The COVID-19 pandemic and polarisation of income distribution in South Africa

A number of reports have shown that workers with certain characteristics are disproportionately affected by the COVID-19 pandemic. Since these characteristics are associated with vulnerable workers, we hypothesise that the income distribution in the pandemic era will be polarised compared to the pre-pandemic period. This article compares the pre-COVID income distribution (February 2020) with the one that prevailed just after the hard lockdown (April 2020). Consistent with the hypothesis, the result shows evidence of polarisation. Disaggregating the analysis by worker characteristics, we find that the polarisation was stronger in vulnerable groups. Our decomposition result suggests that, apart from job losses, returns to gender and job characteristics explain the location and shape differences in the COVID-19 era income distribution. Although this analysis only looks at the short-term effect of the pandemic on income distribution, the result suggests that the structure of labour markets in developing countries is not conducive to a future of work where disruptions (or pandemics) may become more frequent. © The Author(s), 2023. Published by Cambridge University Press on behalf of UNSW Canberra.

A hybrid model for daily air quality index prediction and its performance in the face of impact effect of COVID-19 lockdown.

Accurate and dependable air quality forecasting is critical to environmental and human health. However, most methods usually aim to improve overall prediction accuracy but neglect the accuracy for unexpected incidents. In this study, a hybrid model was developed for air quality index (AQI) forecasting, and its performance during COVID-19 lockdown was analyzed. Specifically, the variational mode decomposition (VMD) was employed to decompose the original AQI sequence into some subsequences with the parameters optimized by the Whale optimization algorithm (WOA), and the residual sequence was further decomposed by the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). On this basis, a deep learning method bidirectional long short-term memory coupled with added time filter layer and attention mechanism (TFA-BiLSTM) was employed to explore the latent dynamic characteristics of each subsequence. This WOA-VMD-CEEMDAN-TFA-BiLSTM hybrid model was used to forecast AQI values for four cities in China, and results verified that the accuracy of the hybrid model outperformed other proposed models, achieving R2 values of 0.96-0.97. In addition, the improvement in MAE (34.71-49.65%) and RMSE (32.82-48.07%) were observed over single decomposition-based model. Notably, during the epidemic lockdown period, the hybrid model had significant superiority over other proposed models for AQI prediction.

A robust NIfTI image authentication framework to ensure reliable and safe diagnosis.

Advancements in digital medical imaging technologies have significantly impacted the healthcare system. It enables the diagnosis of various diseases through the interpretation of medical images. In addition, telemedicine, including teleradiology, has been a crucial impact on remote medical consultation, especially during the COVID-19 pandemic. However, with the increasing reliance on digital medical images comes the risk of digital media attacks that can compromise the authenticity and ownership of these images. Therefore, it is crucial to develop reliable and secure methods to authenticate these images that are in NIfTI image format. The proposed method in this research involves meticulously integrating a watermark into the slice of the NIfTI image. The Slantlet transform allows modification during insertion, while the Hessenberg matrix decomposition is applied to the LL subband, which retains the most energy of the image. The Affine transform scrambles the watermark before embedding it in the slice. The hybrid combination of these functions has outperformed previous methods, with good trade-offs between security, imperceptibility, and robustness. The performance measures used, such as NC, PSNR, SNR, and SSIM, indicate good results, with PSNR ranging from 60 to 61 dB, image quality index, and NC all close to one. Furthermore, the simulation results have been tested against image processing threats, demonstrating the effectiveness of this method in ensuring the authenticity and ownership of NIfTI images. Thus, the proposed method in this research provides a reliable and secure solution for the authentication of NIfTI images, which can have significant implications in the healthcare industry.

Dating Business Cycles in France : A Reference Chronology

This paper proposes a reference quarterly chronology for periods of expansion and recession in France since 1970, carried out by the Dating Committee of the French Economic Association. The methodology is based on two pillars: 1) econometric estimations from various key data to identify candidate periods, and 2) a narrative approach that describes the economic background that prevailed at that time to finalize the dating chronology. Starting from 1970, the Committee has identified four economic recession periods: the two oil shocks 1974-1975 and 1980, the investment cycle of 1992-1993, and the Great Recession 2008-2009. For the Covid recession, the peak is dated in the last quarter of 2019 and the trough in the second quarter of 2020.

Spatiotemporal patterns and drivers of carbon emissions from municipal solid waste treatment in China.

Reducing carbon emissions from municipal solid waste (MSW) treatment is non-negligible for China to meet its "carbon peaking and carbon neutrality" targets. It is critical to objectively evaluate the spatiotemporal patterns and drivers of carbon emissions from MSW treatment. This study estimates the carbon emissions from MSW treatment across 30 Chinese provinces from 2011 to 2020. The joint approach LMDI-PDA model is further used to refine the impact of policy on carbon emission changes from technical and efficiency perspectives, while considering the socio-economic factors. The results showed that carbon emissions from MSW treatment grew significantly until peaking at 202.05Mt CO2e in 2017 and then stabilized, finally dropping to 165.10 Mt CO2e in 2020 due to the impact of COVID-19. Compared with the "12th Five-Year Plan" period, the MSW emissions intensity declined significantly during the "13th Five-Year Plan" period, indicating the effective implementation of waste emission control measures. Furthermore, the slowdown in the growth of national emissions was primarily driven by technological advances in waste treatment. Technical efficiency change effect, MSW generation intensity effect, economic scale effect, and population scale effect impeded national emissions decline. Since the performance of various drivers varied greatly in different provinces, a cluster analysis was conducted to provide policy recommendations in provinces with similar characteristics. Both the methods and results of this study can provide better decision-making support for national and provincial carbon emissions control policies targeting MSW treatment.

Production-sharing of critical resources with dynamic demand under pandemic situation: The COVID-19 pandemic.

The COVID-19 virus's high transmissibility has resulted in the virus's rapid spread throughout the world, which has brought several repercussions, ranging from a lack of sanitary and medical products to the collapse of medical systems. Hence, governments attempt to re-plan the production of medical products and reallocate limited health resources to combat the pandemic. This paper addresses a multi-period production-inventory-sharing problem (PISP) to overcome such a circumstance, considering two consumable and reusable products. We introduce a new formulation to decide on production, inventory, delivery, and sharing quantities. The sharing will depend on net supply balance, allowable demand overload, unmet demand, and the reuse cycle of reusable products. Undeniably, the dynamic demand for products during pandemic situations must be reflected effectively in addressing the multi-period PISP. A bespoke compartmental susceptible-exposed-infectious-hospitalized-recovered-susceptible (SEIHRS) epidemiological model with a control policy is proposed, which also accounts for the influence of people's behavioral response as a result of the knowledge of adequate precautions. An accelerated Benders decomposition-based algorithm with tailored valid inequalities is offered to solve the model. Finally, we consider a realistic case study - the COVID-19 pandemic in France - to examine the computational proficiency of the decomposition method. The computational results reveal that the proposed decomposition method coupled with effective valid inequalities can solve large-sized test problems in a reasonable computational time and 9.88 times faster than the commercial Gurobi solver. Moreover, the sharing mechanism reduces the total cost of the system and the unmet demand on the average up to 32.98% and 20.96%, respectively.

Daily tourism demand forecasting before and during COVID-19: data predictivity and an improved decomposition-ensemble framework

The global tourism industry is struggling to recover from the COVID-19 pandemic. During the COVID-19 pandemic, daily tourism forecasting is more critical than ever before in supporting decisions and planning. Considering the changes in tourist psyche and behaviour caused by COVID-19, this study attempts to investigate whether the statistical modelling methods can work reliably under the new normal when travel restrictions are eased or lifted. To this end, we first compare the predictivity of daily tourism demand data before and during COVID-19, and observe heterogeneous impacts across different geographical scales. Then an improved multivariate & multiscale decomposition-ensemble framework is proposed to forecast daily tourism demand. The empirical study indicates the superiority and practicability of the proposed framework before and during COVID-19. Finally, we call for more research on the comparability of tourism demand forecasting.

Drivers of employment effects in tourism-related sectors: the case of Mainland China

PurposeTourism is a labor-intensive sector with extensive links to other industries and plays a vital role in creating employment. This study aims to propose a new framework to analyze the intrinsic structure of the employment effects of tourism-related sectors and their drivers.Design/methodology/approachThis study uses input–output and structural decomposition analysis (IO-SDA) to quantify the employment effects of tourism-related sectors and their driving mechanisms based on China's I-O tables of 2002, 2007, 2012 and 2017.FindingsThe results show a declining trend in the intensity of direct or indirect employment effects in tourism-related sectors, indicating a decreasing number of jobs directly or indirectly required to create a unit of tourism output. Among tourism-related sectors, catering has the highest intensity of indirect employment effects over the study period. Catering stimulates the indirect employment of agriculture, forestry, animal husbandry, fishery and food and tobacco manufacturing. The decomposition analysis reveals that final demand is the largest contributor to the increase in tourism employment, while technological progress shifts from an employment-creation effect in 2002–2012 to an employment-destruction effect in 2012–2017.Originality/valueThis study proposes a new analytical framework to investigate the structural proportional relationship between the direct and indirect employment effects of various tourism-related sectors and their dynamic changes. Doing so, it provides valuable references for policymakers to promote tourism employment.

A Contact Matrix-Based Approach for Predicting COVID-19 Using Influenza Data

The global pandemic Corona Virus Disease 2019 (COVID-19) has become one of the deadliest epidemics in human history, bringing enormous harm to human society. To help health policymakers respond to the threat of COVID-19, prediction of outbreaks is needed. Research on COVID-19 prediction usually uses data-driven models and mechanism models. However, in the early stages of the epidemic, there were not enough data to establish a data-driven model. The inadequate understanding of the virus that causes COVID-19, SARS-COV-2, has also led to the inaccuracies of the mechanism model. This has left the government with the toughest Non-pharmaceutical interventions (NPIs) to curb the spread of the virus, such as the lockdown of Wuhan in 2020. Yet man is a social animal, and social relations and interactions are necessary for his existence. The novel coronavirus and containment measures have challenged human and community interactions, affecting the lives of individuals and collective societies. To help governments take appropriate and necessary actions in the early stages of an epidemic, and to mitigate its impact on people's psychology and lives, we used the COVID-19 pandemic as an example to develop a model that uses surveillance data from one epidemic to predict the development trend of another. Based on the fact that both influenza and COVID-19 are transmitted through infectious respiratory droplets, we hypothesized that they may have the same underlying contact structure, and we proposed the influenza data-based COVID-19 prediction (ICP) model. In this model, the underlying contact pattern is firstly inferred by using a singular value decomposition method from influenza surveillance data. Then the contact matrix was used to simulate the influenza virus transmission through close contact of people, and the influenza virus transmission model was established. In order to be able to simulate the spread of COVID-19 virus using influenza transmission models, we used influenza contact matrix and COVID-19 infection data to estimate the risk of a population contracting COVID-19, i.e. force of infection of COVID-19. Finally, we used force of infection and influenza virus transmission model to simulate and predict the spread of COVID-19 in the population. We obtained age-disaggregated influenza and COVID-19 infection data for the United States in 2020, as well as data for Europe, which was not disaggregated by age. We use correlation coefficients as an evaluation indicator, and the final results prove that the predicted value and the actual value are positively correlated. So, the development trend of COVID-19 can be predicted using influenza surveillance data. © 2022 IEEE.

Evaluating the impacts of the COVID-19 pandemic on unemployment, income distribution and poverty in Turkey

In the recent World Economic Outlook, the IMF indicates that world output shrank by 3.5% in 2020. Despite all pessimistic expectations, the Turkish economy was one of the few countries to have a positive, albeit low, economic growth rate in 2020. This was, however, achieved at the expense of high social and economic costs. The present research examines the distributional costs of this economic growth during the pandemic and suggests economic measures required to control them. The empirical examination is based on generating unavailable income and living conditions for 2020 by using the results available in TurkStat's 2017 Income and Living Conditions Survey. The actual changes in sectoral output and employment, which are available as of March 2021, are used to generate changes in the income levels of households in TurkStat's 2017 survey. The research empirically shows that adequate fiscal support with a large scope for households and businesses is necessary to compensate for economic losses caused by the pan-demic. The short-run working allowance policy appears to have been very important to improve income distribution, which might have deteriorated due to the pandemic. Direct cash support to households is considered another essential policy measure that is required to mitigate the severity of increased poverty.(c) 2022 Elsevier B.V. All rights reserved.

Influence of biochar on improving hydrological and nutrient status of two decomposed soils for yield of medicinal plant - Pinellia ternata

The root tuber of Pinellia ternata has been used as a traditional therapeutic herbal medicine. It is reported to impart beneficial attributes in recovering COVID-19 patients. To meet an increasing demand of P. ternata, this study is intended to investigate the effects of biochar on the soil hydrological and agronomic properties of two decomposed soils (i.e., completely decomposed granite (CDG) and lateritic soil) for the growth of P. ternata. The plant was grown in instrumented pots with different biochar application rate (0%, 3% and 5%) for a period of three months. Peanut shell biochar inclusion in both soils resulted in reduction of soil hydraulic conductivity and increase in soil water retention capacity. These alterations in hydrological properties were attributed to measured change in total porosity, biochar intra pore and hydrophilic functional groups. The macro-nutrient (i.e., N, P, K, Ca, and Mg) concentration of both soils increased substantially, while the pH and cation exchange capacity levels in the amended soils were altered to facilitate optimum growth of P. ternata. The tuber biomass in biochar amended CDG at all amendment rate increases by up to 70%. In case of lateritic soil, the tuber biomass increased by 23% at only 5% biochar application rate. All treatments satisfied the minimum succinic acid concentration required as per pharmacopoeia standard index. The lower tuber biomass exhibits a higher succinic acid concentration regardless of the soil type used to grow P. ternata. The biochar improved the yield and quality of P. ternata in both soils.

Study of impact of COVID-19 on industrial production indices using singular spectrum analysis

This paper investigates the impact of the COVID-19 pandemic on 8 different indices of industrial production (IIPs) for three major European countries: France, Germany, and the UK. The analysis is based on applying a combination of Singular Spectrum Analysis (SSA) algorithms, in a way that allows for the proper separation of the trend and seasonal subcycles of the IIPs. The main purpose is to illustrate how to carry out the procedure of the correct decomposition by SSA for the specific series. The accurately extracted trends are analysed and the influence of the pandemic is calculated. The results confirm that necessary goods, such as food and utilities, have low income elasticity of demand since the effect of COVID-19 is negligible for these IIPs. However, for the IIPs of less essential products, the negative impact is much more extreme, although the severity varies depending on several factors, which also aligns with the economic theory © 2023, Statistics and its Interface.All Rights Reserved.

Dependence structure among rare earth and financial markets: A multiscale-vine copula approach

This paper examines the dynamic upper and lower tail dependence across rare earth metals, clean energy, gold, world equity, base metals, and crude oil markets at various time scales. Firstly, raw return series are decomposed into various time scales using the maximum overlapping discrete wavelet transform method, then the time-varying pairwise dependencies, accounting for the impact of the covariate (in our case, the rare earth stock index), are analysed using vine-copula. This so called multiscale-vine copula approach is applied to daily data from June 25, 2009 to October 7, 2022, covering the Covid-19 outbreak. The results show that, for raw returns, the rare earth market moderates the positive dependence between world equity and clean energy markets. At the short-term time scale, unlike other pairwise dependencies, rare earth eases the dependency between clean energies. During the Covid-19 pandemic period, the rare earth stock index significantly affects the correlation of the gold and oil markets and makes them more resilient to global health shocks. At the mid-term time scale, the impact of the rare earth index is more pronounced, for both the entire sample and during the Covid-19 outbreak, as the dynamic dependencies of most indices, such as clean energy-world equity, base metals-world equity, and crude oil-clean energy, significantly decline after accounting for the influence of rare earth metals. The main result at the long-term time scale is that the Covid-19 pandemic moderates the dependency of clean energy-gold even further when considering the impact of the rare earth stock index. In general, the rare earth stock index plays a significant role in easing the extent of dependency in the medium term during the entire sample and the pandemic. These findings provide some useful implications for heterogeneous investors and market participants operating at various time scales.

