scholarly journals Estimation of COVID-19 recovery and decease periods in Canada using delay model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Subhendu Paul ◽  
Emmanuel Lorin
Keyword(s):  
Large Scale ◽  
Recovery Period ◽  
Bivariate Distribution ◽  
Bivariate Analysis ◽  
Delay Model ◽  
Death Toll ◽  
Incubation Periods ◽  
Delay Differential ◽  
Novel Model ◽  
Similar Domain

AbstractWe derive a novel model escorted by large scale compartments, based on a set of coupled delay differential equations with extensive delays, in order to estimate the incubation, recovery and decease periods of COVID-19, and more generally any infectious disease. This is possible thanks to some optimization algorithms applied to publicly available database of confirmed corona cases, recovered cases and death toll. In this purpose, we separate (1) the total cases into 14 groups corresponding to 14 incubation periods, (2) the recovered cases into 406 groups corresponding to a combination of incubation and recovery periods, and (3) the death toll into 406 groups corresponding to a combination of incubation and decease periods. In this paper, we focus on recovery and decease periods and their correlation with the incubation period. The estimated mean recovery period we obtain is 22.14 days (95% Confidence Interval (CI) 22.00–22.27), and the 90th percentile is 28.91 days (95% CI 28.71–29.13), which is in agreement with statistical supported studies. The bimodal gamma distribution reveals that there are two groups of recovered individuals with a short recovery period, mean 21.02 days (95% CI 20.92–21.12), and a long recovery period, mean 38.88 days (95% CI 38.61–39.15). Our study shows that the characteristic of the decease period and the recovery period are alike. From the bivariate analysis, we observe a high probability domain for recovered individuals with respect to incubation and recovery periods. A similar domain is obtained for deaths analyzing bivariate distribution of incubation and decease periods.

scholarly journals Estimation of COVID-19 Recovery and Decease Periods in Canada Using Machine Learning Algorithms

2021 ◽  
Author(s):  
Subhendu Paul ◽  
Emmanuel Lorin
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Learning Algorithms ◽  
Recovery Period ◽  
Bivariate Distribution ◽  
Machine Learning Algorithms ◽  
Bivariate Analysis ◽  
Death Toll ◽  
Delay Differential ◽  
Novel Model

Abstract We derive a novel model escorted by large scale compartments, based on a set of coupled delay differential equations with extensive delays, in order to estimate the incubation, recovery and decease periods of COVID-19, and more generally any infectious disease. This is possible thanks to machine learning algorithms applied to publicly available database of confirmed corona cases, recovered cases and death toll. In this purpose, we separate i) the total cases into 14 groups corresponding to 14 incubation periods, ii) the recovered cases into 406 groups corresponding to a combination of incubation and recovery periods, and iii) the death toll into 406 groups corresponding to a combination of incubation and decease periods. In this paper, we focus on recovery and decease periods and their correlation with the incubation period. The estimated mean recovery period we obtain is 22.14 days (95% Confidence Interval(CI): 22.00 to 22.27), and the 90th percentile is 28.91 days (95% CI: 28.71 to 29.13), which is in agreement with statistical supported studies. The bimodal gamma distribution reveals that there are two groups of recovered individuals with a short recovery period, mean 21.02 days (95% CI: 20.92 to 21.12), and a long recovery period, mean 38.88 days (95% CI 38.61 to 39.15). Our study shows that the characteristic of the decease period and the recovery period are alike. From the bivariate analysis, we observe a high probability domain for recovered individuals with respect to incubation and recovery periods. A similar domain is obtained for deaths analyzing bivariate distribution of incubation and decease periods.

scholarly journals Estimation of COVID-19 recovery and decease periods in Canada using machine learning algorithms

2021 ◽  
Author(s):  
Subhendu Paul ◽  
Emmanuel Lorin
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Learning Algorithms ◽  
Recovery Period ◽  
Bivariate Distribution ◽  
Machine Learning Algorithms ◽  
Bivariate Analysis ◽  
Death Toll ◽  
Delay Differential ◽  
Novel Model

