Nonlinear dynamics modeling and epidemic forecast of COVID-19

To study the spreading trend and risk of COVID-19, according to the characteristics of COVID-19, this paper proposes a new transmission dynamic model named SLIR(susceptible-low-risk-infected-recovered), based on the classic SIR model by considering government control and personal protection measures. The equilibria, stability and bifurcation of the model are analyzed to reveal the propagation mechanism of COVID-19. In order to improve the prediction accuracy of the model, the least square method is employed to estimate the model parameters based on the real data of COVID-19 in the United States. Finally, the model is used to predict and analyze COVID-19 in the United States. The simulation results show that compared with the traditional SIR model, this model can better predict the spreading trend of COVID-19 in the United States, and the actual official data has further verified its effectiveness. The proposed model can effectively simulate the spreading of COVID-19 and help governments choose appropriate prevention and control measures. Copyright ©2023 Control and Decision.

Differential and Time-Discrete SEIRS Models with Vaccination: Local Stability, Validation and Sensitivity Analysis Using Bulgarian COVID-19 Data

Bulgaria has the lowest COVID-19 vaccination rate in the European Union and the second-highest COVID-19 mortality rate in the world. That is why we think it is important better to understand the reason for this situation and to analyse the development of the disease over time. In this paper, an extended time-dependent SEIRS model SEIRS-VB is used to investigate the long-term behaviour of the COVID-19 epidemic. This model includes vaccination and vital dynamics. To apply the SEIRS-VB model some numerical simulation tools have been developed and for this reason a family of time-discrete variants are introduced. Suitable inverse problems for the identification of parameters in discrete models are solved. A methodology is proposed for selecting a discrete model from the constructed family, which has the closest parameter values to these in the differential SEIRS-VB model. To validate the studied models, Bulgarian COVID-19 data are used. To obtain all these results for the discrete models a mathematical analysis is carried out to illustrate some biological properties of the differential model SEIRS-VB, such as the non-negativity, boundedness, existence, and uniqueness. Using the next-generation method, the basic reproduction number associated with the model in the autonomous case is defined. The local stability of the disease-free equilibrium point is studied. Finally, a sensitivity analysis of the basic reproduction number is performed.

The Nonlinearity of Working Capital and Cross-Sectional Stock Returns: Does Financial Constraints Matter?

This study is the first to examine the impacts of working capital (WC) and financial constraints on cross-sectional stock returns in Taiwan. The findings indicate a non-linear relationship between WC and stock returns. Moreover, the nonlinearity between WC and cross-sectional stock returns is robust after controlling for financial constraints, risk, and growth factors, before the Covid-19 pandemic. In contrast, there is no evidence of nonlinearity between WC and stock returns throughout the Covid-19 outbreak. In addition, the study shows that any deviations from the minimum WC level enhance the stock returns cross-sectionally. It is found that a positive Deviation effect exists in the Taiwan stock exchange before the Covid-19 pandemic by employing portfolio sorting methodologies. The return difference of the long buying highest Deviation and short selling lowest Deviation portfolios earn from 0.6% to 0.9% per month after controlling for financial constraints, risks, and growth factors. Interestingly, it is determined that the deviation effect becomes negative for small stocks during the Covid-19 pandemic, implying that investors prefer small stocks to maintain minimum working capital. The results support the trade-off theory and liquidity preference theory. Finally, the study provides insights into working capital management for managers, and investment strategies for investors during the pandemic.

Investigation on the relationship between the number of coronavirus disease 2019 cases at the beginning of the epidemic and the decrease of PM2.5 in Hubei, China: The role of temperature changes

In response to the coronavirus disease 2019 pandemic, the Chinese government implemented blockade measures in Hubei, which largely affected the emission of pollutants. This work is aimed to explore the effects of epidemics on pollutants at different temperatures in Hubei, China. We applied for a panel nonlinear model with autonomous search thresholds to explore this, using daily average temperature as a threshold variable, and PM2.5 set as the explained variable, and the cumulative number of confirmed coronavirus disease 2019 cases set as the explanatory variable. An empirical analysis was conducted by running the proposed model and using nine cities in China most impacted by the pandemic. The results show that there was a non-linear negative relationship between the cumulative number of confirmed coronavirus disease 2019 cases and PM2.5. A more detailed non-linear relationship between the two was uncovered by the proposed panel threshold regression model. When the temperature crosses the threshold value (12.5 degrees C and 20.5 degrees C) in sequence, the estimated value was -0.0688, -0.0934, and -0.1520 in that order. This means that this negative non-linear relationship increased with increasing temperature. This work helps to explore the effect of coronavirus disease 2019 on pollutions at different temperatures and provides a methodological reference to study their nonlinear relationship.

