A Fast Initial Response Approach to Sequential Financial Surveillance

Consider the problem of financial surveillance of a heavy-tailed time series modeled as a geometric random walk with log-Student's t increments assuming a constant volatility. Our proposed sequential testing method is based on applying the recently developed taut string (TS) univariate process monitoring scheme to the gaussianized log-differenced process data. With the signal process given by a properly scaled total variation norm of the nonparametric taut string estimator applied to the gaussianized log-differences, the change point detection procedure is constructed to have a desired in-control (IC) average run length (ARL) assuming no change in the process drift. If a change in the process drift is imminent, the proposed approach offers an effective fast initial response (FIR) instrument for rapid yet reliable change point detection. This framework may be particularly advantageous for protection against imminent upsets in financial time series in a turbulent socioeconomic and/or political environment. We illustrate how the proposed approach can be applied to sequential surveillance of real-world financial data originating from Meta Platforms, Inc. (FB) stock prices and compare the performance of the TS chart to that of the more prominent CUSUM and CUSUM FIR charts at flagging the COVID-19 related crash of February 2020. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A New Approach for Forecasting Crude Oil Prices Based on Stochastic and Deterministic Influences of LMD Using ARIMA and LSTM Models

Crude oil is one of the non-renewable power sources and is the lifeblood of the contemporary industry. Every significant change in the price of crude oil (CO) will have an effect on how the global economy, including COVID-19, develops. This study developed a novel hybrid prediction technique that depends on local mean decomposition, Autoregressive Integrated Moving Average (ARIMA), and Long Short-term Memory (LSTM) models to increase crude oil price prediction accuracy. The original data is decomposed by local mean decomposition (LMD), and the decomposed components are reconstructed into stochastic and deterministic (SD) components by average mutual information to reduce the computation cost and enhance forecasting accuracy, predict each individual reconstructed component by ARIMA, and integrate the residuals with LSTM to capture the nonlinearity in residuals and help to find the final prediction result. The new hybrid model LMD-SD-ARIMA-LSTM has reduced the volatility and solved the issue of the overfitting problem of neural networks. The proposed hybrid technique is validated using publicly accessible data from the West Texas Intermediate (WTI), and forecast accuracy are compared using accuracy measures. The value of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) for ARIMA, LSTM, LMD-ARIMA, LMD-SD-ARIMA, LMD-ARIMA-LSTM, LMD-SD-ARIMA-LSTM, and Naïve are 1.00, 1.539, 5.289, 0.873, 0.359, 0.106, 4.014 and 2.165, 1.832, 9.165, 1.359, 1.139, 1.124 and 3.821 respectively. From these results, it is concluded that the proposed model LMD-SD-ARIMA-LSTM has minimum values for MAE and MAPE which assured the superiority of the proposed model in One-step ahead forecasting. Moreover, forecasting performance is also compared up to five steps ahead. The findings demonstrate that the suggested approach is a helpful tool for predicting CO prices both in the short and long term. Furthermore, the current study reduces labor costs by combing the stationary and non-stationary Product Functions (PFs) into stochastic and deterministic components with improved accuracy. Meanwhile, the traditional econometric model can strengthen the prediction behavior of CO prices after decomposition and reconstruction, and the new hybrid forecasting method has better performance in medium and long-term forecasting of the CO price. Moreover, accurate predictions can provide reasonable advice for relevant departments to make correct decisions. © 2013 IEEE.

Does active transport lead to improved mood and performance? A panel study of travel changes during the Covid-19 lockdown in Norway

Everyday commuting is seen as a burden and an unwanted necessity for people. Recent studies have challenged this notion and have found that certain aspects of commuting can be positive. In particular, research has shown that active commuting can be an important source of everyday physical activity and a pause between arenas for daily routine. The current study uses the Covid-19 lockdown situation in Norway, and the associated travel restrictions, as a backdrop to study the relationship between active travel and self-reported mood and work performance. In a situation where people are strongly encouraged to take up active mobility forms in place of more passive forms, the often-encountered challenge of self-selection is reduced. A convenience sample was recruited via social media (N = 1319) in May 2020 and completed a total of six follow-up surveys over a period of four months, thus allowing for a panel design as well as a within-subjects comparison. The survey covered topics related to commute mode, experience of travel, current mood, and work performance. Background variables related to personality, general wellbeing as well as sociodemographic measures were also captured. Multivariate models show that those who during this period commute with active modes (walking and cycling) report a higher degree of travel satisfaction than users of passive modes (driving and public transport). Further, active modes are associated with being in a better mood, and with reporting higher work performance. Finally, looking at individuals who over time change travel mode (N = 151), we find that they report improved mood and work performance when travelling with active vs passive modes. The results have implications for policy makers and for employers looking for justification to spend company money on measures to increase active travel. © 2023 The Authors

