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1.
Clin Transl Sci ; 14(6): 2348-2359, 2021 11.
Article in English | MEDLINE | ID: covidwho-1526356

ABSTRACT

Coronavirus disease 2019 (COVID-19) global pandemic is caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) viral infection, which can lead to pneumonia, lung injury, and death in susceptible populations. Understanding viral dynamics of SARS-CoV-2 is critical for development of effective treatments. An Immune-Viral Dynamics Model (IVDM) is developed to describe SARS-CoV-2 viral dynamics and COVID-19 disease progression. A dataset of 60 individual patients with COVID-19 with clinical viral load (VL) and reported disease severity were assembled from literature. Viral infection and replication mechanisms of SARS-CoV-2, viral-induced cell death, and time-dependent immune response are incorporated in the model to describe the dynamics of viruses and immune response. Disease severity are tested as a covariate to model parameters. The IVDM was fitted to the data and parameters were estimated using the nonlinear mixed-effect model. The model can adequately describe individual viral dynamics profiles, with disease severity identified as a covariate on infected cell death rate. The modeling suggested that it takes about 32.6 days to reach 50% of maximum cell-based immunity. Simulations based on virtual populations suggested a typical mild case reaches VL limit of detection (LOD) by 13 days with no treatment, a moderate case by 17 days, and a severe case by 41 days. Simulations were used to explore hypothetical treatments with different initiation time, disease severity, and drug effects to demonstrate the usefulness of such modeling in informing decisions. Overall, the IVDM modeling and simulation platform enables simulations for viral dynamics and treatment efficacy and can be used to aid in clinical pharmacokinetic/pharmacodynamic (PK/PD) and dose-efficacy response analysis for COVID-19 drug development.


Subject(s)
Antiviral Agents/pharmacology , COVID-19/drug therapy , Drug Development/methods , Host Microbial Interactions/immunology , Models, Biological , Antiviral Agents/therapeutic use , COVID-19/diagnosis , COVID-19/immunology , COVID-19/virology , Cell Death/drug effects , Cell Death/immunology , Datasets as Topic , Dose-Response Relationship, Drug , Host Microbial Interactions/drug effects , Humans , Nonlinear Dynamics , SARS-CoV-2/drug effects , SARS-CoV-2/immunology , Severity of Illness Index , Treatment Outcome , Viral Load
2.
Sci Rep ; 11(1): 20982, 2021 10 25.
Article in English | MEDLINE | ID: covidwho-1483150

ABSTRACT

Intermittent and periodic outbreaks of infectious diseases have had profound and lasting effects on societies throughout human history. During the global spread of SARS-CoV-2 and the resulting coronavirus disease (COVID-19), social distance has been imposed worldwide to limit the spread of the virus. An additional deliberate intention of keeping a minimum safety distance from neighbors can fundamentally alter the "social force" between individuals. Here, we introduce a new "social distance" term inspired by gas molecular dynamics and integrate it into an existing agent-based social force model to describe the dynamics of crowds under social-distanced conditions. The advantage of this "social distance" term over the simple increasing of the repulsive range of other alternatives is that the fundamental crowd properties are precisely described by our model parameters. We compare the new model with the Helbing and Molnar's classical model and experimental data, and show that this new model is superior in reproducing experimental data. We demonstrate the usability of this model with a bottleneck motion base case. The new model shows that the bottleneck effect can be significantly alleviated through small wall modifications. Lastly, we explain the mechanism of this improvement and conclude that this improvement is due to spatial asymmetry.


Subject(s)
COVID-19/prevention & control , Communicable Disease Control/methods , Physical Distancing , Algorithms , COVID-19/transmission , Crowding , Disease Outbreaks , Humans , Models, Statistical , Models, Theoretical , Molecular Dynamics Simulation , Nonlinear Dynamics , Pandemics , Public Health Informatics , SARS-CoV-2
3.
Sci Rep ; 11(1): 20124, 2021 10 11.
Article in English | MEDLINE | ID: covidwho-1462024

ABSTRACT

The Novel Coronavirus which emerged in India on January/30/2020 has become a catastrophe to the country on the basis of health and economy. Due to rapid variations in the transmission of COVID-19, an accurate prediction to determine the long term effects is infeasible. This paper has introduced a nonlinear mathematical model to interpret the transmission dynamics of COVID-19 infection along with providing vaccination in the precedence. To minimize the level of infection and treatment burden, the optimal control strategies are carried out by using the Pontryagin's Maximum Principle. The data validation has been done by correlating the estimated number of infectives with the real data of India for the month of March/2021. Corresponding to the model, the basic reproduction number [Formula: see text] is introduced to understand the transmission dynamics of COVID-19. To justify the significance of parameters we determined the sensitivity analysis of [Formula: see text] using the parameters value. In the numerical simulations, we concluded that reducing [Formula: see text] below unity is not sufficient enough to eradicate the COVID-19 disease and thus, it is required to increase the vaccination rate and its efficacy by motivating individuals to take precautionary measures.