The Pandemic Waves' Impact on the Crude Oil Price and the Rise of Consumer Price Index: Case Study for Six European Countries

This study examines the response of the Consumer Price Index (CPI) in local currency to the COVID-19 pandemic using monthly data (March 2020–February 2022), comparatively for six European countries. We have introduced a model of multivariate adaptive regression that considers the quasi-periodic effects of pandemic waves in combination with the global effect of the economic shock to model the variation in the price of crude oil at international levels and to compare the induced effect of the pandemic restriction as well and the oil price variation on each country's CPI. The model was tested for the case of six emergent countries and developed European countries. The findings show that: (i) pandemic restrictions are driving a sharp rise in the CPI, and consequently inflation, in most European countries except Greece and Spain, and (ii) the emergent economies are more affected by the oil price and pandemic restriction than the developed ones.

Changing Post-Adoptive Personal Healthcare IT Use during the COVID-19 Pandemic

We examine post-adoptive IT use of fitness tracking technologies longitudinally using three data sets gathered before, during, and after the COVID-19 lockdowns in the United States. Using adaptive structuration theory (AST) as a meta-theory, we model post-adoptive IT use as having two fundamental types (continued and novel), each having distinct psychological and sociological antecedents. Sociological antecedents are further broken down into those coming from society and those coming from the technology. Findings indicate there are strong correlations between antecedents and the two types of use in all three data sets. Post-hoc analysis indicates continued and novel use vary across time. These variations are not static and appear to be non-linear. Implications and future research directions are also discussed. © 2022 IEEE Computer Society. All rights reserved.

A Novel Approach to the Two-Dimensional Cargo Load Problem

The transport and logistics sector, which include freight forwarders companies, constitutes a vast network of entities that are central to a good performance in services. With the COVID-19 pandemic and its effects on the global economy, there was a huge shortage in the number of containers available, thus creating the need to optimize the loading of available equipment to avoid waste and maximize profits from each export. The present work presents a novel approach where a set of restrictions were created that, applied in synergy with the Non-Linear GRG algorithm, aim to allocate the boxes in different consecutive lines until forming a wall, and, therefore, the walls complete the container, in order to maximize the occupancy on it. To validate the proposed approach a prototype was developed and studied in real-world problem where the solutions resulted in occupations around 80% to 90%. Thus, we can foresee the importance of the proposed approach in decision-making regarding container consolidation services. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

NON-LINEAR OPERATOR APPROXIMATIONS FOR INITIAL VALUE PROBLEMS

Time-evolution of partial differential equations is fundamental for modeling several complex dynamical processes and events forecasting, but the operators associated with such problems are non-linear. We propose a Padé approximation based exponential neural operator scheme for efficiently learning the map between a given initial condition and the activities at a later time. The multiwavelets bases are used for space discretization. By explicitly embedding the exponential operators in the model, we reduce the training parameters and make it more data-efficient which is essential in dealing with scarce and noisy real-world datasets. The Padé exponential operator uses a recurrent structure with shared parameters to model the non-linearity compared to recent neural operators that rely on using multiple linear operator layers in succession. We show theoretically that the gradients associated with the recurrent Padé network are bounded across the recurrent horizon. We perform experiments on non-linear systems such as Korteweg-de Vries (KdV) and Kuramoto-Sivashinsky (KS) equations to show that the proposed approach achieves the best performance and at the same time is data-efficient. We also show that urgent real-world problems like epidemic forecasting (for example, COVID-19) can be formulated as a 2D time-varying operator problem. The proposed Padé exponential operators yield better prediction results (53% (52%) better MAE than best neural operator (non-neural operator deep learning model)) compared to state-of-the-art forecasting models. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