A multimodal hybrid stochastic-based deterministic ARFIMA model for the sustainable analysis of COVID-19 pandemic

The revise is given as follows: The rapid emergence of the super-spreader COVID-19 with severe economic calamities with devastating social impact worldwide created the demand for effective research on the spread dynamics of the disease to combat and create surveillance systems on a global scale. In this study, a novel hybrid Deterministic Autoregressive Fractional Integral Moving Average (ARFIMA) model is presented to forecast the bimodal COVID-19 transmission dynamics. The heterogeneity of multimodal behavior of the COVID-19 pandemic in Pakistan is modeled by a hybrid paradigm, in which a deterministic pattern is combined with the ARFIMA model to absorb the inherent chaotic pattern of the pandemic spread. The fractional fluctuation of the real epidemic system is effectively taken as a paradigm by stochastic type improved the deterministic model and ARFIMA process. Special transformations are also introduced to enhance the convergent rate of the bimodal paradigm in deterministic modeling. The outcome of the improved deterministic model is combined with the ARFIMA model is evaluated on the spread pattern of pandemic data in Pakistan for the next 30 days. The performance-indices of the hybrid-model based on Relative-Errors and RMSE statistics confirmed the effectiveness of the proposed paradigm for long-term epidemic modeling compared to other classical and machine learning algorithms. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Adaptive Group Testing on Networks with Community Structure: The Stochastic Block Model

Group testing was conceived during World War II to identify soldiers infected with syphilis using as few tests as possible, and it has attracted renewed interest during the COVID-19 pandemic. A long-standing assumption in the probabilistic variant of the group testing problem is that individuals are infected by the disease independently. However, this assumption rarely holds in practice, as diseases often spread through interactions between individuals and therefore cause infections to be correlated. Inspired by characteristics of COVID-19 and other infectious diseases, we introduce an infection model over networks which generalizes the traditional i.i.d. model from probabilistic group testing. Under this model, we ask whether knowledge of the network structure can be leveraged to perform group testing more efficiently, focusing specifically on community-structured graphs drawn from the stochastic block model. We prove that a simple community-aware algorithm outperforms the baseline binary splitting algorithm when the model parameters are conducive to “strong community structure.”Moreover, our novel lower bounds imply that the community-aware algorithm is order-optimal in certain parameter regimes. We extend our bounds to the noisy setting and support our results with numerical experiments. IEEE

Satellite-based estimates of daily NO2 exposure in urban agglomerations of China and application to spatio-temporal characteristics of hotspots

The analysis of the daily spatial patterns of near-surface Nitrogen dioxide (NO2) concentrations can assist decision makers mitigate this common air pollutant in urban areas. However, comparative analysis of NO2 estimates in different urban agglomerations of China is limited. In this study, a new linear mixed effect model (LME) with multi-source spatiotemporal data is proposed to estimate daily NO2 concentrations at high accuracy based on the land-use regression (LUR) model and Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) products. In addition, three models for NO2 concentration estimation were evaluated and compared in four Chinese urban agglomerations from 2018 to 2020, including the COVID-19 closed management period. Each model included a unique combination of methods and satellite NO2 products: ModelⅠ: LUR model with OMI products;Model Ⅱ: LUR model with TropOMI products;Model Ⅱ: LME model with TropOMI products. The results show that the LME model outperformed the LUR model in all four urban agglomerations as the average RMSE decreased by 16.09% due to the consideration of atmospheric dispersion random effects, and using TropOMI instead of OMI products can improve the accuracy. Based on our NO2 estimations, pollution hotspots were identified, and pollution anomalies during the COVID-19 period were explored for two periods;the lockdown and revenge pollution periods. The largest NO2 pollution difference between the hotspot and non-hotspot areas occurred in the second period, especially in the heavy industrial urban agglomerations. © 2022 Elsevier Ltd