Subject(s)
COVID-19 Vaccines/administration & dosage , COVID-19/epidemiology , Communicable Disease Control/organization & administration , Models, Biological , Pandemics/prevention & control , Basic Reproduction Number , COVID-19/prevention & control , COVID-19/transmission , COVID-19/virology , Communicable Disease Control/standards , Computer Simulation , Humans , India/epidemiology , Nonlinear Dynamics , Pandemics/statistics & numerical data , SARS-CoV-2/pathogenicity , Vaccination/statistics & numerical data
4.
IEEE Trans Neural Netw Learn Syst ; 32(10): 4278-4290, 2021 10.
Article in English | MEDLINE | ID: covidwho-1455467

ABSTRACT

This article devises a photograph-based monitoring model to estimate the real-time PM2.5 concentrations, overcoming currently popular electrochemical sensor-based PM2.5 monitoring methods' shortcomings such as low-density spatial distribution and time delay. Combining the proposed monitoring model, the photographs taken by various camera devices (e.g., surveillance camera, automobile data recorder, and mobile phone) can widely monitor PM2.5 concentration in megacities. This is beneficial to offering helpful decision-making information for atmospheric forecast and control, thus reducing the epidemic of COVID-19. To specify, the proposed model fuses Information Abundance measurement and Wide and Deep learning, dubbed as IAWD, for PM2.5 monitoring. First, our model extracts two categories of features in a newly proposed DS transform space to measure the information abundance (IA) of a given photograph since the growth of PM2.5 concentration decreases its IA. Second, to simultaneously possess the advantages of memorization and generalization, a new wide and deep neural network is devised to learn a nonlinear mapping between the above-mentioned extracted features and the groundtruth PM2.5 concentration. Experiments on two recently established datasets totally including more than 100 000 photographs demonstrate the effectiveness of our extracted features and the superiority of our proposed IAWD model as compared to state-of-the-art relevant computing techniques.


Subject(s)
Deep Learning , Environmental Monitoring/methods , Particle Size , Algorithms , COVID-19/prevention & control , Databases, Factual , Humans , Nonlinear Dynamics , Particulate Matter , Photography , SARS-CoV-2
5.
Comput Math Methods Med ; 2021: 1250129, 2021.
Article in English | MEDLINE | ID: covidwho-1398741

ABSTRACT

We formulate and theoretically analyze a mathematical model of COVID-19 transmission mechanism incorporating vital dynamics of the disease and two key therapeutic measures-vaccination of susceptible individuals and recovery/treatment of infected individuals. Both the disease-free and endemic equilibrium are globally asymptotically stable when the effective reproduction number R 0(v) is, respectively, less or greater than unity. The derived critical vaccination threshold is dependent on the vaccine efficacy for disease eradication whenever R 0(v) > 1, even if vaccine coverage is high. Pontryagin's maximum principle is applied to establish the existence of the optimal control problem and to derive the necessary conditions to optimally mitigate the spread of the disease. The model is fitted with cumulative daily Senegal data, with a basic reproduction number R 0 = 1.31 at the onset of the epidemic. Simulation results suggest that despite the effectiveness of COVID-19 vaccination and treatment to mitigate the spread of COVID-19, when R 0(v) > 1, additional efforts such as nonpharmaceutical public health interventions should continue to be implemented. Using partial rank correlation coefficients and Latin hypercube sampling, sensitivity analysis is carried out to determine the relative importance of model parameters to disease transmission. Results shown graphically could help to inform the process of prioritizing public health intervention measures to be implemented and which model parameter to focus on in order to mitigate the spread of the disease. The effective contact rate b, the vaccine efficacy ε, the vaccination rate v, the fraction of exposed individuals who develop symptoms, and, respectively, the exit rates from the exposed and the asymptomatic classes σ and ϕ are the most impactful parameters.


