Detection of Multiple Structural Breaks in Large Covariance Matrices

This article studies multiple structural breaks in large contemporaneous covariance matrices of high-dimensional time series satisfying an approximate factor model. The breaks in the second-order moment structure of the common components are due to sudden changes in either factor loadings or covariance of latent factors, requiring appropriate transformation of the factor models to facilitate estimation of the (transformed) common factors and factor loadings via the classical principal component analysis. With the estimated factors and idiosyncratic errors, an easy-to-implement CUSUM-based detection technique is introduced to consistently estimate the location and number of breaks and correctly identify whether they originate in the common or idiosyncratic error components. The algorithms of Wild Binary Segmentation for Covariance (WBS-Cov) and Wild Sparsified Binary Segmentation for Covariance (WSBS-Cov) are used to estimate breaks in the common and idiosyncratic error components, respectively. Under some technical conditions, the asymptotic properties of the proposed methodology are derived with near-optimal rates (up to a logarithmic factor) achieved for the estimated breaks. Monte Carlo simulation studies are conducted to examine the finite-sample performance of the developed method and its comparison with other existing approaches. We finally apply our method to study the contemporaneous covariance structure of daily returns of S&P 500 constituents and identify a few breaks including those occurring during the 2007–2008 financial crisis and the recent coronavirus (COVID-19) outbreak. An package "” is provided to implement the proposed algorithms.

Epidemic Analytics for Decision Supports in COVID19 Crisis

Covid-19 has hit the world unprepared, as the deadliest pandemic of the century. Governments and authorities, as leaders and decision makers fighting against the virus, enormously tap on the power of AI and its data analytics models for urgent decision supports at the greatest efforts, ever seen from human history. This book showcases a collection of important data analytics models that were used during the epidemic, and discusses and compares their efficacy and limitations. Readers who from both healthcare industries and academia can gain unique insights on how data analytics models were designed and applied on epidemic data. Taking Covid-19 as a case study, readers especially those who are working in similar fields, would be better prepared in case a new wave of virus epidemic may arise again in the near future. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Probabilistic Forecasting Model for the COVID-19 Pandemic Based on the Composite Monte Carlo Model Integrated with Deep Learning and Fuzzy System

There are several techniques to support simulation of time series behavior. In this chapter, the approach will be based on the Composite Monte Carlo (CMC) simulation method. This method is able to model future outcomes of time series under analysis from the available data. The establishment of multiple correlations and causality between the data allows modeling the variables and probabilistic distributions and subsequently obtaining also probabilistic results for time series forecasting. To improve the predictor efficiency, computational intelligence techniques are proposed, including a fuzzy inference system and an Artificial Neural Network architecture. This type of model is suitable to be considered not only for the disease monitoring and compartmental classes, but also for managerial data such as clinical resources, medical and health team allocation, and bed management, which are data related to complex decision-making challenges. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Exploring the Impact of Covid 19 on the Maritime Transport Sector

In the maritime industry, The unanticipated COVID-19 viral epidemic is an unforeseeable circumstance, and other nations implemented enormous containment measures to stop the Coronavirus epidemic from spreading around the world. Thus, directly affecting the maritime shipping sector. This paper will discuss the current problems facing the shipping industry, taking into account the congestion problems, delays, and uncertainty timeframes, using the Los Angeles port as a case study. These problems and more were addressed directly by increasing the operating hours, workload, and available staff, and indirectly by looking for alternatives for shipping goods, and creating more cargo space, furthermore, this study will use Monte Carlo simulation to predict the effectiveness of these solutions on the congestion at the port.

Strategic and flexible LNG production under uncertain future demand and natural gas prices.