Nonlinear control of Covid-19 pandemic based on the SIRD model

In the last month of 2019, a new version of Corona disease was observed in Wuhan (China) which is known as Covid-19. Several models have been proposed to predict disease treatment. The SIR model is considered one of the simplest models for the prediction of pandemic disease. This means susceptible (S), infected (I), and recovered (R) populations. The SIRD model is yet another method that includes one more equation, i.e., the number of deaths (D). This paper proposed a control law for the first time to prevent the progression of the disease. The proposed control law is based on the SIRD model and uses the feedback linearization method for the Covid-19 nonlinear model. The goal of control in this model is to reduce the number of people infected with the Covid-19 and the number of deaths due to the disease. Delay in treatment of infected people and percentage of people who should be treated are investigated as two important parameters. The results show that with the treatment of infected people in the first weeks, the number of people infected decreases by 96.3% and the number of deaths by 93.6% © 2022 IEEE.

Non-linear feature analysis of public emotion evolution for online teaching during the COVID-19 pandemic

PurposeNowadays, the breakout of the COVID-19 pandemic has caused an important change in teaching models. The emotional experience of this change has an important impact on online teaching. This paper aims to explore its time evolution characteristics and provide reference for the development of online teaching in the post epidemic era.Design/methodology/approachThe article firstly crawls the online teaching-related comment text data on Zhihu platform and performs emotional calculation to obtain a one-dimensional time series of daily average emotional values. Then, by using non-linear time-series analysis, this paper reconstructs the daily average emotion value time series in high-dimensional phase space, calculates the maximum Lyapunov exponent and correlation dimension and finally, explores the feature patterns through recurrence plot and recurrence quantification analysis.FindingsIt was found that the sequence has typical non-linear chaotic characteristics;its correlation dimension indicates that it contains obvious fractal characteristics;the public emotional evolution shows a cyclical rise and fall. By text mining and temporal evolution analysis, this paper explores the evolution law over chronically of the daily average emotion value time series, provides feasible strategies to improve students' online learning experience and quality and continuously optimizes this new teaching model in the era of pandemic.Originality/valueBased on social knowledge sharing platform of Q&A, this paper models and analyzes users interaction data under online teaching-related topics. This paper explores the evolution law over a long time period of the daily average emotion value time series using text mining and temporal evolution analysis. It then offers workable solutions to enhance the quality and experience of students' online learning, and it continuously improves this new teaching model in the age of pandemics.

Adapting a Physical Earthquake-Aftershock Model to Simulate the Spread of COVID-19.

There exists a need for a simple, deterministic, scalable, and accurate model that captures the dominant physics of pandemic propagation. We propose such a model by adapting a physical earthquake/aftershock model to COVID-19. The aftershock model revealed the physical basis for the statistical Epidemic Type Aftershock Sequence (ETAS) model as a highly non-linear diffusion process, thus permitting a grafting of the underlying physical equations into a formulation for calculating infection pressure propagation in a pandemic-type model. Our model shows that the COVID-19 pandemic propagates through an analogous porous media with hydraulic properties approximating beach sand and water. Model results show good correlations with reported cumulative infections for all cases studied. In alphabetical order, these include Austria, Belgium, Brazil, France, Germany, Italy, New Zealand, Melbourne (AU), Spain, Sweden, Switzerland, the UK, and the USA. Importantly, the model is predominantly controlled by one parameter (α), which modulates the societal recovery from the spread of the virus. The obtained recovery times for the different pandemic waves vary considerably from country to country and are reflected in the temporal evolution of registered infections. These results provide an intuition-based approach to designing and implementing mitigation measures, with predictive capabilities for various mitigation scenarios.

Epidemic modeling with heterogeneity and social diffusion.

We propose and analyze a family of epidemiological models that extend the classic Susceptible-Infectious-Recovered/Removed (SIR)-like framework to account for dynamic heterogeneity in infection risk. The family of models takes the form of a system of reaction-diffusion equations given populations structured by heterogeneous susceptibility to infection. These models describe the evolution of population-level macroscopic quantities S, I, R as in the classical case coupled with a microscopic variable f, giving the distribution of individual behavior in terms of exposure to contagion in the population of susceptibles. The reaction terms represent the impact of sculpting the distribution of susceptibles by the infection process. The diffusion and drift terms that appear in a Fokker-Planck type equation represent the impact of behavior change both during and in the absence of an epidemic. We first study the mathematical foundations of this system of reaction-diffusion equations and prove a number of its properties. In particular, we show that the system will converge back to the unique equilibrium distribution after an epidemic outbreak. We then derive a simpler system by seeking self-similar solutions to the reaction-diffusion equations in the case of Gaussian profiles. Notably, these self-similar solutions lead to a system of ordinary differential equations including classic SIR-like compartments and a new feature: the average risk level in the remaining susceptible population. We show that the simplified system exhibits a rich dynamical structure during epidemics, including plateaus, shoulders, rebounds and oscillations. Finally, we offer perspectives and caveats on ways that this family of models can help interpret the non-canonical dynamics of emerging infectious diseases, including COVID-19.