Subject(s)
COVID-19/prevention & control , COVID-19/transmission , Models, Biological , Basic Reproduction Number/statistics & numerical data , COVID-19/therapy , COVID-19 Vaccines/pharmacology , Computer Simulation , Humans , Mathematical Concepts , Nonlinear Dynamics , Pandemics/prevention & control , Pandemics/statistics & numerical data , Public Health , SARS-CoV-2 , Senegal/epidemiology , Vaccination
6.
Comput Math Methods Med ; 2021: 8873059, 2021.
Article in English | MEDLINE | ID: covidwho-1362017

ABSTRACT

When encountering the outbreak and early spreading of COVID-19, the Government of Japan imposed gradually upgraded restriction policies and declared the state of emergency in April 2020 for the first time. To evaluate the efficacy of the countering strategies in different periods, we constructed a SEIADR (susceptible-exposed-infected-asymptomatic-documented-recovered) model to simulate the cases and determined corresponding spreading coefficients. The effective reproduction number R t was obtained to evaluate the measures controlling the COVID-19 conducted by the Government of Japan during different stages. It was found that the strict containing strategies during the state of emergency period drastically inhibit the COVID-19 trend. R t was decreased to 1.1123 and 0.8911 in stages 4 and 5 (a state of emergency in April and May 2020) from 3.5736, 2.0126, 3.0672 in the previous three stages when the containing strategies were weak. The state of emergency was declared again in view of the second wave of massive infections in January 2021. We estimated the cumulative infected cases and additional days to contain the COVID-19 transmission for the second state of emergency using this model. R t was 1.028 which illustrated that the strategies were less effective than the previous state of emergency. Finally, the overall infected population was predicted using combined isolation and testing intensity; the effectiveness and the expected peak time were evaluated. If using the optimized control strategies in the current stage, the spread of COVID-19 in Japan could be controlled within 30 days. The total confirmed cases should reduce to less than 4.2 × 105 by April 2021. This model study suggested stricter isolating measures may be required to shorten the period of the state of emergency.


Subject(s)
COVID-19/epidemiology , COVID-19/transmission , Emergencies , Models, Biological , Pandemics , SARS-CoV-2 , Algorithms , COVID-19/prevention & control , COVID-19 Testing/methods , COVID-19 Testing/statistics & numerical data , Communicable Disease Control/legislation & jurisprudence , Communicable Disease Control/methods , Communicable Disease Control/statistics & numerical data , Computational Biology , Computer Simulation , Humans , Japan/epidemiology , Least-Squares Analysis , Mathematical Concepts , Models, Statistical , National Health Programs/legislation & jurisprudence , Nonlinear Dynamics , Pandemics/prevention & control , Pandemics/statistics & numerical data
7.
IEEE/ACM Trans Comput Biol Bioinform ; 18(4): 1290-1298, 2021.
Article in English | MEDLINE | ID: covidwho-1349906

ABSTRACT

An outbreak of COVID-19 that began in late 2019 was caused by a novel coronavirus(SARS-CoV-2). It has become a global pandemic. As of June 9, 2020, it has infected nearly 7 million people and killed more than 400,000, but there is no specific drug. Therefore, there is an urgent need to find or develop more drugs to suppress the virus. Here, we propose a new nonlinear end-to-end model called LUNAR. It uses graph convolutional neural networks to automatically learn the neighborhood information of complex heterogeneous relational networks and combines the attention mechanism to reflect the importance of the sum of different types of neighborhood information to obtain the representation characteristics of each node. Finally, through the topology reconstruction process, the feature representations of drugs and targets are forcibly extracted to match the observed network as much as possible. Through this reconstruction process, we obtain the strength of the relationship between different nodes and predict drug candidates that may affect the treatment of COVID-19 based on the known targets of COVID-19. These selected candidate drugs can be used as a reference for experimental scientists and accelerate the speed of drug development. LUNAR can well integrate various topological structure information in heterogeneous networks, and skillfully combine attention mechanisms to reflect the importance of neighborhood information of different types of nodes, improving the interpretability of the model. The area under the curve(AUC) of the model is 0.949 and the accurate recall curve (AUPR) is 0.866 using 10-fold cross-validation. These two performance indexes show that the model has superior predictive performance. Besides, some of the drugs screened out by our model have appeared in some clinical studies to further illustrate the effectiveness of the model.


Subject(s)
Antiviral Agents/pharmacology , COVID-19/drug therapy , COVID-19/virology , Drug Evaluation, Preclinical/methods , Neural Networks, Computer , SARS-CoV-2/drug effects , COVID-19/epidemiology , Computational Biology , Databases, Pharmaceutical/statistics & numerical data , Drug Development/methods , Drug Development/statistics & numerical data , Drug Evaluation, Preclinical/statistics & numerical data , Drug Repositioning/methods , Drug Repositioning/statistics & numerical data , Host Microbial Interactions/drug effects , Humans , Nonlinear Dynamics , Pandemics
8.
Rev. latinoam. enferm. (Online) ; 29: e3453, 2021.
Article in English | LILACS (Americas) | ID: covidwho-1306578