We derive a novel model escorted by large scale compartments, based on a set of coupled delay differential equations with extensive delays, in order to estimate the incubation, recovery and decease periods of COVID-19, and more generally any infectious disease. This is possible thanks to machine learning algorithms applied to publicly available database of confirmed corona cases, recovered cases and death toll. In this purpose, we separate i) the total cases into 14 groups corresponding to 14 incubation periods, ii) the recovered cases into 406 groups corresponding to a combination of incubation and recovery periods, and iii) the death toll into 406 groups corresponding to a combination of incubation and decease periods. In this paper, we focus on recovery and decease periods and their correlation with the incubation period. The estimated mean recovery period we obtain is 22.14 days (95\% Confidence Interval(CI): 22.00 to 22.27), and the 90th percentile is 28.91 days (95\% CI: 28.71 to 29.13), which is in agreement with statistical supported studies. The bimodal gamma distribution reveals that there are two groups of recovered individuals with a short recovery period, mean 21.02 days (95\% CI: 20.92 to 21.12), and a long recovery period, mean 38.88 days (95\% CI 38.61 to 39.15). Our study shows that the characteristic of the decease period and the recovery period are alike. From the bivariate analysis, we observe a high probability domain for recovered individuals with respect to incubation and recovery periods. A similar domain is obtained for deaths analyzing bivariate distribution of incubation and decease periods.

scholarly journals Distribution of Incubation Period of COVID-19 in the Canadian Context: Modeling and Computational Study

2020 ◽  
Author(s):  
Subhendu Paul ◽  
Emmanuel Lorin
Keyword(s):  
Confidence Interval ◽  
Incubation Period ◽  
Delay Differential Equations ◽  
Computational Study ◽  
Context Modeling ◽  
Canadian Context ◽  
Incubation Periods ◽  
Delay Differential ◽  
Novel Model ◽  
Good Agreement

Abstract We propose a novel model based on a set of coupled delay differential equations with fourteen delays in order to accurately estimate the incubation period of COVID-19, employing publicly available data of confirmed corona cases. In this goal, we separate the total cases into fourteen groups for the corresponding fourteen incubation periods. The estimated mean incubation period we obtain is 6.74 days (95% Confidence Interval(CI): 6.35 to 7.13), and the 90th percentile is 11.64 days (95% CI: 11.22 to 12.17), corresponding to a good agreement with statistical supported studies. This model provides an almost zero-cost approach to estimate the incubation period.

scholarly journals Distribution of incubation periods of COVID-19 in the Canadian context

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Subhendu Paul ◽  
Emmanuel Lorin
Keyword(s):  
Computational Complexity ◽  
Confidence Interval ◽  
Differential Equations ◽  
Incubation Period ◽  
Delay Differential Equations ◽  
Canadian Context ◽  
Incubation Periods ◽  
Delay Differential ◽  
Novel Model ◽  
Good Agreement

AbstractWe propose a novel model based on a set of coupled delay differential equations with fourteen delays in order to accurately estimate the incubation period of COVID-19, employing publicly available data of confirmed corona cases. In this goal, we separate the total cases into fourteen groups for the corresponding fourteen incubation periods. The estimated mean incubation period we obtain is 6.74 days (95% Confidence Interval(CI): 6.35 to 7.13), and the 90th percentile is 11.64 days (95% CI: 11.22 to 12.17), corresponding to a good agreement with statistical supported studies. This model provides an almost zero-cost computational complexity to estimate the incubation period.

A multi-objective sparse evolutionary framework for large-scale weapon target assignment based on a reward strategy

2021 ◽  
Vol 40 (5) ◽  
pp. 10043-10061
Author(s):  
Xiaoping Shi ◽  
Shiqi Zou ◽  
Shenmin Song ◽  
Rui Guo
Keyword(s):  
Large Scale ◽  
The Novel ◽  
Genetic Operators ◽  
Multi Objective ◽  
Target Assignment ◽  
Solution Selection ◽  
Battlefield Environment ◽  
Sparse Problems ◽  
Novel Model ◽  
Error Parameter

 The asset-based weapon target assignment (ABWTA) problem is one of the important branches of the weapon target assignment (WTA) problem. Due to the current large-scale battlefield environment, the ABWTA problem is a multi-objective optimization problem (MOP) with strong constraints, large-scale and sparse properties. The novel model of the ABWTA problem with the operation error parameter is established. An evolutionary algorithm for large-scale sparse problems (SparseEA) is introduced as the main framework for solving large-scale sparse ABWTA problem. The proposed framework (SparseEA-ABWTA) mainly addresses the issue that problem-specific initialization method and genetic operators with a reward strategy can generate solutions efficiently considering the sparsity of variables and an improved non-dominated solution selection method is presented to handle the constraints. Under the premise of constructing large-scale cases by the specific case generator, two numerical experiments on four outstanding multi-objective evolutionary algorithms (MOEAs) show Runtime of SparseEA-ABWTA is faster nearly 50% than others under the same convergence and the gap between MOEAs improved by the mechanism of SparseEA-ABWTA and SparseEA-ABWTA is reduced to nearly 20% in the convergence and distribution.

