neurological, vascular and muscular skeletal system. This paper reports upon field research analysis of the hand–arm vibration (HAV) exposure levels of utility workers in the UK construction sector when operating hand-held vibrating power tools.Design/methodology/approachAn empirical epistemological lens was adopted to analyse primary quantitative data on the management of hand-held tool trigger times (seconds) collected from field studies. To augment the analysis further, an interpretivist perspective was undertaken to qualitatively analyse interviews held with the participating company's senior management team after field study results. This approach sought to provide further depth and perspective on the emergent numerical findings.FindingsThe findings reveal that none of the operatives were exposed above the exposure limit value (ELV) and that 91.07% resided under the exposure action value (EAV). However, the Burr four parameter probability model (which satisfied the Anderson–Darling, Kolmogorov–Smirnov and chi-squared goodness of fit tests at α 0.01, 0.02, 0.05, 0.1 and 0.2 levels of significance) illustrated that given the current data distribution pattern, there was a 3% likelihood that the ELV will be exceeded. Model parameters could be used to: forecast the future probability of HAV exposure levels on other utility contracts and provide benchmark indicators to alert senior management to pending breaches of the ELV.Originality/valueHAV field trials are rarely conducted within the UK utilities sector, and the research presented is the first to develop probability models to predict the likelihood of operatives exceeding the ELV based upon field data. Findings presented could go some way to preserving the health and well-being of workers by ensuing that adequate control measures implemented (e.g. procuring low vibrating tools) mitigate the risk posed.

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