ABSTRACT

Objective: to carry out a theoretical reflection on the Nursing Now Campaign and the experience of the unexpected irruptions facing the pandemic period. Method: a theoretical-reflective study, supported by the theoretical framework of complexity thinking. It aims at understanding the dialogic between the notions of order, disorder and organization, which translate the transition from simplification to complexity of the pandemic phenomenon and its relation to the theme of Nursing Now and Nursing in the future. Results: the universe of phenomena is simultaneously composed of order, disorder and organization. Reasserting the central role of Nursing in the health team, facing the irruptions and uncertainties caused by the current pandemic, implies the ability to dialog with disorder and raise a new and more complex global (re)organization of the being and doing Nursing. Conclusion: in addition to answers, theoretical reflection raises new questions and irruptions. The inseparability between the notions of order and disorder in the evolutionary dynamics of the Nursing system is conceived and the promotion of even more complex levels of organization, management and Nursing assistance to achieve universal access to health is advocated.


Objetivo: realizar reflexão teórica acerca da Campanha Nursing Now e a experiência das irrupções do inesperado face ao período pandêmico. Método: estudo teórico-reflexivo, apoiado no referencial teórico do pensamento da complexidade. Visa-se à compreensão da dialógica entre as noções de ordem, de desordem e de organização, as quais traduzem a passagem da simplificação à complexidade do fenômeno da pandemia e sua relação com a temática Nursing Now and Nursing in the future. Resultados: o universo dos fenômenos é tecido, simultaneamente, de ordem, de desordem e de organização. Reafirmar o papel central da Enfermagem na equipe de saúde, face às irrupções e incertezas provocadas pela pandemia em curso, implica capacidade de dialogar com a desordem e suscitar uma nova e mais complexa (re)organização global do ser e do fazer Enfermagem. Conclusão: a reflexão teórica suscita, além de respostas, novos questionamentos e novas irrupções. Concebe-se a inseparabilidade entre as noções de ordem e de desordem na dinâmica evolutiva do sistema de Enfermagem e defende-se a promoção de níveis de organização, gestão e assistência de Enfermagem ainda mais complexos para o alcance do acesso universal à saúde.


Objetivo: realizar una reflexión teórica sobre la Campaña Nursing Now y la experiencia de las irrupciones de lo inesperado ante el período pandémico. Método: estudio teórico-reflexivo, basado en el marco teórico del pensamiento complejo. Tiene como objetivo comprender la dialógica entre las nociones de orden, desorden y organización, que traducen la transición de la simplificación a la complejidad del fenómeno pandémico y su relación con el tema Nursing Now and Nursing in the future. Resultados: el universo de los fenómenos se entreteje, simultáneamente, de orden, desorden y organización. Reafirmar el papel central de la Enfermería en el equipo de salud, ante las irrupciones e incertidumbres provocadas por la pandemia actual, implica la capacidad de dialogar con el desorden y propiciar una nueva y más compleja (re)organización global del ser y hacer Enfermería. Conclusión: la reflexión teórica plantea, además de respuestas, nuevas interrogantes y nuevas irrupciones. Se concibe la inseparabilidad entre las nociones de orden y desorden en la dinámica evolutiva del sistema de Enfermería y se aboga por la promoción de niveles aún más complejos de organización, gestión y asistencia de Enfermería para lograr el acceso universal a la salud.


Subject(s)
Nursing , Nonlinear Dynamics , Coronavirus , Pandemics , Nursing Care
9.
Clin Transl Sci ; 14(6): 2348-2359, 2021 11.
Article in English | MEDLINE | ID: covidwho-1268104

ABSTRACT

Coronavirus disease 2019 (COVID-19) global pandemic is caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) viral infection, which can lead to pneumonia, lung injury, and death in susceptible populations. Understanding viral dynamics of SARS-CoV-2 is critical for development of effective treatments. An Immune-Viral Dynamics Model (IVDM) is developed to describe SARS-CoV-2 viral dynamics and COVID-19 disease progression. A dataset of 60 individual patients with COVID-19 with clinical viral load (VL) and reported disease severity were assembled from literature. Viral infection and replication mechanisms of SARS-CoV-2, viral-induced cell death, and time-dependent immune response are incorporated in the model to describe the dynamics of viruses and immune response. Disease severity are tested as a covariate to model parameters. The IVDM was fitted to the data and parameters were estimated using the nonlinear mixed-effect model. The model can adequately describe individual viral dynamics profiles, with disease severity identified as a covariate on infected cell death rate. The modeling suggested that it takes about 32.6 days to reach 50% of maximum cell-based immunity. Simulations based on virtual populations suggested a typical mild case reaches VL limit of detection (LOD) by 13 days with no treatment, a moderate case by 17 days, and a severe case by 41 days. Simulations were used to explore hypothetical treatments with different initiation time, disease severity, and drug effects to demonstrate the usefulness of such modeling in informing decisions. Overall, the IVDM modeling and simulation platform enables simulations for viral dynamics and treatment efficacy and can be used to aid in clinical pharmacokinetic/pharmacodynamic (PK/PD) and dose-efficacy response analysis for COVID-19 drug development.


