scholarly journals Complexity of COVID-19 Dynamics

Entropy ◽  
2021 ◽  
Vol 24 (1) ◽  
pp. 50
Author(s):  
Bellie Sivakumar ◽  
Bhadran Deepthi
Keyword(s):  
Phase Space ◽  
Infectious Diseases ◽  
Nearest Neighbor ◽  
Respiratory Illness ◽  
Temporal Dynamic ◽  
Dynamic Complexity ◽  
Dimensional Phase Space ◽  
The World ◽  
Medium Level ◽  
Reconstructed Phase Space

With population explosion and globalization, the spread of infectious diseases has been a major concern. In 2019, a newly identified type of Coronavirus caused an outbreak of respiratory illness, popularly known as COVID-19, and became a pandemic. Although enormous efforts have been made to understand the spread of COVID-19, our knowledge of the COVID-19 dynamics still remains limited. The present study employs the concepts of chaos theory to examine the temporal dynamic complexity of COVID-19 around the world. The false nearest neighbor (FNN) method is applied to determine the dimensionality and, hence, the complexity of the COVID-19 dynamics. The methodology involves: (1) reconstruction of a single-variable COVID-19 time series in a multi-dimensional phase space to represent the underlying dynamics; and (2) identification of “false” neighbors in the reconstructed phase space and estimation of the dimension of the COVID-19 series. For implementation, COVID-19 data from 40 countries/regions around the world are studied. Two types of COVID-19 data are analyzed: (1) daily COVID-19 cases; and (2) daily COVID-19 deaths. The results for the 40 countries/regions indicate that: (1) the dynamics of COVID-19 cases exhibit low- to medium-level complexity, with dimensionality in the range 3 to 7; and (2) the dynamics of COVID-19 deaths exhibit complexity anywhere from low to high, with dimensionality ranging from 3 to 13. The results also suggest that the complexity of the dynamics of COVID-19 deaths is greater than or at least equal to that of the dynamics of COVID-19 cases for most (three-fourths) of the countries/regions. These results have important implications for modeling and predicting the spread of COVID-19 (and other infectious diseases), especially in the identification of the appropriate complexity of models.

scholarly journals Global Outbreaks of SARS-nCoV-2 (COVID-19 Pandemic) and Spread out in Bangladesh

2020 ◽  
Vol 7 (1) ◽  
pp. 38-41
Author(s):  
Md Belal Hossain
Keyword(s):  
Infectious Diseases ◽  
Respiratory Illness ◽  
Viral Pneumonia ◽  
Fatality Rate ◽  
The World ◽  
Death Cases

Currently, the world is concerned about the 2019 novel CoV (SARS-nCoV-2), the disease it causes has been named “coronavirus disease 2019” (COVID-19) that was initially identified in Wuhan, China on 31 December 2019. Infected patients presented with severe viral pneumonia and respiratory illness. The new SARS-CoV-2 is RNA genomes and a beta-coronavirus, like SARS-CoV and MERS-CoV. In this article, provide a brief insights into past and present outbreaks of COVID-19. At end of the April 2020, COVID-19 Pandemic spread out all over the world in 210 countries/areas and the number of confirmed cases has been mounting globally. Reported in USA alone, over one million people are infected which is one-third of world confirmed cases and deaths cases also near to one-fourth of the total estimated deaths cases so far recorded globally. In other countries of the world situation is almost same but in Europe COVID-19 positive cases so high including death cases. In Bangladesh, the number of confirmed cases and fatality rate is lower than other reported countries in the world, due to deficient testing facilities and inadequate number of samples are tested, the virus seems to be highly contagious in Bangladesh as well. Although the fatality rate of SARS-nCoV-2 is currently lower than SARS-CoV and MERS-CoV, but the virus seems to be highly contagious based on the number of infected cases to date. Bangladesh Journal of Infectious Diseases 2020;7(1):38-41

scholarly journals Local Functional Coefficient Autoregressive Model for Multistep Prediction of Chaotic Time Series

