Assessment of Economic Recovery in Hebei Province, China, under the COVID-19 Pandemic Using Nighttime Light Data

The COVID-19 pandemic has presented unprecedented disruptions to human society worldwide since late 2019, and lockdown policies in response to the pandemic have directly and drastically decreased human socioeconomic activities. To quantify and assess the extent of the pandemic's impact on the economy of Hebei Province, China, nighttime light (NTL) data, vegetation information, and provincial quarterly gross domestic product (GDP) data were jointly utilized to estimate the quarterly GDP for prefecture-level cities and county-level cities. Next, an autoregressive integrated moving average model (ARIMA) model was applied to predict the quarterly GDP for 2020 and 2021. Finally, economic recovery intensity (ERI) was used to assess the extent of economic recovery in Hebei Province during the pandemic. The results show that, at the provincial level, the economy of Hebei Province had not yet recovered;at the prefectural and county levels, three prefectures and forty counties were still struggling to restore their economies by the end of 2021, even though these economies, as a whole, were gradually recovering. In addition, the number of new infected cases correlated positively with the urban NTL during the pandemic period, but not during the post-pandemic period. The study results are informative for local government's strategies and policies for allocating financial resources for urban economic recovery in the short- and long-term. © 2022 by the authors.

Forecasting domestic waste generation during successive COVID-19 lockdowns by Bidirectional LSTM super learner neural network

Accurate prediction of domestic waste generation is a challenging task for municipalities to implement sustainable waste management strategies. In the present study, domestic waste generation in the Kingdom of Bahrain, representing a Small Island Developing State (SIDS) case study, has been investigated during successive COVID-19 lockdowns due to the pandemic in 2020. Temporal trends of daily domestic waste generation between 2019 and 2020 and their statistical analyses exhibited remarkable variations highlighting the impact of consecutive COVID-19 lockdowns on domestic waste generation. Machine learning has great potential for predicting solid waste generation rates, but only a few studies utilized deep learning approaches. The state-of-the-art Bidirectional Long Short-Term Memory (BiLSTM) network model as a deep learning method is applied to forecast daily domestic waste data in 2020. Bayesian optimization algorithm (BOA) was hybridized with BiLSTM to generate a super learner approach. The performance of the BOA-BiLSTM super learner model was further compared with the statistical ARIMA model. Performance indicators of the developed models using ARIMA and BiLSTM showed that the latter yielded superior performance for short-term forecasts of domestic waste generation. The MAE, RMSE, MAPE, and R2 were 47.38, 60.73, 256.43, and 0.46, respectively, for the ARIMA model, compared to 3.67, 12.57, 0.24, and 0.96, respectively, for the BiLSTM model. Additionally, the relative errors for the BiLSTM model were lower than those of the ARIMA model. This study highlights that the BiLSTM can be a reliable forecasting tool for solid waste management policymakers during public health emergencies. © 2022 Elsevier B.V.

Decision-making model to predict auto-rejection: An implementation of ARIMA for accurate forecasting of stock price volatility during the Covid-19

This study aims to determine an accurate forecasting model, especially an error rate of around 0, and to examine how the automatic rejection system reacts to stock price as a result of the pandemic. The statistical clustering method is used for the dataset in form of daily observations, while the sample covers the period of cases before and after COVID-19 pandemic from 02 January 2019 to 20 June 2020 at the Trinitan Minerals and Metal Company. Furthermore, the data used in the estimation are the opening and closing price of returns, which are later processed using SAS analysis tools. It is shown that the most appropriate decision-making processes are those proven to be most effective. Therefore, predicting future events based on a suitable time series model will help policymakers and strategists make decisions and develop appropriate strategic plans regarding the stock market. Meanwhile, 98% of the ARIMA (1,1,1) is a forecasting model which can be applied to predict stock prices. The new approach of this study is an integrated autoregressive moving average used as an attempt to accurately predict stock prices during a pandemic. © 2023 by the authors;licensee Growing Science, Canada.

ARIMA MODEL IN PREDICTING OF COVID-19 EPIDEMIC FOR THE SOUTHERN AFRICA REGION.