The expectation in global demand for liquified natural gas (LNG) remains bullish in the coming years. Despite the unprecedented impact of the COVID-19 pandemic, and the oil price wars between OPEC and Russia in 2020, causing oversupply and falling prices, the LNG markets continue to demonstrate flexibility and resilience in delivering the needs of different sectors, whilst helping achieve the emissions targets. This is attributed to the high competitiveness amongst LNG producers and suppliers, providing greater confidence for medium-to-long term demand. However, the uncertainties in the current outlook for the return of demand and price growth in the post-COVID period pose difficulty for new liquefaction project investment decisions in the pre-Investment Decision Phase (pre-FID). Accordingly, the consideration of new production and selling strategies is needed in the early design stages of projects to cope with the shift in buyers' sentiments favouring increased reliance on spot and short-term uncontracted volumes, as well as incorporating additional flexibility into long-term contracts. In this study, the economic valuation of the flexible Air Product's AP-X liquefaction technology was investigated considering the modelling of price volatilities, using the mean-reverting jump-diffusion pricing model and Monte Carlo simulation, assuming different demand level scenarios in the high-income Asia Pacific markets based on historical trends. The results clearly demonstrate that embedding flexibility within an LNG production system allows producers and suppliers to diversify selling strategies, and take advantage of the lucrative market conditions when demand and prices increase, and hedge against market risks when demand and prices are low.

Eco-Efficiency and Its Evolutionary Change under Regulatory Constraints: A Case Study of Chinese Transportation Industry

The transportation industry is characterized as a capital-intensive industry that plays a crucial role in economic and social development, and the rapid expansion of this industry has led to serious environmental problems, which makes the eco-efficiency analysis of the transportation industry an important issue. Previous research paid little attention to the regulatory scenarios and suffered from the incomparability problem, hence this paper aims to reasonably estimate the eco-efficiency and identify its evolutionary characteristics. We measure the eco-efficiency and the corresponding global Malmquist–Luenberger productivity index using a modified model of the data envelopment analysis framework, in which different regulatory constraints are incorporated. Based on the empirical study on the transportation industry of thirty provinces in China, we find that the eco-efficiency of Chinese transportation industry experienced a slight increase during 2015–2016, a sharp decline during 2016–2017, and a continuous rise since year 2017. The Middle Yangtze River area was the best performer among the eight regions in terms of eco-efficiency, while the Southwest area was placed last. The global Malmquist–Luenberger productivity index showed an earlier increase and later decrease trend, which was quite consistent with the reality of the variation of inputs and outputs and the emergence of COVID-19. Moreover, the best practice gap change was found to be the main driven force of productivity. The empirical results verify the practicability of our measurement models and the conclusions can be adopted in guiding the formulation of corresponding policies and regulations.

Simulation of pharmacokinetic drug-drug interaction and dosage regimens optimization of nelfinavir and cepharanthine as a potential combination against COVID-19

The pharmacokinetic (PK) drug-drug interactions (DDIs) of nelfinavir and cepharanthine combination is limited information in human. In addition, the dosage regimen of this combination is not available for COVID-19 treatment. The objective of this study was to perform in silico simulations using GastroPlusTM software to predict physicochemical properties, PK parameters using the physiologically based pharmacokinetic (PBPK) model of healthy adults in different dosage regimens. The DDIs analysis of nelfinavir and cepharanthine combination was carried out to optimize the dosage regimens as a potential against COVID-19. The Spatial Data File (SDF) format of nelfinavir and cepharanthine structures obtained from PubChem database were used to carry out in silico predictions for physicochemical properties and PK parameters using several aspects of modules such as ADMET Predictor, Metabolism and Transporter, PBPK model. Subsequently, all data were utilized in the DDIs simulations. The dynamic simulation feature was selected to calculate and investigate the Cmax, AUC0-120, AUC0-inf, Cmax ratio, AUC0-120 ratio, and AUC0-inf ratio. The victim or nelfinavir dosage regimens were used four oral administration regimens of 500 mg and 750 mg in every 8 and 12 hours for simulations. The perpetrator or cepharanthine oral dosage regimens were used in several regimens from 10 mg to 120 mg in every 8, 12, and 24 hours. From all predicted results, the dosage regimen as a potential combination against COVID-19 was nelfinavir 500 mg every 8 hours and cepharanthine 10 mg every 12 hours.Copyright © 2023 by Faculty of Pharmacy, Mahidol University, Thailand is licensed under CC BY-NC-ND 4.0. To view a copy of this license, visit https://www.creativecommons.org/licenses/by-nc-nd/4.0/.

The impact of the Covid-19 pandemic on the hotel Industry's economic performance: Evidence from Portugal.