Subject(s)
Antiviral Agents/pharmacology , COVID-19/drug therapy , Drug Development/methods , Host Microbial Interactions/immunology , Models, Biological , Antiviral Agents/therapeutic use , COVID-19/diagnosis , COVID-19/immunology , COVID-19/virology , Cell Death/drug effects , Cell Death/immunology , Datasets as Topic , Dose-Response Relationship, Drug , Host Microbial Interactions/drug effects , Humans , Nonlinear Dynamics , SARS-CoV-2/drug effects , SARS-CoV-2/immunology , Severity of Illness Index , Treatment Outcome , Viral Load
10.
Biomed Res Int ; 2021: 6645688, 2021.
Article in English | MEDLINE | ID: covidwho-1232375

ABSTRACT

As of December 2020, since the beginning of the year 2020, the COVID-19 pandemic has claimed worldwide more than 1 million lives and has changed human life in unprecedented ways. Despite the fact that the pandemic is far from over, several countries managed at least temporarily to make their first-wave COVID-19 epidemics to subside to relatively low levels. Combining an epidemiological compartment model and a stability analysis as used in nonlinear physics and synergetics, it is shown how the first-wave epidemics in the state of New York and nationwide in the USA developed through three stages during the first half of the year 2020. These three stages are the outbreak stage, the linear stage, and the subsiding stage. Evidence is given that the COVID-19 outbreaks in these two regions were due to instabilities of the COVID-19 free states of the corresponding infection dynamical systems. It is shown that from stage 1 to stage 3, these instabilities were removed, presumably due to intervention measures, in the sense that the COVID-19 free states were stabilized in the months of May and June in both regions. In this context, stability parameters and key directions are identified that characterize the infection dynamics in the outbreak and subsiding stages. Importantly, it is shown that the directions in combination with the sign-switching of the stability parameters can explain the observed rise and decay of the epidemics in the state of New York and the USA. The nonlinear physics perspective provides a framework to obtain insights into the nature of the COVID-19 dynamics during outbreak and subsiding stages and allows to discuss possible impacts of intervention measures. For example, the directions can be used to determine how different populations (e.g., exposed versus symptomatic individuals) vary in size relative to each other during the course of an epidemic. Moreover, the timeline of the computationally obtained stages can be compared with the history of the implementation of intervention measures to discuss the effectivity of such measures.


Subject(s)
COVID-19/epidemiology , Disease Outbreaks/prevention & control , COVID-19/transmission , COVID-19/virology , Humans , Models, Statistical , New York/epidemiology , Nonlinear Dynamics , Physics , SARS-CoV-2/isolation & purification , SARS-CoV-2/pathogenicity , United States/epidemiology
11.
J Infect Public Health ; 14(7): 817-831, 2021 Jul.
Article in English | MEDLINE | ID: covidwho-1193398

ABSTRACT

Coronaviruses did not invite attention at a global level and responsiveness until the series of 2003-SARS contagion followed by year-2012 MERS plus, most recently, 2019-nCoV eruptions. SARS-CoV &MERS-CoV are painstaking, extremely pathogenic. Also, very evidently, both have been communicated from bats to palm-civets & dromedary camels and further transferred ultimately to humans. No country has been deprived of this viral genomic contamination wherever populaces reside and are interconnected. This study aimed to develop a mathematical model for calculating the transmissibility of this viral genome. The analysis aids the study of the outbreak of this Virus towards the other parts of the continent and the world. The parameters such as population mobility, natural history, epidemiological characteristics, and the transmission mechanism towards viral spread when considered into crowd dynamism result in improved estimation. This article studies the impact of time on the amount of susceptible, exposed, the infected person taking into account asymptomatic and symptomatic ones; recovered i.e., removed from this model and the virus particles existing in the open surfaces. The transition from stable phase to attractor phase happens after 13 days i.e.; it takes nearly a fortnight for the spread to randomize among people. Further, the pandemic transmission remains in the attractor phase for a very long time if no control measures are taken up. The attractor-source phase continues up to 385 days i.e., more than a year, and perhaps stabilizes on 386th day as per the Lyapunov exponent's analysis. The time series helps to know the period of the Virus's survival in the open sources i.e. markets, open spaces and various other carriers of the Virus if not quarantined or sanitized. The Virus cease to exist in around 60 days if it does not find any carrier or infect more places, people etc. The changes in LCEs of all variables as time progresses for around 400 days have been forecasted. It can be observed that phase trajectories indicate how the two variables interact with each other and affect the overall system's dynamics. It has been observed that for exposed and asymptomatically infected (y-z), as exposed ones (y) change from 0 to 100 the value of asymptomatically infected (z) increased upto around 58, at exposed ones (y)=100, asymptomatically infected (z) has two values as 58 and 10 i.e. follows bifurcation and as exposed ones (y) changes values upto 180, the value of asymptomatically infected (z) decreases to 25 so for exposed ones (y) from 100 to 180, asymptomatically infected (z) varies from 58 to 25 to 10 follows bifurcation. Also, phase structures of exposed-symptomatically infected (y-u), exposed-removed (y-v), exposed-virus in the reservoir (y-w), asymptomatically infected-removed (z-v), symptomatically infected-removed (u-v) specifically depict bifurcations in various forms at different points. In case of asymptomatically infected-virus in the reservoir (z-w), at asymptomatically infected (z)=10, the value of viruses in the reservoir (w)=50, then as asymptomatically infected (z) increases to upto around 60. At this point, removed ones (v) increase from 50 to 70 and asymptomatically infected (z) decrease to 20 i.e., crosses the same value twice, which shows its limiting is known as limit cycle behavior and both the values tend to decrease towards zero. It shows a closed-loop limit cycle. Today, there has been no scientific revolution in the development of vaccination, nor has any antiviral treatment been successful, resulting in lack of its medication. Based on the phases, time series, and complexity analysis of the model's various parameters, it is studied to understand the variation in this pandemic's scenario.