Modelling output gaps in the Euro Area with structural breaks: The COVID-19 recession

This paper proposes a novel unobserved component model with a COVID-19 structural break in the trend growth rate to model output gaps. Using historical real GDP data for the Euro Area between 1995Q1 and 2022Q1, we test our framework against a battery of competing models, including a standard unobserved components model, a correlated model with a second-order Markov process, a Hodrick–Prescott filter and an augmented version of it. To examine the impact on the fitting performance, we test the inclusion and exclusion of pandemic quarters and we also extend the estimation to a country-level detail. We find that: (i) our suggested model outperforms the competing ones;(ii) when excluding pandemic quarters, the standard unobserved component model outperforms their counterparts;(iii) our model yields the best fitting performance for most of the Euro Area countries and (iv) the Hodrick–Prescott filter model has the poorest fitting performance.

Optimized DEC: An effective cough detection framework using optimal weighted Features-aided deep Ensemble classifier for COVID-19.

Since the year 2019, the entire world has been facing the most hazardous and contagious disease as Corona Virus Disease 2019 (COVID-19). Based on the symptoms, the virus can be identified and diagnosed. Amongst, cough is the primary syndrome to detect COVID-19. Existing method requires a long processing time. Early screening and detection is a complex task. To surmount the research drawbacks, a novel ensemble-based deep learning model is designed on heuristic development. The prime intention of the designed work is to detect COVID-19 disease using cough audio signals. At the initial stage, the source signals are fetched and undergo for signal decomposition phase by Empirical Mean Curve Decomposition (EMCD). Consequently, the decomposed signal is called "Mel Frequency Cepstral Coefficients (MFCC), spectral features, and statistical features". Further, all three features are fused and provide the optimal weighted features with the optimal weight value with the help of "Modified Cat and Mouse Based Optimizer (MCMBO)". Lastly, the optimal weighted features are fed as input to the Optimized Deep Ensemble Classifier (ODEC) that is fused together with various classifiers such as "Radial Basis Function (RBF), Long-Short Term Memory (LSTM), and Deep Neural Network (DNN)". In order to attain the best detection results, the parameters in ODEC are optimized by the MCMBO algorithm. Throughout the validation, the designed method attains 96% and 92% concerning accuracy and precision. Thus, result analysis elucidates that the proposed work achieves the desired detective value that aids practitioners to early diagnose COVID-19 ailments.

Feasibility of diffusion-tensor and correlated diffusion imaging for studying white-matter microstructural abnormalities: Application in COVID-19.

There has been growing attention on the effect of COVID-19 on white-matter microstructure, especially among those that self-isolated after being infected. There is also immense scientific interest and potential clinical utility to evaluate the sensitivity of single-shell diffusion magnetic resonance imaging (MRI) methods for detecting such effects. In this work, the performances of three single-shell-compatible diffusion MRI modeling methods are compared for detecting the effect of COVID-19, including diffusion-tensor imaging, diffusion-tensor decomposition of orthogonal moments and correlated diffusion imaging. Imaging was performed on self-isolated patients at the study initiation and 3-month follow-up, along with age- and sex-matched controls. We demonstrate through simulations and experimental data that correlated diffusion imaging is associated with far greater sensitivity, being the only one of the three single-shell methods to demonstrate COVID-19-related brain effects. Results suggest less restricted diffusion in the frontal lobe in COVID-19 patients, but also more restricted diffusion in the cerebellar white matter, in agreement with several existing studies highlighting the vulnerability of the cerebellum to COVID-19 infection. These results, taken together with the simulation results, suggest that a significant proportion of COVID-19 related white-matter microstructural pathology manifests as a change in tissue diffusivity. Interestingly, different b-values also confer different sensitivities to the effects. No significant difference was observed in patients at the 3-month follow-up, likely due to the limited size of the follow-up cohort. To summarize, correlated diffusion imaging is shown to be a viable single-shell diffusion analysis approach that allows us to uncover opposing patterns of diffusion changes in the frontal and cerebellar regions of COVID-19 patients, suggesting the two regions react differently to viral infection.