Interacting With COVID-19: How Population Behavior, Feedback and Memory Shaped Recurrent Waves of the Epidemic

Until the approval of vaccines at the end of 2020, societies relied on non-pharmaceutical interventions (NPIs) in order to control the COVID-19 pandemic. Spontaneous changes in individual behavior might have contributed to or counteracted epidemic control due to NPIs. For example, the population compliance to NPIs may have varied over time as people developed 'epidemic fatigue' or altered their perception of the risk and severity of COVID-19. Whereas official measures are well documented, the behavioral response of the citizens is harder to capture. We propose a mathematical model of the societal response, taking into account three main effects: the citizen response dynamics, the authorities' NPIs, and the occurrence of unpreventable events that significantly alter the virus transmission rate. A key assumption is that a society has a waning memory of the epidemic effects, which reflects on both the severity of the authorities' NPIs and on the citizens' compliance to the prescribed rules. This, in turn, feeds back onto the transmission rate of the disease, such that a higher number of hospitalizations decreases the probability of transmission. We show that the model is able to reproduce the COVID-19 dynamics in terms of hospital admissions for several European countries during 2020 over surprisingly long time scales. Also, it is capable of capturing the effects of disturbances (for example the emergence of new virus variants) and can be exploited for implementing control actions to limit such effects. A possible application, illustrated in this letter, consists of exploiting the estimations based on the data of one country, to predict and control the evolution in another country, where the virus spreading is still in an earlier phase. © 2017 IEEE.

Volatility spillover across Chinese carbon markets: Evidence from quantile connectedness method

The paper investigates the volatility spillover across China's carbon emission trading (CET) markets using the connectedness method based on the quantile VAR framework. The non-linear result shows strong volatility spillover effects in upper quantiles, resulting from major economic and political events. This is in accordance with the risk contagion hypothesis that volatility of carbon price returns is affected by the shocks of economic fundamentals and spills over to other pilots. Guangdong and Shanghai are the most significant contributors to volatility transmission because of their high liquidity and active markets. Hubei CET pilot has shifted from transmitter to receiver since the COVID-19 pandemic. Regarding the pairwise directional connectedness, geographical location and similar market attribute also matter in volatility transmission. This provides implications for policymakers and investors to attach importance to risk management given the quantile-based method rather than the average shocks. © 2023 Elsevier B.V.

Improving the quality of the power supply during the period of covid-19 using plug-in electric vehicles and artificial intelligence

COVID-19, corona virus disease, has been ravaging the world since the last quarter of 2019. To address this threat, the World Health Organization (WHO) has established a list of priority medical equipment that must be used by hospitals and clinics. These equipments are in fact non-linear harmonic-producing loads that have a detrimental effect on the devices and degrade the quality of the power supply. In this paper a parallel active filter: a neuronal harmonic compensation strategy based on plug-in electric vehicles (PEVs) connected to the charging stations in the parking lot of hospitals and clinics and artificial intelligence is proposed in order to improve the quality of power supply thus protecting medical equipment in order to save lives. This strategy will be simulated in MATLAB and the results will be presented as evidence of its effectiveness. © 2022 IEEE.

Non-parametric generalised newsvendor model.

In the present paper we generalise the classical newsvendor problem for critical perishable commodities having more severe costs than its linear alternative. Piece wise polynomial cost functions are introduced to accommodate the excess severity. Stochastic demand is assumed to follow a completely unknown probability distribution. Non parametric estimator of the optimal order quantity has been developed from an estimating equation using a random sample. Strong consistency of the estimator is proved for unique optimal order quantity and the result is extended for multiple solutions. Simulation results indicate that non parametric estimator is efficient in terms of mean square error. Real life application of the proposed non-parametric estimator has been demonstrated with Avocado demand in the United States of America and Covid-19 test kit demand during second wave of SARS-COV2 pandemic across 86 countries.