Subject(s)
COVID-19 , SARS Virus , Humans , Nonlinear Dynamics , Pandemics , SARS-CoV-2
12.
Phys Biol ; 18(4)2021 05 28.
Article in English | MEDLINE | ID: covidwho-1192595

ABSTRACT

In this paper, we demonstrate the application of MATLAB to develop a pandemic prediction system based on Simulink. The susceptible-exposed-asymptomatic but infectious-symptomatic and infectious (severe infected population + mild infected population)-recovered-deceased (SEAI(I1+I2)RD) physical model for unsupervised learning and two types of supervised learning, namely, fuzzy proportional-integral-derivative (PID) and wavelet neural-network PID learning, are used to build a predictive-control system model that enables self-learning artificial intelligence (AI)-based control. After parameter setting, the data entering the model are predicted, and the value of the data set at a future moment is calculated. PID controllers are added to ensure that the system does not diverge at the beginning of iterative learning. To adapt to complex system conditions and afford excellent control, a wavelet neural-network PID control strategy is developed that can be adjusted and corrected in real time, according to the output error.


Subject(s)
COVID-19/epidemiology , Computer Simulation , Models, Biological , COVID-19/transmission , Deep Learning , Fuzzy Logic , Humans , India/epidemiology , Neural Networks, Computer , Nonlinear Dynamics , Pandemics , SARS-CoV-2/physiology , United States/epidemiology
13.
PLoS One ; 16(4): e0249852, 2021.
Article in English | MEDLINE | ID: covidwho-1190167

ABSTRACT

This paper employs the multifractal detrended cross-correlation analysis (MF-DCCA) model to estimate the nonlinear relationship between the money market rate and stock market liquidity in China from a multifractal perspective, leading to a better understanding of the complexity in the relationship between the interest rate and stock market liquidity. The empirical results show that the cross-correlations between the money market rate and stock market liquidity present antipersistence in the long run and that they tend to be positively persistent in the short run. The negative cross-correlations between the interest rate and stock market liquidity are more significant than the positive cross-correlations. Furthermore, the cross-correlations between the money market rate and stock market liquidity display multifractal characteristics, explaining the variations in the relationship between the interest rate and stock market liquidity at different time scales. In addition, the lower degree of multifractality in the cross-correlations between the money market rate and stock market liquidity confirms that it is effective for the interest rate to control stock market liquidity. The Chinese stock market liquidity is more sensitive to fluctuations in the money market rate in the short term and is inelastic in response to the money market rate in the long term. In particular, the positive cross-correlations between the money market rate and stock market liquidity in the short run become strong in periods of crises and emergencies. All the evidence proves that the interest rate policy is an emergency response rather than an effective response to mounting concerns regarding the economic impact of unexpected exogenous emergencies and that the interest rate cut policy will not be as effective as expected.