Fractal correlations in the Covid-19 genome sequence via multivariate rescaled range analysis

An approach based on fractal scaling analysis to characterize the organization of the Covid-19 genome sequences is presented in this work. The method is based on a multivariate version of the fractal rescaled range analysis implemented on a sliding window scheme to detect variations of long-range correlations over the genome sequence domains. As a preliminary step, the nucleotide sequence is mapped in a numerical sequence by following a Voss rule, resulting in a multichannel sequence represented as a binary matrix. Fractal correlations, quantified in terms of the Hurst exponent, depending on the region of the sequence, where the Covid-19 genome sequences are predominantly random, with some patches of weak long-range correlations. The analysis shows that the regions of randomness are more abundant in the Covid-19 sequences than in the primitive SARS sequence, which suggests that the Covid-19 virus possesses a more diverse genomic structure for replication and infection. The analysis constrained to the surface glycoprotein region shows that the Covid-19 sequence is less random as compared to the SARS sequence, which indicates that the Covid-19 virus can undergo more ordered replications of the spike protein. The Omicron variation exhibits an interesting pattern with some randomness similarities with the other SARS and the Covid-19 genome sequences. Overall, the results show that the multivariate rescaled range analysis provides a suitable framework to assess long-term correlations hidden in the internal organization of the Covid-19 genome sequence. © 2023

Using Social Interactions to Redefine Vaccination Strategy

The pandemics are believed to change the human perception and significantly affect the socio-economical, environmental and psychological outlook of affected people. The recent Covid-19 pandemic has challenged the state of art healthcare systems and has put modern day technology driven healthcare system to a task. While the doctors, biotechnologist, epidemiologist and technologist put their heart in, to model and study the impact of Covid-19;the researchers were tirelessly working on identifying a vaccine that can efficiently put an end to the pandemic. The mass vaccination has always seemed a solution to communicable diseases, pandemics and endemics. The authors believe an efficient vaccination strategy / model is needed to reach the major population in least possible time. It will facilitate to reach the goal of mass vaccination and decrease the spread of virus. The paper presents a PageRank based vaccination model that utilizes the depth first search to traverse a social graph that proves to converge faster than most widely used Random Walk. The idea is to prioritize the vaccination of the most connected individual who is more likely to be a victim or be a super-spreader. The paper also studies the hesitation and acceptance of vaccination among various communities. © 2022 IEEE.

Research of Variants of Modernization of Transport Terminal Infrastructure Based on Stochastic Models

Modern passenger ports and terminals (main transport hubs) require the development of new models and methods for effective work planning and accurate forecasting of infrastructure development today. Such key transport infrastructure point as development of sea passenger ports, their modernization, and changes in route cruise and ferry networks are especially relevant in conditions of passenger traffic recovery after gradual lifting of Covid-19 restrictions. In an effort to achieve pre-crisis levels of cruise ship and passenger traffic, it is necessary to incorporate stochastic processes into port management models. It is necessary to make an assessment not only based on the existing schedule of ship arrivals, but also to evaluate various options based on modeling with regard to probabilistic processes. In view of increase in size of cruise and ferry vessels, many ports face the task of modernization of berths. The Baltic Sea region was chosen as an object of research. The flow of cruise and ferry vessels of the sea passenger port "Passenger port St. Petersburg "Sea Facade"(St. Petersburg) was chosen as an object of research. As a result, of the research a new stochastic model was presented and the results were compared with known distribution laws. Based on the obtained data a reliable decision-making area on cruise lines intensity and the necessity of infrastructure modernization is defined. Then the obtained data are used for optimization experiments in the software environment AnyLogic for modeling the annual situation for the purpose of complex assessment of the seaport operation and making decisions on modernization of berths. © 2022 IEEE.