Background: Coronavirus pandemic, a serious global public health threat, affects the Southern African countries more than any other country on the continent. The region has become the epicenter of the coronavirus with South Africa accounting for the most cases. To cap the deadly effect caused by the pandemic, we apply a statistical modelling approach to investigate and predict COVID-19 incidence. Methods: Using secondary data on the daily confirmed COVID-19 cases per million for Southern Africa Development Community (SADC) member states from March 5, 2020, to July 15, 2021, we model and forecast the spread of coronavirus in the region. We select the best ARIMA model based on the log-likelihood, AIC, and BIC of the fitted models. Results: The ARIMA (11,1,11) model for the complete data set was finally selected among ARIMA models based upon the parameter test and the Box-Ljung test. The ARIMA(11,1,9) was the best candidate for the training set. A 15-day forecast was also made from the model, which shows a perfect fit with the testing set. Conclusion: The number of new COVID-19 cases per million for the SADC shows a downward trend, but the trend is characterized by peaks from time to time. Tightening up of the preventive measures continuously needs to be adapted in order to eradicate the coronavirus epidemic from the population.

Quality Food Products as a Tourist Attraction in the Province of Córdoba (Spain).

Traveling to learn about the gastronomy of a destination is becoming increasingly important among tourists, especially in the wake of the pandemic. Quality foods endorsed by protected designations of origin (PDOs) are increasingly in demand, as are experiences related to their production processes. In this study, the seven PDOs in the province of Córdoba (Spain) are analyzed. These PDOs produce olive oil, wine or ham. A field study was performed, whereby 315 gastronomic tourists who visited a gastronomic route or a PDO in Córdoba were surveyed. The objective was to characterize the profile of visiting tourists and to anticipate future demand using ARIMA models. The results indicate that the growth in gastronomic tourism in Córdoba is lower than that in the wider region, and that there are no significant differences among the different profiles (oil tourist, enotourist and ham tourists) due in part to the fact that most tourists travel from nearby regions. The novelty of this study is that three products are analyzed, and strategies are proposed to deseasonalize this type of tourism, for example, by creating a gastronomic brand that represents Córdoba and selling products under that brand (especially in international markets), by highlighting raw materials and prepared dishes and by making gastronomy a complement to heritage tourism in the city and rural tourism in the province.

Lightweight IoT-Based System for COVID-19 Patient Health Monitoring and Prediction

With the present Coronavirus disease (COVID-19) pandemic, Internet of Things (IoT)-based health monitoring devices are precious to COVID-19 patients. We present a real-time IoT-based health monitoring system that monitors patients' heart rate and oxygen saturation, the most significant measures necessary for critical care. Specifically, the proposed IoT-based system is built with Arduino Uno-based hardware and a web application for retrieving the patients' health information. In addition, we implement the Autoregressive Integrated Moving Average (ARIMA) method in the back-end server to predict future patient measurements based on current and past measurements. Compared to commercially available devices, the system's results are adequately accurate, with an acceptable RMSE for predicted value. © 2022 IEEE.

The Governance and Disclosure of IFRS 9 Economic Scenarios

Extraordinary economic conditions during the COVID-19 pandemic caused many IFRS 9 impairment models to produce unreliable results. Severe market reactions, resulting from unprecedented events, prompted swift action from the regulatory authorities to maintain the financial system's stability. Banks managed the uncertainty and volatility in the models with expert overlays, increasing the risk of biased outcomes. This study examines new ways of enhancing the governance and transparency of the IFRS 9 economic scenarios within banks and suggests additional financial disclosures. Benchmarking is proposed as a useful tool to evaluate the IFRS 9 economic scenarios and ensure effective challenge as part of a model risk governance framework. Archimedean copulas are used to generate objective economic benchmarks. Ideas around benchmarking are illustrated for a set of South African economic variables, and the outcomes are compared to the IFRS 9 scenarios published by the six biggest South African banks in their annual financial statements during the pandemic.

The Immediate Impact of Covid19 on Tourism Employment in Spain: Debunking the Myth of Job Precariousness?

This note explores the immediate impact that the COVID-19 crisis has had on tourist and non-tourist employment in Spain as a result of the state of alarm and period of confinement decreed from March 14th. The employment and self-employment series drawn from the Social Security affiliation data corresponding to the period between January 2017 and April 2020 are examined using the classical Box–Jenkins method (ARIMA) and the more recent Bayesian Structural Time-Series Models.