Subject(s)
Commerce/economics , Financial Management/economics , Models, Economic , China , Nonlinear Dynamics
14.
Chaos ; 31(4): 043109, 2021 Apr.
Article in English | MEDLINE | ID: covidwho-1185499

ABSTRACT

Recently, it has been successfully shown that the temporal evolution of the fraction of COVID-19 infected people possesses the same dynamics as the ones demonstrated by a self-organizing diffusion model over a lattice, in the frame of universality. In this brief, the relevant emerging dynamics are further investigated. Evidence that this nonlinear model demonstrates critical dynamics is scrutinized within the frame of the physics of critical phenomena. Additionally, the concept of criticality over the infected population fraction in epidemics (or a pandemic) is introduced and its importance is discussed, highlighting the emergence of the critical slowdown phenomenon. A simple method is proposed for estimating how far away a population is from this "singular" state, by utilizing the theory of critical phenomena. Finally, a dynamic approach applying the self-organized diffusion model is proposed, resulting in more accurate simulations, which can verify the effectiveness of restrictive measures. All the above are supported by real epidemic data case studies.


Subject(s)
COVID-19 , Diffusion , Humans , Nonlinear Dynamics , Pandemics , SARS-CoV-2
15.
Sci Rep ; 11(1): 7857, 2021 04 12.
Article in English | MEDLINE | ID: covidwho-1180263

ABSTRACT

Given that a substantial proportion of the subgroup of COVID-19 patients that face a severe disease course are younger than 60 years, it is critical to understand the disease-specific characteristics of young COVID-19 patients. Risk factors for a severe disease course for young COVID-19 patients and possible non-linear influences remain unknown. Data were analyzed from COVID-19 patients with clinical outcome in a single hospital in Wuhan, China, collected retrospectively from Jan 24th to Mar 27th. Clinical, demographic, treatment and laboratory data were collected from patients' medical records. Uni- and multivariable analysis using logistic regression and random forest, with the latter allowing the study of non-linear influences, were performed to investigate the clinical characteristics of a severe disease course. A total of 762 young patients (median age 47 years, interquartile range [IQR] 38-55, range 18-60; 55.9% female) were included, as well as 714 elderly patients as a comparison group. Among the young patients, 362 (47.5%) had a severe/critical disease course and the mean age was statistically significantly higher in the severe subgroup than in the mild subgroup (59.3 vs. 56.0, Student's t-test: p < 0.001). The uni- and multivariable analysis suggested that several covariates such as elevated levels of serum amyloid A (SAA), C-reactive protein (CRP) and lactate dehydrogenase (LDH), and decreased lymphocyte counts influence disease severity independently of age. Elevated levels of complement C3 (odds ratio [OR] 15.6, 95% CI 2.41-122.3; p = 0.039) are particularly associated with the risk of developing severe COVID-19 specifically in young patients, whereas no such influence seems to exist for elderly patients. Additional analysis suggests that the influence of complement C3 in young patients is independent of age, gender, and comorbidities. Variable importance values and partial dependence plots obtained using random forests delivered additional insights, in particular indicating non-linear influences of risk factors on disease severity. This study identified increased levels of complement C3 as a unique risk factor for adverse outcomes specific to young COVID-19 patients.


Subject(s)
COVID-19/blood , Complement C3/analysis , Adolescent , Adult , Area Under Curve , COVID-19/immunology , China/epidemiology , Female , Humans , Male , Middle Aged , Models, Statistical , Multivariate Analysis , Nonlinear Dynamics , Retrospective Studies , Risk Factors , Severity of Illness Index , Young Adult
16.
Comput Intell Neurosci ; 2021: 5584756, 2021.
Article in English | MEDLINE | ID: covidwho-1177603

ABSTRACT

Psychological and behavioral evidence suggests that home sports activity reduces negative moods and anxiety during lockdown days of COVID-19. Low-cost, nonintrusive, and privacy-preserving smart virtual-coach Table Tennis training assistance could help to stay active and healthy at home. In this paper, a study was performed to develop a Forehand stroke' performance evaluation system as the second principal component of the virtual-coach Table Tennis shadow-play training system. This study was conducted to show the effectiveness of the proposed LSTM model, compared with 2DCNN and RBF-SVR time-series analysis and machine learning methods, in evaluating the Table Tennis Forehand shadow-play sensory data provided by the authors. The data was generated, comprising 16 players' Forehand strokes racket's movement and orientation measurements; besides, the strokes' evaluation scores were assigned by the three coaches. The authors investigated the ML models' behaviors changed by the hyperparameters values. The experimental results of the weighted average of RMSE revealed that the modified LSTM models achieved 33.79% and 4.24% estimation error lower than 2DCNN and RBF-SVR, respectively. However, the R ¯ 2 results show that all nonlinear regression models are fit enough on the observed data. The modified LSTM is the most powerful regression method among all the three Forehand types in the current study.