2015 ◽  
Vol 2015 ◽  
pp. 1-13
Author(s):  
Liyun Su ◽  
Chenlong Li
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Chaos Theory ◽  
Nearest Neighbor ◽  
Simulated Data ◽  
Real Data ◽  
Chaotic Time Series ◽  
Dimensional Phase Space ◽  
Local Functional ◽  
Functional Coefficient

A new methodology, which combines nonparametric method based on local functional coefficient autoregressive (LFAR) form with chaos theory and regional method, is proposed for multistep prediction of chaotic time series. The objective of this research study is to improve the performance of long-term forecasting of chaotic time series. To obtain the prediction values of chaotic time series, three steps are involved. Firstly, the original time series is reconstructed inm-dimensional phase space with a time delayτby using chaos theory. Secondly, select the nearest neighbor points by using local method in them-dimensional phase space. Thirdly, we use the nearest neighbor points to get a LFAR model. The proposed model’s parameters are selected by modified generalized cross validation (GCV) criterion. Both simulated data (Lorenz and Mackey-Glass systems) and real data (Sunspot time series) are used to illustrate the performance of the proposed methodology. By detailed investigation and comparing our results with published researches, we find that the LFAR model can effectively fit nonlinear characteristics of chaotic time series by using simple structure and has excellent performance for multistep forecasting.

scholarly journals Streamflow disaggregation: a nonlinear deterministic approach

2004 ◽  
Vol 11 (3) ◽  
pp. 383-392 ◽  
Author(s):  
B. Sivakumar ◽  
W. W. Wallender ◽  
C. E. Puente ◽  
M. N. Islam
Keyword(s):  
Stochastic Process ◽  
Phase Space ◽  
Mississippi River ◽  
Nearest Neighbor ◽  
Local Approximation ◽  
Transformation Process ◽  
Deterministic Approach ◽  
Deterministic Process ◽  
Dimensional Phase Space ◽  
Insight Into

Abstract. This study introduces a nonlinear deterministic approach for streamflow disaggregation. According to this approach, the streamflow transformation process from one scale to another is treated as a nonlinear deterministic process, rather than a stochastic process as generally assumed. The approach follows two important steps: (1) reconstruction of the scalar (streamflow) series in a multi-dimensional phase-space for representing the transformation dynamics; and (2) use of a local approximation (nearest neighbor) method for disaggregation. The approach is employed for streamflow disaggregation in the Mississippi River basin, USA. Data of successively doubled resolutions between daily and 16 days (i.e. daily, 2-day, 4-day, 8-day, and 16-day) are studied, and disaggregations are attempted only between successive resolutions (i.e. 2-day to daily, 4-day to 2-day, 8-day to 4-day, and 16-day to 8-day). Comparisons between the disaggregated values and the actual values reveal excellent agreements for all the cases studied, indicating the suitability of the approach for streamflow disaggregation. A further insight into the results reveals that the best results are, in general, achieved for low embedding dimensions (2 or 3) and small number of neighbors (less than 50), suggesting possible presence of nonlinear determinism in the underlying transformation process. A decrease in accuracy with increasing disaggregation scale is also observed, a possible implication of the existence of a scaling regime in streamflow.

scholarly journals Three-Mode Biomedical Signal Denoising in the Local Phase Space based on a Tensor Approach

1970 ◽  
Vol 109 (3) ◽  
pp. 49-52 ◽  
Author(s):  
K. Pukenas
Keyword(s):  
Phase Space ◽  
Additive White Gaussian Noise ◽  
Simulated Data ◽  
Signal Denoising ◽  
Suggested Model ◽  
Biomedical Signal ◽  
One Dimensional ◽  
Dimensional Phase Space ◽  
Reconstructed Phase Space ◽  
Value Decomposition

In this research, an improved local projection noise reduction approach with three-mode model of neighborhood is proposed. Firstly, one dimensional time series are embedded into a high dimensional phase space. Secondly, the neighborhood tensor of each reference no overlapping window with several consecutive vectors of reconstructed phase-space is computed rather than neighborhood matrix of each separate vector. Lastly, with the suggested model a higher order singular value decomposition (HOSVD) is performed on the neighborhood tensors to split the three mode data into two orthogonal subspaces: the signal and noise subspaces. Throughout the experiment, the effectiveness of the proposed method is validated with a noisy simulated data — the x component of Rossler system and real biomedical signal contaminated with additive white Gaussian noise. Ill. 3, bibl. 14 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.109.3.169