This paper estimates the impact of the Covid-19 pandemic on the economic and financial performance of the Portuguese mainland hotel industry. For that purpose, we implement a novel empirical approach to gauge the impact of the pandemic during the 2020-2021 period in terms of the industry's aggregated operating revenues, net total assets, net total debt, generated cash flow, and financial slack. To that end, we derive and estimate a sustainable growth model to project the 2020 and 2021 'Covid-free' aggregated financial statements of a representative Portuguese mainland hotel industry sample. The impact of the Covid pandemic is measured by the difference between the 'Covid-free' financial statements and the historical data drawn from the Orbis and Sabi databases. An MC simulation with bootstrapping indicates that the deviations of the deterministic from the stochastic estimates for major indicators vary between 0.5 and 5.5%. The deterministic operating cash flow estimate lies within plus or minus two standard deviations from the mean interval of the operating cash flow distribution. Based on this distribution, we estimate the downside risk, measured by cash flow at risk, at 1294 million euros. Overall findings shed some light on the economic and financial repercussions of extreme events such as the Covid-19 pandemic, providing us with a better understanding of how to design public policies and business strategies to recover from such an impact.

Designing reverse logistics network for healthcare waste management considering epidemic disruptions under uncertainty.

Population growth and recent disruptions caused by COVID-19 and many other man-made or natural disasters all around the world have considerably increased the demand for medical services, which has led to a rise in medical waste generation. The improper management of these wastes can result in a serious threat to living organisms and the environment. Designing a reverse logistics network using mathematical programming tools is an efficient and effective way to manage healthcare waste. In this regard, this paper formulates a bi-objective mixed-integer linear programming model for designing a reverse logistics network to manage healthcare waste under uncertainty and epidemic disruptions. The concept of epidemic disruptions is employed to determine the amount of waste generated in network facilities; and a Monte Carlo-based simulation approach is used for this end. The proposed model minimizes total costs and population risk, simultaneously. A fuzzy goal programming method is developed to deal with the uncertainty of the model. A simulation algorithm is developed using probabilistic distribution functions for generating data with different sizes; and then used for the evaluation of the proposed model. Finally, the efficiency of the proposed model and solution approach is confirmed using the sensitivity analysis process on the objective functions' coefficients.

Total Value Adjustment of Multi-Asset Derivatives under Multivariate CGMY Processes

Counterparty credit risk (CCR) is a significant risk factor that financial institutions have to consider in today's context, and the COVID-19 pandemic and military conflicts worldwide have heightened concerns about potential default risk. In this work, we investigate the changes in the value of financial derivatives due to counterparty default risk, i.e., total value adjustment (XVA). We perform the XVA for multi-asset option based on the multivariate Carr–Geman–Madan–Yor (CGMY) processes, which can be applied to a wider range of financial derivatives, such as basket options, rainbow options, and index options. For the numerical methods, we use the Monte Carlo method in combination with the alternating direction implicit method (MC-ADI) and the two-dimensional Fourier cosine expansion method (MC-CC) to find the risk exposure and make value adjustments for multi-asset derivatives.

Measurement of Phthalates in Settled Dust in University Dormitories and Its Implications for Exposure Assessment

Six phthalates: dimethyl phthalate (DMP), diethyl phthalate (DEP), di(n-butyl) phthalate (DnBP), butyl benzyl phthalate (BBzP), di(2-ethylhexyl) phthalate (DEHP), and di(n-octyl) phthalate (DOP) in settled dust on different indoor surfaces were measured in 30 university dormitories. A Monte Carlo simulation was used to estimate college students' exposure via inhalation, non-dietary ingestion, and dermal absorption based on measured concentrations. The detection frequencies for targeted phthalates were more than 80% except for DEP (roughly 70%). DEHP was the most prevalent compound in the dust samples, followed by DnBP, DOP, and BBzP. Statistical analysis suggested that phthalate levels were higher in bedside dust than that collected from table surfaces, indicating a nonuniform distribution of dust-phase phthalates in the sleep environment. The simulation showed that the median DMP daily intake was 0.81 μg/kg/day, which was the greatest of the targeted phthalates. For the total exposures to all phthalates, the mean contribution of exposures during the daytime and sleeping time was 54% and 46%, respectively.

Long memory in Bitcoin and ether returns and volatility and Covid-19 pandemic

PurposeThe purpose of this research is to analyze the Bitcoin (BTC) and Ether (ETH) long memory and conditional volatility.Design/methodology/approachThe empirical approach includes ARFIMA-HYGARCH and ARFIMA-FIGARCH, both models under Student‘s t-distribution, during the period (ETH: November 9, 2017 to November 25, 2021 and BTC: September 17, 2014 to November 25, 2021).FindingsFindings suggest that ARFIMA-HYGARCH is the best model to analyze BTC volatility, and ARFIMA-FIGARCH is the best approach to model ETH volatility. Empirical evidence also confirms the existence of long memory on returns and on BTC volatility parameters. Results evidence that the models proposed are not as suitable for modeling ETH volatility as they are for the BTC.Originality/valueFindings allow to confirm the fractal market hypothesis in BTC market. The data confirm that, despite the impact of the Covid-19 crisis, the dynamics of BTC returns, and volatility maintained their patterns, i.e. the way in which they evolve, in relation to the prepandemic era, did not change, but it is rather reaffirmed. Yet, ETH conditional volatility was more affected, as it is apparently higher during Covid-19. The originality of the research lies in the focus of the analysis, the proposed methodology and the variables and periods of study.

Spatial and Temporal Analysis of 2019 Novel Coronavirus (2019-Ncov) Cases in Selangor, Malaysia

The COVID-19 pandemic continues to wreak devastation on public health systems warldwide, particularly in Selangor, Malay sia, COVID-1 9 was reported from October 2020 to October 2021 at prevalent rate . In order to control and prevent: tlie spread of this pandemic, which is already underway, there is need to comprehend the spatial dimension of this disease. Therefore, the purpose of this study was to describe the patterns of COVID-a9 virus transmission in the state of Selangor. Methods: Using a Geographic Information System (GIS), and the Moran's Index (MI), spatial distribution of COVID-19 across the entire mukim was mapped and spatial statistical analysis was carried out with indications of local spatial correlations. Results: The finding revealed that the clusters were concentrated in the western and southern regions (Global Moran's I = 0.468, p = 0.05, Z = 7.01) of the state oi Selangor, thus, this research provides important information on the regional distribution and temporal dynamtcs of COV4D-Í9. Conclusion: Aa assessment ot COVID-14's geographic spread can help enhance healih care programs and resource allouation in Malaysia, specifically Selangorwhere the COVID-19 is pandemic.

Numerical studies of implied volatility expansions under the gatheral model

The Gatheral model is a three factor model with mean-reverting stochastic volatility that reverts to a stochastic long run mean. This chapter reviews previous analytical results on the first and second order implied volatility expansions under this model. Using the Monte Carlo simulation as the benchmark method, numerical studies are conducted to investigate the accuracy and properties of these analytical expansions. The classical Black-Scholes option pricing model assumes that the underlying asset follows a geometric Brownian motion with constant volatility. The chapter discusses partial calibration procedure is proposed and synthetic and real data calibration. If a full calibration is desired, we can use the results from the partial calibration as inputs for the final local optimization over all model parameters. In implementing the calibration procedure, the effect of the Covid-19 pandemic on the model calibration is high. © ISTE Ltd 2022.

Estimate the incubation period of coronavirus 2019 (COVID-19).

COVID-19 is an infectious disease that presents unprecedented challenges to society. Accurately estimating the incubation period of the coronavirus is critical for effective prevention and control. However, the exact incubation period remains unclear, as COVID-19 symptoms can appear in as little as 2 days or as long as 14 days or more after exposure. Accurate estimation requires original chain-of-infection data, which may not be fully available from the original outbreak in Wuhan, China. In this study, we estimated the incubation period of COVID-19 by leveraging well-documented and epidemiologically informative chain-of-infection data collected from 10 regions outside the original Wuhan areas prior to February 10, 2020. We employed a proposed Monte Carlo simulation approach and nonparametric methods to estimate the incubation period of COVID-19. We also utilized manifold learning and related statistical analysis to uncover incubation relationships between different age and gender groups. Our findings revealed that the incubation period of COVID-19 did not follow general distributions such as lognormal, Weibull, or Gamma. Using proposed Monte Carlo simulations and nonparametric bootstrap methods, we estimated the mean and median incubation periods as 5.84 (95% CI, 5.42-6.25 days) and 5.01 days (95% CI 4.00-6.00 days), respectively. We also found that the incubation periods of groups with ages greater than or equal to 40 years and less than 40 years demonstrated a statistically significant difference. The former group had a longer incubation period and a larger variance than the latter, suggesting the need for different quarantine times or medical intervention strategies. Our machine-learning results further demonstrated that the two age groups were linearly separable, consistent with previous statistical analyses. Additionally, our results indicated that the incubation period difference between males and females was not statistically significant.