LSTM and ARIMA for Forecasting COVID-19 Positive and Mortality Cases in DKI Jakarta and West Java

The spread of COVID-19 in Indonesia is still classified as a pandemic until October 31, 2022. Even though the endemic has been enforced in several nations worldwide. However, the fact that people's mobility is increasing means that this condition can increase the number of new cases of COVID-19. The Indonesian government remains vigilant about any decisions that will be taken to maintain the stability of the country's health sector, economy, and population mobility. First, The purpose of this our research is to forecast of daily positive confirmed and daily mortality for the next 13 days using COVID-19 epidemiological data in Indonesia, i.e. DKI Jakarta and West Java. Second, the forecasting model uses a deep learning approach, i.e. LSTM and ARIMA. furthermore, The LSTM method and ARIMA modeling results are compared based on their respective to regions. Finally, The LSTM method has good model performance and the ability to forecast COVID-19 cases based on RMSE and MAPE. © 2022 IEEE.

Air Quality Improvement Following COVID-19 Lockdown Measures and Projected Benefits for Environmental Health

Many regions worldwide suffer from heavy air pollution caused by particulate matter (PM2.5) and nitrogen dioxide (NO2), resulting in a huge annual disease burden and significant welfare costs. Following the outbreak of the COVID-19 global pandemic, enforced curfews and restrictions on human mobility (so-called periods of 'lockdown') have become important measures to control the spread of the virus. This study aims to investigate the improvement in air quality following COVID-19 lockdown measures and the projected benefits for environmental health. China was chosen as a case study. The work projects annual premature deaths and welfare costs by integrating PM2.5 and NO2 pollutant measurements derived from satellite imagery (MODIS instruments on Terra and Aqua, and TROPOMI on Sentinel-5P) with census data archived by the Organization for Economic Co-operation and Development (OECD). A 91-day timeframe centred on the initial lockdown date of 23 January 2020 was investigated. To perform the projections, OECD data on five variables from 1990 to 2019 (mean population exposure to ambient PM2.5, premature deaths, welfare costs, gross domestic product and population) were used as training data to run the Autoregressive Integrated Moving Average (ARIMA) and multiple regression models. The analysis of the satellite imagery revealed that across the regions of Beijing, Hebei, Shandong, Henan, Xi'an, Shanghai and Hubei, the average concentrations of PM2.5 decreased by 6.2, 30.7, 14.1, 20.7, 29.3, 5.5 and 17.3%, while the NO2 decreased by 45.5, 54.7, 60.5, 58.7, 63.6, 50.5 and 66.5%, respectively, during the period of lockdown restrictions in 2020, as compared with the equivalent period in 2019. Such improvements in air quality were found to be beneficial, reducing in 2020 both the number of premature deaths by approximately 97,390 and welfare costs by over USD 74 billion.

Forecasting oil, coal, and natural gas prices in the pre-and post-COVID scenarios: Contextual evidence from India using time series forecasting tools.

Stock market price prediction is considered a critically important issue for designing future investments and consumption plans. Besides, given the fact that the COVID-19 pandemic has adversely impacted stock markets worldwide, especially over the past two years, investment decisions have become more challenging for risky. Hence, we propose a two-phase framework for forecasting prices of oil, coal, and natural gas in India, both for pre-and post-COVID-19 scenarios. Notably, the Autoregressive Integrated Moving Average, Simple Exponential Smoothing, and K- Nearest Neighbor approaches are utilized for analyses using data from January 2020 to May 2022. Besides, the various outcomes from the analytical exercises are matched with root mean squared error and mean absolute and percentage errors. Overall, the empirical outcomes show that the Autoregressive Integrated Moving Average method is appropriate for predicting India's oil, coal, and natural gas prices. Moreover, the predictive precision of oil, coal, and natural gas in the pre-COVID-19 period seems to be better than in that the post-COVID-19 stage. Additionally, prices of these energy resources are forecasted to increase through the year 2025. Finally, in line with the findings, significant policy recommendations are made.

Anti-Asian American Hate Crimes Spike During the Early Stages of the COVID-19 Pandemic.

