scholarly journals A data driven epidemic model to analyse the lockdown effect and predict the course of COVID-19 progress in India

2020 ◽  
Author(s):  
B.K. Sahoo ◽  
B.K. Sapra
Keyword(s):  
Infection Rate ◽  
Epidemic Model ◽  
Current Trend ◽  
Real Data ◽  
Daily Basis ◽  
Least Square ◽  
Control Measures ◽  
Data Driven ◽  
Peak Time ◽  
The Real

AbstractWe propose a data driven epidemic model using the real data on the infection, recovery and death cases for the analysis of COVID-19 progression in India. The model assumes continuation of existing control measures such as lockdown and quarantines, the suspected and confirmed cases and does not consider the scenario of 2nd surge of the epidemic due to any reason. The model is arrived after least square fitting of epidemic behaviour model based on theoretical formulation to the real data of cumulative infection cases reported between 24 March 2020 and 15 May 2020. The predictive capability of the model has been validated with real data of infection cases reported during May 15–30, 2020. A detailed analysis of model predictions in terms of future trend of COVID-19 progress individually in 18 states of India and India as a whole has been attempted. Infection rate in India as a whole is continuously decreasing with time and has reached 3 times lower than the initial infection rate after 6 weeks of lock down suggesting the effectiveness of the lockdown in containing the epidemic. Results suggest that India as a whole could see the peak and end of the epidemic in the month of July 2020 and January 2021. As per the current trend in the data, active infected cases in India may reach 2 lakhs at the peak time and total infected cases may reach around 14 lakhs. State-wise results have been discussed in the manuscript. However, the prediction may deviate particularly for longer dates, as assumptions of model cannot be met always in a real scenario. In view of this, a real time application (COV-IND Predictor) has been developed which automatically syncs the latest data from COVID19 dash board on daily basis and update the model input parameters and predictions of relevant results on daily basis. This application can serve as a practical tool for epidemic management decisions

Comparison of two different types of fractional-order COVID-19 distributed time-delay models with real data application

2021 ◽  
pp. 2150219
Author(s):  
Liangli Yang ◽  
Yongmei Su ◽  
Xinjian Zhuo
Keyword(s):  
Time Delay ◽  
Fractional Order ◽  
Distributed Delay ◽  
Real Data ◽  
Least Square ◽  
Order Model ◽  
The Real ◽  
Fractional Order Model ◽  
Different Types ◽  
Distributed Time Delay

The outbreak of COVID-19 has a great impact on the world. Considering that there are different infection delays among different populations, which can be expressed as distributed delay, and the distributed time-delay is rarely used in fractional-order model to simulate the real data, here we establish two different types of fractional order (Caputo and Caputo–Fabrizio) COVID-19 models with distributed time-delay. Parameters are estimated by the least-square method according to the report data of China and other 12 countries. The results of Caputo and Caputo–Fabrizio model with distributed time-delay and without delay, the integer-order model with distributed delay are compared. These show that the fractional-order model can be better in fitting the real data. Moreover, Caputo order is better in short-term time fitting, Caputo–Fabrizio order is better in long-term fitting and prediction. Finally, the influence of several parameters is simulated in Caputo order model, which further verifies the importance of taking strict quarantine measures and paying close attention to the incubation period population.

scholarly journals A Fractional Epidemic Model with Mittag-Leffler Kernel for COVID-19

2021 ◽  
Vol 16 (1) ◽  
pp. 39-56
Author(s):  
Hassan Aghdaoui ◽  
Mouhcine Tilioua ◽  
Kottakkaran Sooppy Nisar ◽  
Ilyas Khan
Keyword(s):  
Numerical Simulations ◽  
Epidemic Model ◽  
Fractional Derivatives ◽  
Real Data ◽  
Disease Evolution ◽  
Seir Model ◽  
The Real ◽  
Stability Results ◽  
Positivity And Boundedness

The aim is to explore a COVID-19 SEIR model involving Atangana-Baleanu Caputo type (ABC) fractional derivatives. Existence, uniqueness, positivity, and boundedness of the solutions for the model are established. Some stability results of the proposed system are also presented. Numerical simulations results obtained in this paper, according to the real data, show that the model is more suitable for the disease evolution.

scholarly journals Exploring the spread dynamics of COVID-19 in Morocco

2020 ◽  
Author(s):  
Mohamed NAJI
Keyword(s):  
Infection Rate ◽  
Transmission Rate ◽  
Real Data ◽  
Peak Position ◽  
Rate Function ◽  
Time Dependent ◽  
Death Rates ◽  
The Real ◽  
Spread Dynamics

Despite some similarities of the dynamic of COVID−19 spread in Morocco and other countries, the infection, recovery and death rates remain very variable. In this paper, we analyze the spread dynamics of COVID−19 in Morocco within a standard susceptible−exposed−infected−recovered−death (SEIRD) model. We have combined SEIRD model with a time−dependent infection rate function, to fit the real data of i) infection counts and ii) death rates due to COVID−19, for the period between March 2nd and Mai 15th 2020. By fitting the infection rate, SEIRD model placed the infection peak on 04/24/2020 and could reproduce it to a large extent on the expense of recovery and death rates. Fitting the SEIRD model to death rates gives rather satisfactory predictions with a maximum of infections on 04/06/2020. Regardless of the low peak position, the peak position, confirmed cases and transmission rate were well reproduced.

scholarly journals Wind Power Prediction Based on Nonlinear Partial Least Square

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Qian Wang ◽  
Yang Lei ◽  
Hui Cao
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wind Power ◽  
Real Data ◽  
Partial Least Square ◽  
Least Square ◽  
Power Prediction ◽  
Short Term ◽  
The Real ◽  
Wind Power Prediction

Wind power prediction is important for the smart grid safe operation and scheduling, and it can improve the economic and technical penetration of wind energy. The intermittent and the randomness of wind would affect the accuracy of prediction. According to the sequence correlation between wind speed and wind power data, we propose a method for short-term wind power prediction. The proposed method adopts the wind speed in every sliding data window to obtain the continuous prediction of wind power. Then, the nonlinear partial least square is adopted to map the wind speed under the time series to wind power. The model carries the neural network as the nonlinear function to describe the inner relation, and the outputs of hidden layer nodes are the extension term of the original independent input matrix to partial least squares regression. To verify the effectiveness of the proposed algorithm, the real data of wind power with different working conditions are adopted in experiments. The proposed method, backpropagation neural network, radial basis function neural network, support vector machine, and partial least square are performed on the real data and their effectiveness is compared. The experimental results show that the proposed algorithm has higher precision, and the real power running curves also verify that the proposed method can predict the wind power in short-term effectively.

scholarly journals Estimation of COVID-19 Transmission and Advice on Public Health Interventions

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2849
Author(s):  
Qingqing Ji ◽  
Xu Zhao ◽  
Hanlin Ma ◽  
Qing Liu ◽  
Yiwen Liu ◽  
...  
Keyword(s):  
Least Square ◽  
Control Measures ◽  
Research Progress ◽  
Model Parameters ◽  
Daily Lives ◽  
The Real ◽  
Containment Measures ◽  
Simulation Results ◽  
Novel Coronavirus ◽  
And Control

At the end of 2019, an outbreak of the novel coronavirus (COVID-19) made a profound impact on the country’s production and people’s daily lives. Up until now, COVID-19 has not been fully controlled all over the world. Based on the clinical research progress of infectious diseases, combined with epidemiological theories and possible disease control measures, this paper establishes a Susceptible Infected Recovered (SIR) model that meets the characteristics of the transmission of the new coronavirus, using the least square estimation (LSE) method to estimate the model parameters. The simulation results show that quarantine and containment measures as well as vaccine and drug development measures can control the spread of the epidemic effectively. As can be seen from the prediction results of the model, the simulation results of the epidemic development of the whole country and Nanjing are in agreement with the real situation of the epidemic, and the number of confirmed cases is close to the real value. At the same time, the model’s prediction of the prevention effect and control measures have shed new light on epidemic prevention and control.