Dynamic model change analysis of social behavior processes during the COVID19 pandemic

In this paper, an analysis of changes in dynamic process models described by variables that represent social behavior from the point of view of people's mobility and of economic indices in the framework of the COVID19 pandemic is presented. Here, the mobility described by Google and Apple is used as a proxy for the social behavior to correlate it with the dynamic evolution of daily COVID19 infections. In addition, indices related from the global economy are used as a proxy of the socio-economic process, where two of ascending evolution (MSFT Microsoft and NASDAQ, Inc.) and another with smooth evolution (WTI oil gallon price) are analyzed. The evolution of such proxies are related to the daily COVID19 cases. In the latter case, it is difficult to detect a territorial region of influence given the number of origins of influences that the selected indices have, but the impact of the first peak in China and the subsequent evolution in the world can be studied, especially in our country and in the Netherlands. The main findings include that the underlying model for social behavior has changed in different stages, depending on the months of the year and that after mid-2021 an unstable equilibrium is on the track, with the addition of the new possibilities provided by the vaccination process and the rules of social coexistence. It is concluded that it is necessary to analyze which decision should be taken at the social level of public policy and which personal decisions for each individual. © 2021 IEEE.

The impact of financial development on environmental sustainability: A European perspective

This research aimed to analyze the impact of financial development on environmental sustainability. Data was collected for 34 countries in Europe, covering the period from 2000 to 2020. Data analysis was conducted using the Feasible Generalised Least Squares (FGLS) model, a random-effects model (specified by the Hausman test), and the Generalised Method of Moments (GMM) approach. It was found that lending rates are negatively related to CO2 emissions per capita, total CO2, and CO2 by the transport industry. It was also found that bank credit to the private sector increases total CO2 emissions and CO2 emissions from the power and transport industries. This study found that domestic credit to the private sector increases total CO2 emissions. An important implication of these results is that borrowers should be selected and monitored using more stringent criteria to ensure compliance with environmental requirements. This study has made multiple contributions. It has extended knowledge about how the financial sector impacts the environment. It has used two models that can handle issues of collinearity and heteroscedasticity. Its findings are useful for understanding the financial development-environmental health association in this unique COVID-19 pandemic context. © 2022 Elsevier Ltd

Multi-Objective Optimization of Car Sharing Points Under Uncertainty for Sustainable Transportation

As an important part of the sharing economy, the usage of car sharing increases world widely with the help of developments in the technology. Especially after COVID-19 the demand for private car ownership and car sharing systems increased tremendously. Therefore, its market share attracts new investors and causes existing service providers to enlarge their service area. In this article, a novel multi-objective location-dependent two-stage stochastic optimization model is proposed to determine the most appropriate locations for car sharing system and allocate the demand to these locations. The model is applied to determine the best locations among 15 candidates, and three objectives are considered, which are the minimization of total cost that comprises locating costs minus income from satisfying the demand, minimization of CO<formula><tex>$_2$</tex></formula> emission occurs by the usage of car sharing system's cars and minimization of average unsatisfied demand. Both location-independent and location-dependent demands are taken into account. The proposed model delivers a more precise decision process framework for problems include stochasticity and multiobjectivity, and it easily can be implemented to any region, providing region sensitive parameters. IEEE

An Approach and Implementation for Knowledge Graph Construction and QA System

In this paper, we investigate and propose a knowledge graph-based method and implementation of the question-and-answer (QA) system for COVID-19 cases imported from abroad. It mainly analyzes and organizes the knowledge graph construction methods based on knowledge acquisition and visualization. In addition, this paper implements the knowledge graph-based QA system by training term frequency-inverse document frequency (TF-IDF) model and Bidirectional Long Short-Term Memory + Conditional Random Field (Bi-LSTM+CRF) model as well as Cypher query statements using the graph database Neo4j. Finally, the visual intelligent interface of the QA system is designed to meet user requirements and realize the function of accurate QA. © 2021 IEEE.