The recent high-profile cases of hate crimes in the U.S., especially those targeting Asian Americans, have raised concerns about their risk of victimization. Following the onset of the COVID-19 pandemic, intimations-and even accusations-that the novel coronavirus is an "Asian" or "Chinese" virus have been linked to anti-Asian American hate crime, potentially leaving members of this group not only fearful of being victimized but also at risk for victimization. According to the Stop AAPI Hate Center, nearly 1900 hate crimes against Asian Americans were reported by victims, and around 69% of cases were related to verbal harassment, including being called the "Chinese Coronavirus." Yet, most of the evidence martialed on spikes in anti-Asian American hate crime during the COVID-19 pandemic has been descriptive. Using data from four U.S. cities that have large Asian American populations (New York, San Francisco, Seattle, and Washington D.C.), this study finds that hate crime against Asian Americans increased considerably in 2020 compared with that of 2019. Specifically, hate crime against Asian Americans temporarily surged after March 16, 2020, when the blaming labels including "Kung flu" or "Chinese Virus" were used publicly. However, the significant spike after March 16, 2020, in anti-Asian American hate crime was not sustained over the follow-up time period available for analysis.

Examining Hurricane Ida's Impact on Mental Health: Results From a Quasi-Experimental Analysis.

Limited research has evaluated the mental health effects during compounding disasters (e.g., a hurricane occurring during a pandemic), and few studies have examined post-disaster mental health with alternative data sources like crisis text lines. This study examined changes in crisis help-seeking for individuals in Louisiana, USA, before and after Hurricane Ida (2021), a storm that co-occurred during the COVID-19 pandemic. An interrupted time series analysis and difference-in-difference analysis for single and multiple group comparisons were used to examine pre-and post-changes in crisis text volume (i.e., any crisis text, substance use, thoughts of suicide, stress/anxiety, and bereavement) among help-seeking individuals in communities that received US Federal Emergency Management Agency individual and public assistance following a presidential disaster declaration. Results showed a significant increase in crisis texts for any reason, thoughts of suicide, stress/anxiety, and bereavement in the four-week, three-month, and four-month post-impact period. Findings highlight the need for more mental health support for residents directly impacted by disasters like Hurricane Ida.

A comparative study for COVID-19 cases forecasting with loss function as AIC and MSE in RNN family and ARIMA

Forecasting COVID-19 incidents is a trending research study in today's world. Since Machine learning models have been occupied in forecasting recently, this study focus on comparing statical and machine learning models such as ARIMA, RNN, LSTM, Seq2Seq, and Stacked LSTM. The performances were evaluated using two loss functions, namely, AIC and RMSE. The results showed that RNN performs with the lowest RMSE with-49.5% compared with the ARIMA. Seq2Seq scored the highest correlation of determination (R2) with 0.92. © 2022 IEEE.

COVID-19 Outbreak Estimation Approach Using Hybrid Time Series Modelling

In the beginning of March 2020, coronavirus was claimed to be a worldwide pandemic by the World Health Organization (WHO). In Wuhan, a region in China, around December 2019, the Corona virus, also known as the novel COVID-19 was first to arise and spread throughout the world within weeks. Depending upon publicly available data-sets, for the COVID-19 outbreak, we have developed a forecasting model with the use of hybridization of sequential and time series modelling. In our work, we assessed the main elements to forecasting the potential of COVID-19 outbreak throughout the globe. Inside the work, we have analyzed several relevant algorithms like Long short-term memory (LSTM) model (which is a sequential deep learning model), used to predict the tendency of the pandemic, Auto-Regressive Integrated Moving Average (ARIMA) method, used for analyzing and forecasting time series data, Prophet model an algorithm to construct forecasting/predictive models for time series data. Based on our analysis outcome proposed hybrid LSTM and ARIMA model outperformed other models in forecasting the trend of the Corona Virus Outbreak. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Modelability of WAR metaphors across time in cross-national COVID-19 news translation: An insight into ideology manipulation.

Previous studies have compared Covid metaphors across languages and national contexts, but seldom focus on the translation issue where news narratives of the same event may be different when translated for different readers. Another unexplored question is whether, and how, successive discursive observations across time in such narratives are related. To fill these gaps, this study employs the Box-Jenkins time series analysis (TSA) method to investigate whether and how WAR metaphor usage in Chinese-English COVID-19 news reports (source articles and their translations) can be fitted with ARIMA (Autoregressive Integrated Moving Average) models. These reports come from three different sources across the year 2020: the Chinese Global Times (GT), the American New York Times (NYT) and the British The Economist (TE). Results show that WAR metaphors in the source news of GT and TE are modelable with an autoregressive and moving average model. However, no models were found to fit their translation counterparts. By contrast, WAR metaphors in both NYT's source and translated news were not modelable. These differences are further qualitatively analyzed with examples in context. The study may contribute to the existing debates on WAR frames in COVID-19 discourse by adding a translation and TSA angle.