The association of COVID-19 incidence with temperature, humidity, and UV radiation - A global multi-city analysis.

BACKGROUND AND AIM: The associations between COVID-19 transmission and meteorological factors are scientifically debated. Several studies have been conducted worldwide, with inconsistent findings. However, often these studies had methodological issues, e.g., did not exclude important confounding factors, or had limited geographic or temporal resolution. Our aim was to quantify associations between temporal variations in COVID-19 incidence and meteorological variables globally. METHODS: We analysed data from 455 cities across 20 countries from 3 February to 31 October 2020. We used a time-series analysis that assumes a quasi-Poisson distribution of the cases and incorporates distributed lag non-linear modelling for the exposure associations at the city-level while considering effects of autocorrelation, long-term trends, and day of the week. The confounding by governmental measures was accounted for by incorporating the Oxford Governmental Stringency Index. The effects of daily mean air temperature, relative and absolute humidity, and UV radiation were estimated by applying a meta-regression of local estimates with multi-level random effects for location, country, and climatic zone. RESULTS: We found that air temperature and absolute humidity influenced the spread of COVID-19 over a lag period of 15 days. Pooling the estimates globally showed that overall low temperatures (7.5 °C compared to 17.0 °C) and low absolute humidity (6.0 g/m3 compared to 11.0 g/m3) were associated with higher COVID-19 incidence (RR temp =1.33 with 95%CI: 1.08; 1.64 and RR AH =1.33 with 95%CI: 1.12; 1.57). RH revealed no significant trend and for UV some evidence of a positive association was found. These results were robust to sensitivity analysis. However, the study results also emphasise the heterogeneity of these associations in different countries. CONCLUSION: Globally, our results suggest that comparatively low temperatures and low absolute humidity were associated with increased risks of COVID-19 incidence. However, this study underlines regional heterogeneity of weather-related effects on COVID-19 transmission.

Possible Understanding and Model-Based Prediction of Time Evolution of Number of Infected People in COVID-19 Pandemic in Japan

This paper analyzes the time evolution of the number of infected people in Japan since 2020, and important aspects are discovered. It is found out that the time evolution of the number of infected people exhibits two critical aspects of time evolution of infection;that is, the discovered specific intervals between the peak values and the valley values are useful in predicting the next peak. In addition, it is revealed that the time evolution of the increase in the number of infected people in Japan over the present 6th peak is non-linear, which is different from past characteristics since 2020. A possible background mechanism is discussed, and a possible prediction is also introduced. © 2022 IEEE.

Healthy Food Delivery: Evidences from Italy

For several years Food Delivery Business has been growing strongly both in Italy and internationally, but during the 2020 global COVID-19 epidemic, there was an even stronger increase because the benefits of online food delivery were evident, as it facilitated consumer access to prepared meals and allowed restaurant workers and suppliers to continue to operate. This research work focuses on a very particular type of food delivery, namely the one that deals only with deliveries of healthy food and superfoods. Large companies and Healthy Food Delivery (HFD) startups have different operational models and ways for service provision than traditional Restaurant Delivery systems. These are complex and non-linear mechanisms and for this reason they have proved to be interesting to analyze. The authors of this paper use a qualitative approach through multiple case studies to obtain a detailed description. A framework has been established to observe the different entrepreneurial initiatives from different perspectives. © 2022 IEEE.

Nonlinear Modeling of COVID-19 and the Significance of Testing: The Case of Kazakhstan

It has been more than two years since the world faced a global pandemic of COVID-19, which affected the global economy negatively and took many human lives. This paper considers the extended susceptible-exposed-infectious-recovered (SEIR) model and finds out whether it is effective for the government of Kazakhstan to conduct massive free PCR testing of the exposed population. To this end, we constructed a new mathematical model and the government cost function that incorporates the hospital cost for the COVID-19 treatment and the cost of PCR testing. To address the above-mentioned objectives, we constructed nonlinear differential equations for our epidemic model and numerically solved them. Furthermore, the government's cost was modeled as a function that depends on the rate of PCR tests. The findings of the numerical analysis show that the government's cost is minimized if the exposed individuals were tested for the disease as often as possible. Moreover, testing both susceptible and exposed individuals is not beneficial in terms of the economic cost. © 2022 IEEE.