Personal Audio System: Hearing Symptoms, Habits, and Sound Pressure Levels Measured in Real Ear and a Manikin

2020 ◽  
Vol 63 (6) ◽  
pp. 2016-2026
Author(s):  
Tamara R. Almeida ◽  
Clayton H. Rocha ◽  
Camila M. Rabelo ◽  
Raquel F. Gomes ◽  
Ivone F. Neves-Lobo ◽  
...  
Keyword(s):  
Sound Pressure ◽  
Daily Basis ◽  
Cross Sectional Study ◽  
Normal Hearing ◽  
Chi Square ◽  
Cross Sectional ◽  
The Real ◽  
Significant Difference ◽  
Sound Pressure Levels ◽  
Audio System

Purpose The aims of this study were to characterize hearing symptoms, habits, and sound pressure levels (SPLs) of personal audio system (PAS) used by young adults; estimate the risk of developing hearing loss and assess whether instructions given to users led to behavioral changes; and propose recommendations for PAS users. Method A cross-sectional study was performed in 50 subjects with normal hearing. Procedures included questionnaire and measurement of PAS SPLs (real ear and manikin) through the users' own headphones and devices while they listened to four songs. After 1 year, 30 subjects answered questions about their usage habits. For the statistical analysis, one-way analysis of variance, Tukey's post hoc test, Lin and Spearman coefficients, the chi-square test, and logistic regression were used. Results Most subjects listened to music every day, usually in noisy environments. Sixty percent of the subjects reported hearing symptoms after using a PAS. Substantial variability in the equivalent music listening level (Leq) was noted ( M = 84.7 dBA; min = 65.1 dBA, max = 97.5 dBA). A significant difference was found only in the 4-kHz band when comparing the real-ear and manikin techniques. Based on the Leq, 38% of the individuals exceeded the maximum daily time allowance. Comparison of the subjects according to the maximum allowed daily exposure time revealed a higher number of hearing complaints from people with greater exposure. After 1 year, 43% of the subjects reduced their usage time, and 70% reduced the volume. A volume not exceeding 80% was recommended, and at this volume, the maximum usage time should be 160 min. Conclusions The habit of listening to music at high intensities on a daily basis seems to cause hearing symptoms, even in individuals with normal hearing. The real-ear and manikin techniques produced similar results. Providing instructions on this topic combined with measuring PAS SPLs may be an appropriate strategy for raising the awareness of people who are at risk. Supplemental Material https://doi.org/10.23641/asha.12431435

Single Depth View Based Real-Time Reconstruction of Hand-Object Interactions

2021 ◽  
Vol 40 (3) ◽  
pp. 1-12
Author(s):  
Hao Zhang ◽  
Yuxiao Zhou ◽  
Yifei Tian ◽  
Jun-Hai Yong ◽  
Feng Xu
Keyword(s):  
Real Time ◽  
Synthetic Data ◽  
Real Data ◽  
Depth Image ◽  
Real Time System ◽  
The Real ◽  
Time Performance ◽  
Contact Constraint ◽  
Object Shapes ◽  
Object Interactions

Reconstructing hand-object interactions is a challenging task due to strong occlusions and complex motions. This article proposes a real-time system that uses a single depth stream to simultaneously reconstruct hand poses, object shape, and rigid/non-rigid motions. To achieve this, we first train a joint learning network to segment the hand and object in a depth image, and to predict the 3D keypoints of the hand. With most layers shared by the two tasks, computation cost is saved for the real-time performance. A hybrid dataset is constructed here to train the network with real data (to learn real-world distributions) and synthetic data (to cover variations of objects, motions, and viewpoints). Next, the depth of the two targets and the keypoints are used in a uniform optimization to reconstruct the interacting motions. Benefitting from a novel tangential contact constraint, the system not only solves the remaining ambiguities but also keeps the real-time performance. Experiments show that our system handles different hand and object shapes, various interactive motions, and moving cameras.

scholarly journals Eastern Cape Healthcare Workers Acquisition of SARS-CoV-2 (ECHAS): Cross-Sectional (Nested Cohort) Study Protocol

2021 ◽  
Vol 18 (1) ◽  
pp. 323
Author(s):  
Oladele Vincent Adeniyi ◽  
David Stead ◽  
Mandisa Singata-Madliki ◽  
Joanne Batting ◽  
Leo Hyera ◽  
...  
Keyword(s):  
Cohort Study ◽  
Infection Rate ◽  
Healthcare Workers ◽  
Health Facilities ◽  
Control Measures ◽  
Infection Prevention And Control ◽  
Eastern Cape ◽  
Rt Pcr ◽  
Cross Sectional ◽  
Increased Risk