Improved autoregressive integrated moving average model for COVID-19 prediction by using statistical significance and clustering techniques.

Purpose: The COVID-19 pandemic has affected more than 192 countries. The condition results in a respiratory illness (e.g., influenza) with signs and symptoms such as cold, cough, fever, and breathing difficulties. Predicting new instances of COVID-19 is always a challenging task. Methods: This study improved the autoregressive integrated moving average (ARIMA)-based time series prediction model by incorporating statistical significance for feature selection and k-means clustering for outlier detection. The accuracy of the improved model (ARIMAI) was examined using World Health Organization's official data on the COVID-19 pandemic worldwide and compared with that of many modern, cutting-edge algorithms. Results: The ARIMAI model (RSS score = 0.279, accuracy = 97.75%) outperformed the current ARIMA model (RSS score = 0.659, accuracy = 93%). Conclusions: The ARIMAI model is not only an efficient but also a rapid and simple technique to forecast COVID-19 trends. The usage of this model enables the prediction of any disease that will affect patients in the future pandemics.

Analysis and Forecasting of the COVID-19 Epidemic Curve

Corona Virus Disease 2019 (COVID-19) has emerged as a supreme challenge for the whole world as well as India. As of now approximately 6.5 million people died in the world. However, the major setback to the world was in 2021 as a result of the second and third waves of COVID-19, which were caused by a different variation of COVID-19 than the first variant. The governments and health sectors were not aware of the subsequent possible waves due to the lack of data analysis competency and improper forecasting models. Hence finding an inflection point of this epidemic curve for COVID-19 infection and death is very imperative to understand different waves and variants instigating these waves. Similarly predicting the epidemic curve for the future is vital to make the government and the systems aware of the impending situation and make them prepare accordingly. Hence this work attempts to demonstrate conditions for finding inflection points and intervals which helps in finding the number of waves and the variants of COVID-19. Simultaneously the forecasting of the number of infections in forthcoming wave is also done using the auto-regressive integrated moving average model to identify the number of waves in India. The prediction of the two months data was compared with actual data for proper analysis. © 2022 IEEE.

Vaccine Supply Optimization and Forecasting using Random Forest and ARIMA Models

In order to tackle the Corona Virus Disease, it took a considerable amount of time for the governments to come up with effective and efficient vaccines. After the vaccines were developed, the next challenge was to supply the vaccines to various designated centers based on demographics, population distribution, and other factors. The whole system for vaccine supply played a vital role during the COVID-19 pandemic. We also saw a lot of haphazard and mismanagement in some places especially when the cases per day surged high, as people weren't prepared for such a situation. Now that we have got enough data, we can use it to optimize the vaccine supply across various Covid Vaccination Centers and be prepared for any such circumstances in the future. In this paper, we have proposed a two-step approach where considering the past supply and wastage data we performed a classification task that indicates whether doses are to get wasted at a given center. If yes, we then perform demand forecasting based on the number of administered doses so that the wastage can be reduced, and supply can be optimized. © 2022 IEEE.

Data Subject to Multiple Treatment Effects-Disentangle the Impacts of Global Pandemic and a Specific Disease Control Policy

Most literature works on estimating treatment effects assume that the observed data are either under the specific "treatment" or not. However, in many cases, the observed data could be subject to multiple treatments. We propose to combine econometric methods developed for different purposes to disentangle the multiple treatment effects. We illustrate this strategy by considering the impact of global pandemic v.s. the strictest "lockdown" policy of Hubei, China implemented in January, 2020. We show that although the strictest "lockdown" policy quickly contained the spread of COVID-19, it also inflicted huge economic loss on Hubei economy. It lowered Hubei GDP by about 37% compared to the level had there been no "lockdown" under the pandemic. However, even though Hubei economy managed to recover from the "lockdown", it could not escape the global impact of pandemic. Its economy is still about 90% of the level had there been no pandemic.