A one-dimensional self-gravitating stellar gas

1966 ◽  
Vol 25 ◽  
pp. 46-48 ◽  
Author(s):  
M. Lecar
Keyword(s):  
Gravitational Field ◽  
Phase Space ◽  
Motion Picture ◽  
Space Distribution ◽  
Two Dimensional ◽  
One Dimensional ◽  
Phase Space Distribution ◽  
Dimensional Phase Space ◽  
The Mean

“Dynamical mixing”, i.e. relaxation of a stellar phase space distribution through interaction with the mean gravitational field, is numerically investigated for a one-dimensional self-gravitating stellar gas. Qualitative results are presented in the form of a motion picture of the flow of phase points (representing homogeneous slabs of stars) in two-dimensional phase space.

Organization of restrictive quarantine measures to prevent especially dangerous infections

2020 ◽  
pp. 6-11
Author(s):  
Petr Ilyin
Keyword(s):  
Infectious Diseases ◽  
Clinical Symptoms ◽  
International Health ◽  
World Health ◽  
The World ◽  
History Of ◽  
Entire History ◽  
Health Organization ◽  
Former Ussr ◽  
Health Regulations

Especially dangerous infections (EDIs) belong to the conditionally labelled group of infectious diseases that pose an exceptional epidemic threat. They are highly contagious, rapidly spreading and capable of affecting wide sections of the population in the shortest possible time, they are characterized by the severity of clinical symptoms and high mortality rates. At the present stage, the term "especially dangerous infections" is used only in the territory of the countries of the former USSR, all over the world this concept is defined as "infectious diseases that pose an extreme threat to public health on an international scale." Over the entire history of human development, more people have died as a result of epidemics and pandemics than in all wars combined. The list of especially dangerous infections and measures to prevent their spread were fixed in the International Health Regulations (IHR), adopted at the 22nd session of the WHO's World Health Assembly on July 26, 1969. In 1970, at the 23rd session of the WHO's Assembly, typhus and relapsing fever were excluded from the list of quarantine infections. As amended in 1981, the list included only three diseases represented by plague, cholera and anthrax. However, now annual additions of new infections endemic to different parts of the earth to this list take place. To date, the World Health Organization (WHO) has already included more than 100 diseases in the list of especially dangerous infections.

1+1 > 2: Integration of the Host-directed Traditional Chinese Medicine and the Virus-targeted Modern Medicine in Control of COVID-19

2020 ◽  
Vol 7 (2) ◽  
pp. 89-94
Author(s):  
Jianjun Sun
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Infectious Diseases ◽  
Modern Medicine ◽  
The World ◽  
Plausible Mechanism

The COVID-19 pandemic has caused millions of infections and hundreds of thousands deaths in the world. The pandemic is still ongoing and no specific antivirals have been found to control COVID-19. The integration of Traditional Chinese Medicine with supportive measures of Modern Medicine has reportedly played an important role in the control of COVID-19 in China. This review summarizes the evidence of TCM in the treatment of COVID-19 and discusses the plausible mechanism of TCM in control of COVID-19 and other viral infectious diseases.

scholarly journals Geometric particle-in-cell methods for the Vlasov–Maxwell equations with spin effects

2021 ◽  
Vol 87 (3) ◽  
Author(s):  
Nicolas Crouseilles ◽  
Paul-Antoine Hervieux ◽  
Yingzhe Li ◽  
Giovanni Manfredi ◽  
Yajuan Sun
Keyword(s):  
Phase Space ◽  
Maxwell Equations ◽  
Stimulated Raman Scattering ◽  
Extended Phase ◽  
Five Dimensions ◽  
Particle In Cell ◽  
Spin Effects ◽  
Dimensional Phase Space ◽  
Spin Polarized ◽  
Underdense Plasma