Healthcare workers (HCWs) are at increased risk of infection by the virulent severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Though data exist on the positivity rate of the SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR) test as well as COVID-19-related deaths amongst HCWs in South Africa, the overall infection rate remains underestimated by these indicators. It is also unclear whether the humoral immune response after SARS-CoV-2 infection offers durable protection against reinfection. This study will assess the SARS-CoV-2 seroprevalence amongst HCWs in the Eastern Cape (EC) and examine the longitudinal changes (rate of decay) in the antibody levels after infection in this cohort. Using a multi-stage cluster sampling of healthcare workers in selected health facilities in the EC, a cross-sectional study of 2250 participants will be recruited. In order to assess the community infection rate, 750 antenatal women in the same settings will be recruited. Relevant demographic and clinical characteristics will be obtained by a self-administered questionnaire. A chemiluminescent microparticle immunoassay (CMIA) will be used for the qualitative detection of IgG antibodies against SARS-CoV-2 nucleocapsid protein. A nested cohort study will be conducted by performing eight-weekly antibody assays (X2) from 201 participants who tested positive for both SARS-CoV-2 RT-PCR and serology. Logistic regression models will be fitted to identify the independent risk factors for SARS-CoV-2 infection. The cumulative SARS-CoV-2 infection rate and infection fatality rate among the frontline HCWs will be estimated. In addition, the study will highlight the overall effectiveness of infection prevention and control measures (IPC) per exposure sites/wards at the selected health facilities. Findings will inform the South African Department of Health’s policies on how to protect HCWs better as the country prepares for the second wave of the SARS-CoV pandemic.

scholarly journals A fractional order epidemic model for the simulation of outbreaks of Ebola

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Weiqiu Pan ◽  
Tianzeng Li ◽  
Safdar Ali
Keyword(s):  
Numerical Solution ◽  
Root Mean Square ◽  
Fractional Order ◽  
Relative Error ◽  
Numerical Solutions ◽  
Real Data ◽  
Mean Square ◽  
Order Model ◽  
The Real ◽  
Fractional Orders

AbstractThe Ebola outbreak in 2014 caused many infections and deaths. Some literature works have proposed some models to study Ebola virus, such as SIR, SIS, SEIR, etc. It is proved that the fractional order model can describe epidemic dynamics better than the integer order model. In this paper, we propose a fractional order Ebola system and analyze the nonnegative solution, the basic reproduction number $R_{0}$ R 0 , and the stabilities of equilibrium points for the system firstly. In many studies, the numerical solutions of some models cannot fit very well with the real data. Thus, to show the dynamics of the Ebola epidemic, the Gorenflo–Mainardi–Moretti–Paradisi scheme (GMMP) is taken to get the numerical solution of the SEIR fractional order Ebola system and the modified grid approximation method (MGAM) is used to acquire the parameters of the SEIR fractional order Ebola system. We consider that the GMMP method may lead to absurd numerical solutions, so its stability and convergence are given. Then, the new fractional orders, parameters, and the root-mean-square relative error $g(U^{*})=0.4146$ g ( U ∗ ) = 0.4146 are obtained. With the new fractional orders and parameters, the numerical solution of the SEIR fractional order Ebola system is closer to the real data than those models in other literature works. Meanwhile, we find that most of the fractional order Ebola systems have the same order. Hence, the fractional order Ebola system with different orders using the Caputo derivatives is also studied. We also adopt the MGAM algorithm to obtain the new orders, parameters, and the root-mean-square relative error which is $g(U^{*})=0.2744$ g ( U ∗ ) = 0.2744 . With the new parameters and orders, the fractional order Ebola systems with different orders fit very well with the real data.

Petrophysical Facies Classification Based on Fuzzy Inference Networks

2013 ◽  
Vol 634-638 ◽  
pp. 4017-4021
Author(s):  
Jun Hui Pan ◽  
Hui Wang ◽  
Xiao Gang Yang
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Reasoning ◽  
Real Data ◽  
Analysis Data ◽  
Classification Model ◽  
Core Analysis ◽  
Development Zone ◽  
The Real ◽  
Facies Classification ◽  
Quantitative Indicators

Aiming at the petrophysical facies recognition, a novel identification method based on the weighted fuzzy reasoning networks is proposed in the paper. First, the types and indicators are obtained from core analysis data and the results given by experts, and then the standard patterning database of reservoir petrophysical facies is established. Secondly, by integrating expert experiences and quantitative indicators to reflect the change of petrophysical facies, the classification model of petrophysical facies based on the weighted fuzzy reasoning networks is designed. The preferable application results are presented by processing the real data from the Sabei development zone of Daqing oilfield.

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