Subject(s)
Deep Learning , Tennis/psychology , Aged , Algorithms , Arm/physiology , Biomechanical Phenomena , Computer Simulation , Female , Humans , Learning , Male , Middle Aged , Motor Skills , Nonlinear Dynamics , Regression Analysis
17.
J Biol Dyn ; 15(1): 195-212, 2021 12.
Article in English | MEDLINE | ID: covidwho-1172612

ABSTRACT

Incidence vs. Cumulative Cases (ICC) curves are introduced and shown to provide a simple framework for parameter identification in the case of the most elementary epidemiological model, consisting of susceptible, infected, and removed compartments. This novel methodology is used to estimate the basic reproduction ratio of recent outbreaks, including those associated with the ongoing COVID-19 pandemic.


Subject(s)
COVID-19/epidemiology , Pandemics/statistics & numerical data , SARS-CoV-2 , Basic Reproduction Number/statistics & numerical data , COVID-19/transmission , China/epidemiology , Computer Simulation , Disease Susceptibility , Gastroenteritis/epidemiology , Humans , Incidence , Mathematical Concepts , Models, Biological , Models, Statistical , Nonlinear Dynamics , Poisson Distribution , Signal-To-Noise Ratio , Spain/epidemiology
18.
J Infect Dev Ctries ; 15(2): 230-236, 2021 03 07.
Article in English | MEDLINE | ID: covidwho-1125225

ABSTRACT

INTRODUCTION: The spatiotemporal patterns of Corona Virus Disease 2019 (COVID-19) is detected in the United States, which shows temperature difference (TD) with cumulative hysteresis effect significantly changes the daily new confirmed cases after eliminating the interference of population density. METHODOLOGY: The nonlinear feature of updated cases is captured through Generalized Additive Mixed Model (GAMM) with threshold points; Exposure-response curve suggests that daily confirmed cases is changed at the different stages of TD according to the threshold points of piecewise function, which traces out the rule of updated cases under different meteorological condition. RESULTS: Our results show that the confirmed cases decreased by 0.390% (95% CI: -0.478 ~ -0.302) for increasing each one degree of TD if TD is less than 11.5°C; It will increase by 0.302% (95% CI: 0.215 ~ 0.388) for every 1°C increase in the TD (lag0-4) at the interval [11.5, 16]; Meanwhile the number of newly confirmed COVID-19 cases will increase by 0.321% (95% CI: 0.142 ~ 0.499) for every 1°C increase in the TD (lag0-4) when the TD (lag0-4) is over 16°C, and the most fluctuation occurred on Sunday. The results of the sensitivity analysis confirmed our model robust. CONCLUSIONS: In US, this interval effect of TD reminds us that it is urgent to control the spread and infection of COVID-19 when TD becomes greater in autumn and the ongoing winter.


Subject(s)
COVID-19/epidemiology , Nonlinear Dynamics , Atmospheric Pressure , Humans , Humidity , Meteorological Concepts , Population Density , Rain , Spatio-Temporal Analysis , Temperature , United States/epidemiology , Wind
19.
Chaos ; 31(2): 023136, 2021 Feb.
Article in English | MEDLINE | ID: covidwho-1114751

ABSTRACT

Using nonlinear mathematical models and experimental data from laboratory and clinical studies, we have designed new combination therapies against COVID-19.


Subject(s)
COVID-19 , Models, Biological , Nonlinear Dynamics , SARS-CoV-2 , COVID-19/epidemiology , COVID-19/therapy , Humans
20.
Sci Rep ; 11(1): 4956, 2021 03 02.
Article in English | MEDLINE | ID: covidwho-1114728

ABSTRACT

The future dynamics of the Corona Virus Disease 2019 (COVID-19) outbreak in African countries is largely unclear. Simultaneously, required strengths of intervention measures are strongly debated because containing COVID-19 in favor of the weak health care system largely conflicts with socio-economic hardships. Here we analyze the impact of interventions on outbreak dynamics for South Africa, exhibiting the largest case numbers across sub-saharan Africa, before and after their national lockdown. Past data indicate strongly reduced but still supracritical growth after lockdown. Moreover, large-scale agent-based simulations given different future scenarios for the Nelson Mandela Bay Municipality with 1.14 million inhabitants, based on detailed activity and mobility survey data of about 10% of the population, similarly suggest that current containment may be insufficient to not overload local intensive care capacity. Yet, enduring, slightly stronger or more specific interventions, combined with sufficient compliance, may constitute a viable option for interventions for South Africa.


Subject(s)
COVID-19/epidemiology , COVID-19/prevention & control , Communicable Disease Control/methods , Disease Outbreaks , Computer Simulation , Critical Care , Health Policy , Humans , Intensive Care Units , Linear Models , Nonlinear Dynamics , Physical Distancing , Quarantine , South Africa/epidemiology
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