We propose a numerical scheme to solve the semiclassical Vlasov–Maxwell equations for electrons with spin. The electron gas is described by a distribution function $f(t,{\boldsymbol x},{{{\boldsymbol p}}}, {\boldsymbol s})$ that evolves in an extended 9-dimensional phase space $({\boldsymbol x},{{{\boldsymbol p}}}, {\boldsymbol s})$ , where $\boldsymbol s$ represents the spin vector. Using suitable approximations and symmetries, the extended phase space can be reduced to five dimensions: $(x,{{p_x}}, {\boldsymbol s})$ . It can be shown that the spin Vlasov–Maxwell equations enjoy a Hamiltonian structure that motivates the use of the recently developed geometric particle-in-cell (PIC) methods. Here, the geometric PIC approach is generalized to the case of electrons with spin. Total energy conservation is very well satisfied, with a relative error below $0.05\,\%$ . As a relevant example, we study the stimulated Raman scattering of an electromagnetic wave interacting with an underdense plasma, where the electrons are partially or fully spin polarized. It is shown that the Raman instability is very effective in destroying the electron polarization.

scholarly journals Computational Intelligence-Based Model for Mortality Rate Prediction in COVID-19 Patients

2021 ◽  
Vol 18 (12) ◽  
pp. 6429
Author(s):  
Irfan Ullah Khan ◽  
Nida Aslam ◽  
Malak Aljabri ◽  
Sumayh S. Aljameel ◽  
Mariam Moataz Aly Kamaleldin ◽  
...  
Keyword(s):  
Mortality Rate ◽  
Computational Intelligence ◽  
Nearest Neighbor ◽  
Gradient Boosting ◽  
K Nearest Neighbor ◽  
Detection And Identification ◽  
Proposed Model ◽  
Extreme Gradient Boosting ◽  
The World ◽  
Detection And Diagnosis

The COVID-19 outbreak is currently one of the biggest challenges facing countries around the world. Millions of people have lost their lives due to COVID-19. Therefore, the accurate early detection and identification of severe COVID-19 cases can reduce the mortality rate and the likelihood of further complications. Machine Learning (ML) and Deep Learning (DL) models have been shown to be effective in the detection and diagnosis of several diseases, including COVID-19. This study used ML algorithms, such as Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN) and DL model (containing six layers with ReLU and output layer with sigmoid activation), to predict the mortality rate in COVID-19 cases. Models were trained using confirmed COVID-19 patients from 146 countries. Comparative analysis was performed among ML and DL models using a reduced feature set. The best results were achieved using the proposed DL model, with an accuracy of 0.97. Experimental results reveal the significance of the proposed model over the baseline study in the literature with the reduced feature set.

Clinical Management Updates for Novel Corona Virus (COVID-19)

2021 ◽  
Vol 14 (3) ◽  
pp. 5449-5456
Author(s):  
Yogesh Chand Yadav ◽  
Ramakant Yadav ◽  
Sushant Kumar
Keyword(s):  
Clinical Management ◽  
Respiratory Illness ◽  
Symptomatic Treatment ◽  
Structural Features ◽  
Therapeutic Agents ◽  
Human Coronavirus ◽  
Transmission Potential ◽  
The World ◽  
Severe Respiratory Illness ◽  
Droplet Transmission

The SARS-CoV-2 virus was first detected in Wuhan, China in December 2019 and was known to produce acute severe respiratory illness in humans which rapidly spread almost throughout the world within a few months. This human coronavirus has seven strains and they commonly produce illness in the nervous system, respiratory system and hepato- intestinal systems. This present review is an attempt to illustrate recent reports pertaining to the management of SARS-CoV-2. Further, it also highlights the diagnosis and clinical management of COVID-19. Various search engines like Scopus, Pubmed and WHO databases were accessed and literature on current advances about COVID-19 including structural features, replication, possible pathogenic, symptoms, diagnosis, prognosis, methods of prevention and possible therapeutic agents used for treatment of patients was reviewed. Current studies indicate that COVID-19 is very infectious with droplet transmission potential. The key modalities to prevent the infection is by keeping social distancing, respiratory/hand hygiene, detection of infection and subsequent quarantine of the infected persons. Presently, either no vaccine for prevention or specific treatments available, however, COVID-19 patients may be managed by using some repositioned drugs and symptomatic treatment.

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