Analyzing the Stock Volatility Spillovers in Chinese Financial and Economic Sectors

By regarding the Chinese financial and economic sectors as a system, this article studies the stock volatility spillover in the system and explores its effects on the overall performance of the macroeconomy in China. The recent outbreak of COVID-19, U.S.-China trade friction, and three historical financial turbulences are involved to distinguish the changes in the spillover in these distinct crises, which has seldom been unveiled in the literature. By considering that the stock volatility spillover may vary over distinct timescales, the spillovers are disclosed through innovatively constructing the multi-scale spillover networks, followed by connectedness computation, based on variational mode decomposition (VMD) and generalized vector autoregression (GVAR) process. Our empirical analysis first demonstrates the different levels of increases in the total sectoral volatility spillover and changes in the roles of the sectors in the system under the aforementioned crises. Besides, the increases in the sectoral spillover in the long-term are verified to negatively impact the macroeconomy and can thereby act as warning signals.

Deep learning-based multistep ahead wind speed and power generation forecasting using direct method

Long-term effective and accurate wind power potential prediction, especially for wind farms, facilitates planning for the sustainable development of renewable energy. Accurate wind speed forecasting enhances wind power generation planning and reduces costs. Wind speed time series has nonlinearity, intermittence, and fluctuation, which makes the prediction difficult. Deep learning techniques can be beneficial when there is no specific structure to data. These techniques can predict wind speed with reasonable accuracy and reliability. In this study, four different algorithms, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolu-tional Neural Network (CNN), and CNN-LSTM, for three different long-term horizons (6 months, 1 year, and 5 years) are successfully developed using the direct method. GRU method showed a higher degree of accuracy compared to other methods. In addition, it is confirmed that using a multivariate data set increases the model's accuracy compared to the univariate model. A computational cost analysis is also conducted to compare the proposed algorithms. Finally, the power production capacity of the wind farm at a given location, Zabol city, is calculated for the next five years, which is indispensable for planning, management, and economic analysis. The reasonable conformance between the real data and predicted ones is shown to confirm the capability of the proposed model to use in long-term wind speed forecasting.

Novel Decomposition and Ensemble Model with Attention Mechanism for Container Throughput Forecasting at Four Ports in Asia

Container shipping has suffered a sharp decline since COVID-19, and risks associated with container transit will persist in the future. The decrease in container transportation has caused a ripple impact on the global supply chain. However, container throughput forecasting is both critical and complicated under the circumstances of economic uncertainty and the outbreak of the COVID-19 pandemic. A novel model propounded in this paper for container throughput forecasting to assist the port management bureau and container shipping industry integrates with the variational mode decomposition (VMD) algorithm, SARIMA technique, convolutional neural network (CNN) method, long short-term memory (LSTM) approach, and attention mechanism, among others. In this model, there are three stages: (i) data decomposition, (ii) component prediction, and (iii) ensemble output. In the first stage, the original data of the container throughput time series is decomposed into several different components using the VMD algorithm. Next, from low frequency to high frequency, each component is modeled by the corresponding prediction approach. Subsequently, the prediction results of each component generated by the previous stage are integrated into the final forecasting results by addition strategy. To enhance the prediction accuracy in the second stage, the attention mechanism is adopted in the CNN-bidirectional LSTM method. Finally, six measurement criteria, the container throughput times series at four ports, and a statistical evaluation approach are applied to comprehensively evaluate the proposed model compared with seven benchmark models. The empirical analysis demonstrates that the proposed model significantly outperforms other comparable models with regard to prediction results, level, and directional prediction accuracy.

Forecasting Commercial Vehicle Production Using Quantitative Techniques

Firms selling commercial vehicles often face difficulties due to recessions in the globalized economy. Manufacturers are keen to anticipate demand in future quarters to optimize their production schedules. In this study, commercial vehicle production data from a leading Indian automotive manufacturer were analyzed using moving averages, exponential smoothing, seasonal decomposition and autoregressive integrated moving average (ARIMA) models with the goal of forecasting. The results reveal that the ARIMA (0,1,1) model effectively predicts the sectoral downturn coinciding with the global financial crisis of 2008. As life returns to normal after the financial crisis caused by COVID-19, such models may be used to strategically move past the disruption.