Disease burden due to COVID-19 in Taiwan: Disability-adjusted life years (DALYs) with implication of Monte Carlo simulations.

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) has affected a large number of countries. Informing the public and decision makers of the COVID-19's economic burdens is essential for understanding the real pandemic impact. METHODS: COVID-19 premature mortality and disability impact in Taiwan was analyzed using the Taiwan National Infectious Disease Statistics System (TNIDSS) by estimating the sex/age-specific years of life lost through death (YLLs), the number of years lived with disability (YLDs), and the disability-adjusted life years (DALYs) from January 2020 to November 2021. RESULTS: Taiwan recorded 1004.13 DALYs (95% CI: 1002.75-1005.61) per 100,000 population for COVID-19, with YLLs accounting for 99.5% (95% CI: 99.3%99.6%) of all DALYs, with males suffering more from the disease than females. For population aged ≥ 70 years, the disease burdens of YLDs and YLLs were 0.1% and 99.9%, respectively. Furthermore, we found that duration of disease in critical state contributed 63.9% of the variance in DALY estimations. CONCLUSIONS: The nationwide estimation of DALYs in Taiwan provides insights into the demographic distributions and key epidemiological parameter for DALYs. The essentiality of enforcing protective precautions when needed is also implicated. The higher YLLs percentage in DALYs also revealed the fact of high confirmed death rates in Taiwan. To reduce infection risks and disease, it is crucial to maintain moderate social distancing, border control, hygiene measures, and increase vaccine coverage levels.

Possibilities of simulation of coronavirus SARS-CoV-2 by using light scattering approach.

Issues related to human coronavirus (SARS CoV-2) are a burning topic of research in present times. Due to its easily contagious nature, real experimentation under laboratory conditions requires a high level of biosafety. A powerful algorithm serves as a potential tool for the analysis of these particles. We attempted to simulate the light scattering from coronavirus (SARS CoV-2) model. Different images were modelled using a modified version of a Monte Carlo code. The results indicate that spikes on the viruses exhibit a significant scattering profile and that the presence of spikes during modelling contributes to the distinctiveness of the scattering profiles.

Optimization of sewage sampling for wastewater-based epidemiology through stochastic modeling

The proliferation of the SARS-CoV-2 global pandemic has brought to attention the need for epidemiological tools that can detect diseases in specific geographical areas through non-contact means. Such methods may protect those potentially infected by facilitating early quarantine policies to prevent the spread of the disease. Sampling of municipal wastewater has been studied as a plausible solution to detect pathogen spread, even from asymptomatic patients. However, many challenges exist in wastewater-based epidemiology such as identifying a representative sample for a population, determining the appropriate sample size, and establishing the right time and place for samples. In this work, a new approach to address these questions is assessed using stochastic modeling to represent wastewater sampling given a particular community of interest. Using estimates for various process parameters, inferences on the population infected are generated with Monte Carlo simulation output. A case study at the University of Oklahoma is examined to calibrate and evaluate the model output. Finally, extensions are provided for more efficient wastewater sampling campaigns in the future. This research provides greater insight into the effects of viral load, the percentage of the population infected, and sampling time on mean SARS-CoV-2 concentration through simulation. In doing so, an earlier warning of infection for a given population may be obtained and aid in reducing the spread of viruses. © 2023, The Author(s).

Evaluating Prediction Models for Airport Passenger Throughput Using a Hybrid Method

This paper proposes a hybrid evaluation method to assess the prediction models for airport passenger throughput (APT). By analyzing two hundred three airports in China, five types of models are evaluated to study the applicability to different airports with various airport passenger throughput and developing conditions. The models were fitted using the historical data before 2014 and were verified by using the data from 2015–2019. The evaluating results show that the models employed for evaluating perform well in general except that there are insufficient historical data for modelling, or the APT of the airports changes abruptly owing to expansion, relocation or other kinds of external forces such as earthquakes. The more the APT of an airport is, the more suitable the models are for the airport. Particularly, there is no direct relation between the complexity and the predicting accuracy of the models. If the parameters of the models are properly set, time series models, causal models, market share methods and analogy-based methods can be utilized to predict the APT of 88% of studied airports effectively.