Neural Network for Big Data Sets

2022 ◽  
pp. 41-67
Author(s):  
Vo Ngoc Phu ◽  
Vo Thi Ngoc Tran
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Computer Science ◽  
Large Scale ◽  
Massive Data ◽  
Data Sets ◽  
Massive Data Sets ◽  
Large Scale Data ◽  
Commercial Applications ◽  
Novel Model

Machine learning (ML), neural network (NN), evolutionary algorithm (EA), fuzzy systems (FSs), as well as computer science have been very famous and very significant for many years. They have been applied to many different areas. They have contributed much to developments of many large-scale corporations, massive organizations, etc. Lots of information and massive data sets (MDSs) have been generated from these big corporations, organizations, etc. These big data sets (BDSs) have been the challenges of many commercial applications, researches, etc. Therefore, there have been many algorithms of the ML, the NN, the EA, the FSs, as well as computer science which have been developed to handle these massive data sets successfully. To support for this process, the authors have displayed all the possible algorithms of the NN for the large-scale data sets (LSDSs) successfully in this chapter. Finally, they have presented a novel model of the NN for the BDS in a sequential environment (SE) and a distributed network environment (DNE).

scholarly journals Improving POI Recommendation via Dynamic Tensor Completion

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Jinzhi Liao ◽  
Jiuyang Tang ◽  
Xiang Zhao ◽  
Haichuan Shang
Keyword(s):  
Large Scale ◽  
Real Life ◽  
Location Based Services ◽  
Low Rank ◽  
Tensor Structure ◽  
Tensor Completion ◽  
Tensor Method ◽  
Rank Tensor ◽  
Poi Recommendation ◽  
Novel Model

POI recommendation finds significant importance in various real-life applications, especially when meeting with location-based services, e.g., check-ins social networks. In this paper, we propose to solve POI recommendation through a novel model of dynamic tensor, which is among the first triumphs of its kind. In order to carry out timely recommendation, we predict POI by utilizing a completion algorithm based on fast low-rank tensor. Particularly, the dynamic tensor structure is complemented by the fast low-rank tensor completion algorithm so as to achieve prediction with better performance, where the parameter optimization is achieved by a pigeon-inspired heuristic algorithm. In short, our POI recommendation via the dynamic tensor method can take advantage of the intrinsic characteristics of check-ins data due to the multimode features such as current categories, subsequent categories, and temporal information as well as seasons variations are all integrated into the model. Extensive experiment results not only validate the superiority of our proposed method but also imply the application prospect in large-scale and real-time POI recommendation environment.

scholarly journals Graph CNNs with Motif and Variable Temporal Block for Skeleton-Based Action Recognition

2019 ◽  
Vol 33 ◽  
pp. 8989-8996 ◽  
Author(s):  
Yu-Hui Wen ◽  
Lin Gao ◽  
Hongbo Fu ◽  
Fang-Lue Zhang ◽  
Shihong Xia
Keyword(s):  
Spatial Structure ◽  
Action Recognition ◽  
Large Scale ◽  
Human Skeleton ◽  
Semantic Roles ◽  
Temporal Domain ◽  
Non Local ◽  
Novel Model ◽  
Local Block ◽  
Skeleton Structure

Hierarchical structure and different semantic roles of joints in human skeleton convey important information for action recognition. Conventional graph convolution methods for modeling skeleton structure consider only physically connected neighbors of each joint, and the joints of the same type, thus failing to capture highorder information. In this work, we propose a novel model with motif-based graph convolution to encode hierarchical spatial structure, and a variable temporal dense block to exploit local temporal information over different ranges of human skeleton sequences. Moreover, we employ a non-local block to capture global dependencies of temporal domain in an attention mechanism. Our model achieves improvements over the stateof-the-art methods on two large-scale datasets.

scholarly journals Numerical Simulations of a Delay Model for Immune System-Tumor Interaction

2018 ◽  
Vol 23 (1) ◽  
pp. 19 ◽  
Author(s):  
Mohamed A. Hajji ◽  
Qasem Al-Mdallal
Keyword(s):  
Steady State ◽  
Immune System ◽  
Numerical Simulations ◽  
Relative Rate ◽  
Delay Model ◽  
Effector Cells ◽  
Rate Of Increase ◽  
System Tumor ◽  
Delay Differential ◽  
Parameter Values

In this paper we consider a system of delay differential equations as a model for the dynamics of tumor-immune system interaction. We carry out a stability analysis of the proposed model. In particular, we show that the system can have up to two steady states: the tumor free steady state, which always exist, and the tumor persistent steady state, which exists only when the relative rate of increase of the tumor cells exceeds the ratio between the natural proliferation rate and the relative death rate of the effector cells. We also determine an upper bound for the delay, such that stability is preserved. Numerical simulations of the system under different parameter values are performed.

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