Pandemic of Covid-19 as Global Entropy: When Shannon Theory fits To-Date Data of New Infections

It is well-known that Coronavirus has been propagated due to human activities mainly based at intercontinental flights. Thus, in the first months of 2020, most new countries have already presented peaks in the number of infections, so that airports and borders were closed. With the social restrictions imposed along the beginning of second semester of 2020, the curve of cases of infections has exhibited to be flat in comparison to the beginning of 2020. Therefore, the human activities of end-of-year 2020 have caused againg peaks as the second wave of the pandemic in most countries. So far, by the end of 2021, most countries particularly located at Europe, are exhibiting the fourth wave. In this paper, the entropy of Shannon is considered as inherent mechanism and responsible of waves and large peaks of the number of infections. Modelling of data, the results of this paper suggest the inherent presence of a global entropy due to the transfer of randomness between neighboring countries. © 2021 IEEE.

Accelerated Virus Spread Driven by Randomness in Human Behavior

In this paper is demonstrated that the morphology of infection’s curve is a consequence of the entropic behavior of macro-systems that are entirely dependent on the nonlinearity of social dynamics. Thus in the ongoing pandemic the so-called curve of cases would acquire an exponential morphology as consequence of the human mobility and the intensity of randomness that it exhibits still under social distancing and other types of social protection adopted in most countries along the first wave of spreading of Covid-19. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

A Hybridized Stochastic SIR-Vasiček Model in Evaluating a Pandemic Emergency Financing Facility

In the context of the present global health crisis, we examine the design and valuation of a pandemic emergency financing facility (PEFF) akin to a catastrophe (CAT) bond. While a CAT bond typically enables fund generation to the insurers and re-insurers after a disaster happens, a PEFF or pandemic bond's payout is linked to random thresholds that keep evolving as the pandemic continues to unfold. The subtle difference in the timing and structure of the funding payout between the usual CAT bond and PEFF complicates the valuation of the latter. We address this complication, and our analysis identifies certain aspects in the PEFF's design that must be simplified and strengthened so that this financial instrument is able to serve the intent of its original creation. An extension of the compartmentalized deterministic epidemic model--which describes the random number of people in three classes: susceptible (S), infected (I), and removed (R) or SIR for short--to its stochastic analog is put forward. At time t, S(t), I(t), and R(t) satisfy a system of interacting stochastic differential equations in our extended framework. The payout is triggered when the number of infected people exceeds a predetermined threshold. A CAT-bond pricing setup is developed with the Vasiček-based financial risk factor correlated with the SIR dynamics for the PEFF valuation. The probability of a pandemic occurrence during the bond's term to maturity is calculated via a Poisson process. Our sensitivity analyses reveal that the SIR's disease transmission and recovery rates, as well as the interest rate's mean-reverting level, have a substantial effect on the bond price. Our proposed synthesized model was tested and validated using a Canadian COVID-19 dataset during the early development of the pandemic. We illustrate that the PEFF's payout could occur as early as seven weeks after the official declaration of the pandemic, and the deficiencies of the most recent PEFF sold by an international financial institution could be readily rectified. IEEE

Machine learning models for COVID-19 confirmed cases prediction: A meta-analysis approach

COVID-19, CoronaVirus Disease - 2019, belongs to the genus of Coronaviridae. COVID-19 is no longer pandemic but rather endemic with the number of deaths around the world of more than 3,166,516 cases. This reality has placed a massive burden on limited healthcare systems. Thus, many researchers try to develop a prediction model to further understand this phenomenon. One of the recent methods used is machine learning models that learn from the historical data and make predictions about the events. These data mining techniques have been used to predict the number of confirmed cases of COVID-19. This paper investigated the variability of the effect size on the correlation performance of machine learning models in predicting confirmed cases of COVID-19 using meta-analysis. It explored the correlation between actual and predicted COVID-19 cases from different Neural Network machine learning models by means of estimated variance, chi-square heterogeneity (Q), heterogeneity index (I2) and random effect model. The results gave a good summary effect of 95% confidence interval. Based on chi-square heterogeneity (Q) and heterogeneity index (I2), it was found that the correlations were heterogeneous among the studies. The 95% confidence interval of effect summary also supported the difference in correlation between actual and predicted number of confirmed COVID-19 cases among the studies. There was no evidence of publication bias based on funnel plot and Egger and Begg's test. Hence, findings from this study provide evidence of good prediction performance from the Neural Network model based on a combination of studies that can later serve in the prediction of COVID-19 confirmed cases. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence.