COVID-19 Pandemic Period Research Articles

Dinamika Kinerja Keuangan Perusahaan di Sektor Kesehatan, Keuangan, dan Teknologi

This study explores the financial performance comparison of three major service sectors: healthcare, finance, and technology, with a focus on liquidity, solvency, activity, and profitability. Using data from 2021 and 2022, the study aims to provide in-depth insights into the financial strengths and weaknesses of each sector during the Covid-19 pandemic period. The analysis involves comparing the financial ratios of selected companies within each sector to industry averages to identify trends, differences, and potential issues. The results show that the healthcare sector has the highest liquidity, solvency, and profitability among the three sectors. The finance sector demonstrates the highest operational efficiency compared to the other two sectors but has the lowest liquidity, solvency, and profitability. The technology sector shows adequate liquidity, solvency, and profitability but lacks efficiency in its operations. Future strategies for the healthcare sector should include diversifying investments, optimizing operations, and enhancing customer service. For the finance sector, strategies should focus on improving liquidity and solvency, innovating in products and services to boost profitability. The technology sector should invest in automation, human resource development, and maintain a strong focus on innovation.

Machine Learning and Artificial Intelligence for a Sustainable Tourism: A Case Study on Saudi Arabia

This work conducts a rigorous examination of the economic influence of tourism in Saudi Arabia, with a particular focus on predicting tourist spending patterns and classifying spending behaviors during the COVID-19 pandemic period and its implications for sustainable development. Utilizing authentic datasets obtained from the Saudi Tourism Authority for the years 2015 to 2021, the research employs a variety of machine learning (ML) algorithms, including Decision Trees, Random Forests, K-Neighbors Classifiers, Gaussian Naive Bayes, and Support Vector Classifiers, all meticulously fine-tuned to optimize model performance. Additionally, the ARIMA model is expertly adjusted to forecast the economic landscape of tourism from 2022 to 2030, providing a robust predictive framework for future trends. The research framework is comprehensive, encompassing diligent data collection and purification, exploratory data analysis (EDA), and extensive calibration of ML algorithms through hyperparameter tuning. This thorough process tailors the predictive models to the unique dynamics of Saudi Arabia’s tourism industry, resulting in robust forecasts and insights. The findings reveal the growth trajectory of the tourism sector, highlighted by nearly 965,073 thousand tourist visits and 7,335,538 thousand overnights, with an aggregate tourist expenditure of SAR 2,246,491 million. These figures, coupled with an average expenditure of SAR 89,443 per trip and SAR 9198 per night, form a solid statistical basis for the employed predictive models. Furthermore, this research expands on how ML and AI innovations contribute to sustainable tourism practices, addressing key aspects such as resource management, economic resilience, and environmental stewardship. By integrating predictive analytics and AI-driven operational efficiencies, the study provides strategic insights for future planning and decision-making, aiming to support stakeholders in developing resilient and sustainable strategies for the tourism sector. This approach not only enhances the capacity for navigating economic complexities in a post-pandemic context, but also reinforces Saudi Arabia’s position as a premier tourism destination, with a strong emphasis on sustainability leading into 2030 and beyond.

Effectuation, Causation and Bricolage; Entrepreneurial Orientation in the Face of Crisis, a Perspective from Developing Countries

Abstract While previous studies on individual entrepreneurial orientation have evolved more from environmental, social and cultural features, far less attention has been dedicated to personal agency factors, especially during the crisis period of post COVID-19 pandemic. Relying on personal agency theory and the entrepreneurial cognitive logic concept, this study investigates how entrepreneur’s internal locus of control affect their individual entrepreneurial orientation, particularly during periods of crisis and scarcity-post COVID-19 pandemic. Data were collected from 500 small and medium scale entrepreneurs via an online questionnaire and analyzed using partial least square (PLS-SEM) methodology. The findings show that causation, bricolage and effectuation’s affordable loss and flexibility, which are most responsive to internal locus of control, significantly impact individual entrepreneurial orientation. However, no evidence has been found on how gender, education level and education background impacts the internal locus of control to promote entrepreneurial orientation. Finally, the paper highlights the relevance of considering personal agency factors, namely internal locus of control, for policy makers, especially those involved in institutions of learning and government ministries, to boost citizens perceptions and entrepreneurial orientation and behaviour particularly in developing countries.

Chemotherapy delays among cancer patients in Iran during COVID-19 pandemic

Background and objectivesFollowing the outbreak of COVID-19, a set of restrictions, health advice, and limitations were put in place to reduce the spread of the virus. These restrictions, together with fear and anxiety of the population, limited people’s access to public services such as health care services. Cancer patients during this era are a significant concern due to being at high risk for COVID-19 infection and also being exposed to delays in their diagnosis, treatment, and follow-ups. Delays in the treatment of cancer could lead to a poorer prognosis. In this study, we attempted to determine the magnitude of delays in chemotherapy and factors associated with delays during the COVID-19 pandemic.MethodAll patients diagnosed with colorectal, lung, gastric cancer, and lymphoma who had chemotherapy at teaching hospitals of Iran University of Medical Sciences (IUMS) between February 20, 2020, and March 20, 2022, were included. Age, gender, cancer type, having metastatic cancer, and date of each chemotherapy session were included for each patient individually. Every session with delays longer than two days was recorded. A three to six-day delay was considered a moderate delay, and a seven-day or longer delay was considered a severe delay in receiving each chemotherapy session. Additionally, each patient’s total number of delays in the entire course was calculated. Logistic regression was used to examine the impact of pandemic waves on delays. On the other hand, Poisson regression was used to evaluate the number of delays in the entire course of chemotherapy.ResultsThe research findings indicated an association between the male gender and having metastasis with a higher likelihood of a moderate delay in the treatment regimen. Regarding cancer type, colorectal cancer was associated with higher rates of moderate delays (IRR = 1.88, P < 0.001), but gastric (IRR = 0.75, P = 0.001) and lung cancer (IRR = 0.59, P = 0.002) were associated with reduced rates of severe and moderate delays, respectively. Compared to the COVID-19 pandemic plateau periods, the first (OR = 2.08, P < 0.001), third, and fifth waves of the pandemic were associated with increased delays.ConclusionWe found an association between the male gender, colorectal cancer, metastatic disease and higher rates of moderate delays. The initial COVID-19 pandemic wave was associated with increased severe delays in the chemotherapy course. According to the findings of this study, male cancer patients and those with metastatic cancer are at risk of poorer prognosis due to lower adherence to treatment. These findings can assist policymakers in developing targeted strategies to lessen the delay rates in the more vulnerable population.

Dynamic patterns of COVID stress syndrome among university students during an outbreak: a time-series network analysis

Objective Assessing the progression of COVID stress syndrome (CSS) and understanding how cognitive and emotional factors play a role in the dynamic system is critical for prevention and intervention during the COVID-19 pandemic. We investigated the dynamic relationship between self-reported COVID stress syndrome, cognitive factors, and emotional factors through time-series network analysis. Methods and Measures Participants were 188 university students involved in an experience sampling method study that lasted 14 days, three times a day, during the COVID-19 pandemic period following a shift in pandemic prevention and control policy. Results CSS symptoms are usually present simultaneously, and xenophobia is the most central node of the network. There is a complex mutual predictive relationship between CSS symptoms, in which traumatic stress symptoms are crucial in developing and maintaining the CSS symptom network. Negative affect was associated with CSS symptoms at the same time, and subjective health cognition was a significant predictor of CSS symptoms the next time. Conclusions Traumatic stress symptoms are essential nodes in the CSS symptom network, and negative emotions and subjective health influence the occurrence and development of CSS symptoms.

Foreign Trade as a Channel of Pandemic Transmission to the Agricultural Sector in Poland

The resilience of economic and social systems to shocks is one of the fundamental goals of sustainable development. One such event that seriously disrupted the global economy was the COVID-19 pandemic. It posed a direct threat to people’s health and lives and also caused an economic crisis. One of the effects of the crisis was the loss of stability in agricultural production with the resulting threat to food security. The foreign trade channel played a vital role in the mechanism of pandemic transmission to the agricultural sector in the world and Poland. This article aims to assess the significance of changes in international trade in agri-food commodities conducted by Polish entities during the COVID-19 pandemic. The research period covers the years 2017–2021, focusing on the years 2020–2021, the period of the COVID-19 pandemic. Such a research period allows comparative analyses during the pandemic and pre-pandemic periods. The following research methods are used in this study: a critical analysis of the literature, the descriptive approach, the comparative method, and verbal logic and descriptive statistical methods. The data used in this article come from the Central Statistical Office (CSO) and cover Poland’s foreign trade turnover according to the International Standard Trade Classification (SITC) used by the United Nations Statistics Department. The COVID-19 pandemic period did not cause a drastic collapse in Polish foreign trade. Short-lived blockages in the flow of goods, which appeared in Q2 2020, resulted in a 14% drop in the value of Polish exports. Already in Q3 2020, foreign trade turnover returned to pre-pandemic levels. In Q2 2020, the value of the exports of agri-food goods decreased by only 3%. For other commodity groups, the export values were 7–40% lower. Agri-food trade proved more resilient to pandemic shocks than trade in non-agricultural sectors. The foreign trade crisis affected the imports of goods more than exports.

Islamic cryptocurrency integration for enhanced sustainable finance: evidence from time-frequency volatility transmission investigation

ABSTRACT This study explores the potential of Islamic gold-backed cryptocurrencies in sustainable finance by focusing on volatility transmission between these cryptocurrencies and conventional digital assets like Bitcoin and PaxGold. Using a Time-Varying Parameter Vector Autoregression (TVP-VAR) approach, we analyze data from December 2019 to July 2023, including the COVID-19 pandemic period. Findings reveal a complex volatility network, with Bitcoin and HelloGold as major transmitters of spillover shocks, while X8X and PaxGold mainly serve as net recipients. Notable interactions between Bitcoin and Islamic gold-backed cryptocurrency markets show short-lived pairwise volatility interactions. Islamic gold-backed cryptocurrencies, net recipients of volatility, absorb market shocks, making them suitable for hedging against volatility. This can aid in diversifying portfolios, mitigating adverse market impacts, and informing risk management and regulatory approaches. Our research emphasizes the importance of Islamic finance in ethical and sustainable investment within the evolving cryptocurrency market.

The effect of the COVID-19 pandemic lockdown on the distribution of fat tissue and skinfold thickness in adolescents from Kraków (Poland).

The COVID-19 pandemic has significantly changed the lifestyle and dietary habits of societies worldwide. The aim of the study was to assess changes in the distribution of fat tissue and skinfold thickness in adolescents (aged 11-15) from Kraków during the COVID-19 pandemic period. Two cross-sectional studies conducted in 2020 and 2022 (before and after the pandemic) involved 1662 adolescents from the Kraków population. Skinfold thickness measurements were taken. The trunk adiposity index, limbs-to-trunk fat ratio and limbs-to-total skinfold ratio were calculated. Statistical analysis employed a two-way ANOVA, Tukey's HSD test and the Kruskal-Wallis test, depending on the normality of the distribution. Higher skinfold thicknesses were observed in the majority of age groups in both sexes from the 2022 cohort in comparison with the 2020 cohort. Additionally, higher values of the trunk adiposity index and the limbs-to-total skinfold ratio in most age groups were observed. However, an inverse trend was observed in the limbs-to-trunk fat radio. The introduced restrictions such as lockdown, remote learning, reduced physical activity and changes in dietary habits could have influenced the distribution of fat tissue and skinfold thickness amongst adolescents from Kraków.

Data architectures and the prevalent cyberattacks encountered by West African higher educational institutions in the COVID-19 era

The growing interconnectedness of the world has led to a rise in cybersecurity risks. Although it is increasingly conventional to use technology to assist business transactions, exposure to these risks must be minimised to allow business owners to do transactions in a secure manner. While a wide range of studies have been undertaken regarding the effects of cyberattacks on several industries and sectors, However, very few studies have focused on the effects of cyberattacks on the educational sector, specifically higher educational institutions (HEIs) in West Africa. Consequently, this study developed a survey and distributed it to HEIs particularly universities in West Africa to examine the data architectures they employed, the cyberattacks they encountered during the COVID-19 pandemic period, and the role of data analysis in decision-making, as well as the countermeasures employed in identifying and preventing cyberattacks. A total of one thousand, one hundred and sixty-four (1164) responses were received from ninety-three (93) HEIs and analysed. According to the study’s findings, data-informed architecture was adopted by 71.8% of HEIs, data-driven architecture by 24.1%, and data-centric architecture by 4.1%, all of which were vulnerable to cyberattacks. In addition, there are further concerns around data analysis techniques, staff training gaps, and countermeasures for cyberattacks. The study’s conclusion includes suggestions for future research topics and recommendations for repelling cyberattacks in HEIs.

Using a Smartphone-Based Chatbot for Postoperative Care After Intravitreal Injection During the COVID-19 Pandemic: Retrospective Cohort Study.

During the COVID-19 pandemic period, it was difficult to carry out regular and scheduled follow-up of patients in the outpatient department, especially during lockdown periods. However, early detection of initial infection or other serious conditions is vital for patients after ocular surgery, such as intravitreal injection (IVI) for age-related macular degeneration (AMD). We evaluated the use of a smartphone-based postoperative care chatbot system (PCCS) with an instant bidirectional feedback system for patients to self-report postoperative symptoms and signs. During the COVID-19 level 3 epidemic alert in July 2021 in Taiwan, the PCCS alerted the patients to report and grade 6 ocular symptoms and signs associated with ocular inflammation or retinal detachment. Patients used the PCCS for 7 days post surgery to assess their symptoms and signs each day after receiving an alert. Data were automatically collected using a cloud computer system, including symptom grades and messages sent to medical staff for further medical assistance. A user satisfaction questionnaire was administered to the patients on the seventh day post surgery. In total, 185 patients participated in this study. There were 26 (3.03%) reports of symptom grade deterioration (including increased blurred vision, eye swelling, nausea, and floaters or flashes) from 12 (6.5%) patients. We found no difference in the gender of patients who received an early medical consultation. One case of endophthalmitis was reported, wherein an improvement was observed after prompt administration of IVI antibiotics twice. Overall, 87% (n=185) of patients were satisfied or very satisfied with communicating their symptoms instantly through the app; they were willing to use it again and believed that it could improve the quality of care. Seven of the 185 (3.8%) patients had an earlier medical consultation and 1 (0.5%) had endophthalmitis. The chatbot system, designed for self-reporting postoperative symptoms and providing instant bidirectional feedback on smartphones, could be beneficial for enhancing the quality of care in early medical consultations without gender differences among patients with AMD receiving IVI, and achieved satisfactory responses from patients.

Empirical Analysis of Portfolio Allocation During Market Turbulence Based on Efficient Frontier

The global financial markets have experienced considerable turmoil due to the COVID-19 pandemic, in this context, portfolio management becomes critically important. This paper employs Monte Carlo Simulation to determine Efficient Frontier of a portfolio comprising fourteen US technology stocks in the period of COVID-19 pandemic by python, which also allocates portfolios for Minimum Volatility and Maximum Sharpe Ratio, along with their key parameters. According to this study, Maximum Sharpe Ratio portfolio outperforms Minimum Volatility portfolio in terms of cumulative return. However, its important to note that Maximum Sharpe Ratio portfolio also experiences a greater maximum drawdown. Additionally, this paper visually depicts the optimal allocation of asset weights across different levels of risk aversion using a chart. Furthermore, it identifies NVDA as the optimal choice for investors who have low degree of risk-aversion due to its high volatility and expected return. Conversely, IBM and CSCO emerge as the preferred choices for risk-averse investors, as they effectively mitigate the portfolios entire risk.

Analysis of Change in Marriage and Divorce and Associated Socioeconomic Variables during the COVID-19 Pandemic Period

Due to the COVID-19 pandemic, people have contended with health, economic, and social issues on a global scale. Therefore, the study aimed to investigate the effect of the COVID-19 epidemic on marriage-divorce, (Crude marriage rate, divorce rate, birth rate, male and female first marriage age) and their socioeconomic related indicators (The ratio of female high school and undergraduate school graduates, man and women employment rates, unemployment rate, GDP per capita,suicide rate). The regions had the highest female employment rate were differentiated by lowest crude marriage and crude birth rates. In addition to this, the regions had the highest divorce rate were differentiated by the highest educated women rate and GDP per capita. As a result, the regional differences were high in the country in terms of study indicators.

The Legacy of the COVID-19 Pandemic: Impact on Infant and Maternal and Health from an Appalachian Academic Medical Center.

The COVID-19 pandemic period from 2020 to 2022 had a significant impact on maternal infant health with mothers impacted more than their infants. We questioned whether there have been any lingering effects from the pandemic. We examined intermediate and long-term pandemic effects on maternal and neonatal outcomes before, during, and after the COVID-19 pandemic period. We reviewed mother-infant pairs from the following three epochs: (1) the pre-COVID-19 period, (2) the COVID-19 pandemic period, and (3) the post-pandemic period. The Case Mix Index (CMI) for the neonates from all three epochs were detailed. Post-pandemic, we noted a rising trend of LGA infants (10%) and an increase in SGA infants (13%). For women in 2023, we noted an increase in hypertension, preeclampsia, diabetes, and a higher BMI than in the pre-pandemic period. There have also been more congenital anomalies (9%), and neonatal CMI increased in the post-pandemic period. Well after the pandemic period, maternal-infant health continues to be affected. For women, the increase in hypertension and diabetes during pregnancy is concerning. For infants, being LGA or SGA may have long-term consequences. The post-pandemic increase in infants with congenital anomalies compared to the pre-pandemic era is an area that needs ongoing review.

Hazardous Elements in the Edible Portion of Perna perna Mussels: Relation with the COVID-19 Pandemic Period and Assessment of the Risk to Human Health.

In this work, the concentrations of hazardous elements (As, Cd and Pb) in the edible portion of the Perna perna mussel from Southeast Brazil were analyzed to understand the effects of the COVID-19 pandemic on the coastal environment and to evaluate the quality of this fishery resource. Decreases in anthropogenic chemical outputs to the environment were expected to occur during the COVID-19 pandemic, with decreases in element concentrations in mussels. The ranges of median concentrations (µgg-1dw) in the pre- and pandemic periods were 5.4-16.1 and 2.2-10.6 for As; 0.2-0.6 and 0.1-0.5 for Cd; and 1.2-3.2 and 0.7-1.8 for Pb, respectively. Temporal variations (prepandemic x pandemic) were more significant than spatial variations (five sampling sites). The relationships between the concentrations of hazardous elements and isotopic ratios (δ15N and δ13C) suggested that food sources were more diverse across the sampling sites during the pandemic period, when individuals exhibited less efficient trophic transfer. The concentrations of Cd and Pb were below the tolerable maximum limit, whereas for As, they were above the limit; however, these concentrations are not a risk to human health, as most As was present in the least toxic organic form. The intake estimates were below the tolerable intake limits, and only Pb concentrations are at risk of causing concern if the intake of mussels increases. This is the first study in Brazil that presents a spatial-temporal comparison of hazardous elements in marine fauna considering COVID-19 as a temporal landmark. The results are of interest for both public health and environmental health management in a post-COVID-19 scenario.

Classification of supervised deep learning models for COVID-19 tweets sentiment analysis

Social media provided a successful management of human societies in light of global crises. Social media platforms have been considered the central authority in guiding society, receiving information and conducting business in many countries during the COVID-19 pandemic period in March 2020. The social platform has seen an increase in use of 45% for public platforms and 35% for the use of messages. This study suggests An AI-based model for predicting the likelihood of infection with COVID-19 through sentiment analysis and early detection using a Natural Language Processing library with deep learning techniques CNN. The model performed improved the distinction between patients who are 'positive' and patients who are 'natural' and unaffected are 'negative'. The performance of the model was tested using publicly available databases on Twitter for the period from March 16, 2020 to April 14, 2020.The achieved accuracy percentage was (~99.8% )) and based on the four measures Accuracy, Recall, Precision and F1-score.

Perceptions and Difficulties of Distance Learning Among Beginning Teachers and Kindergarteners During the Covid-19 Pandemic Period

Exploring the perceptions and challenges faced by beginning teachers and kindergarten teachers in the realm of distance learning reveals critical insights into the evolving landscape of early education in a digital age. The present study explored the challenges and difficulties experienced by beginning teachers and kindergarteners in the context of distance learning. Qualitative research was conducted through semi-structured interviews involving 254 novice teachers and kindergarten educators. The findings revealed a range of difficulties new teachers face in the distance learning environment. These included challenges in conveying educational content, issues with Wi-Fi and internet connectivity, a lack of resources and technological tools, difficulties in engaging students in Zoom meetings, poor communication between teachers and students, insufficient preparation and training for distance teaching, inadequate digital skills, and a lack of experience among parents with the new method of learning. Understanding the challenges and difficulties that novice teachers encounter, particularly during their initial professional years and in exceptional circumstances like the COVID-19 pandemic, is crucial. This understanding not only aids in addressing and surmounting these challenges but also contributes to the enhancement and development of current distance teaching models. Such improvements should consider the specific needs and challenges of new teachers. L'étude des perceptions et des difficultés rencontrées par les enseignants débutants et les enseignants de maternelle dans le domaine de l'enseignement à distance révèle des informations essentielles sur l'évolution du paysage de l'éducation préscolaire à l'ère numérique. La présente étude a exploré les défis et les difficultés rencontrés par les enseignants débutants et les enseignants de maternelle dans le contexte de l'enseignement à distance. La recherche qualitative a été menée par le biais d'entrevues semi-structurées auxquelles ont participé 254 enseignants débutants et éducateurs de maternelle. Les résultats ont révélé une série de difficultés auxquelles les nouveaux enseignants sont confrontés dans le milieu de l'enseignement à distance. Il s'agit notamment des difficultés à transmettre le contenu éducatif, des problèmes de connexion Wi-Fi et Internet, du manque de ressources et d'outils technologiques, des difficultés à faire participer les élèves aux réunions Zoom, de la mauvaise communication entre les enseignants et les élèves, du manque de préparation et de formation à l'enseignement à distance, des compétences numériques inadéquates et du manque d'expérience des parents en ce qui concerne cette nouvelle méthode d'apprentissage. Il est essentiel de comprendre les défis et les difficultés que rencontrent les enseignants débutants, en particulier au cours de leurs premières années professionnelles et dans des circonstances exceptionnelles telles que la pandémie de COVID-19. Cette compréhension permet non seulement d'aborder et de surmonter ces défis, mais aussi de contribuer à l'amélioration et au développement des modèles actuels d'enseignement à distance. Ces améliorations devraient prendre en compte les besoins et les défis spécifiques des nouveaux enseignants.

Improvement of Air Quality During Lockdown Period of COVID-19 Pandemic in Kathmandu Valley, Nepal

The Kathmandu Valley is currently Nepal's most hazardous region for human health and environmental sustainability because of increased air pollution and high particulate matter levels. According to multiple studies and observations, Nepal's urban districts saw an improvement in air quality during the COVID-19 lockdown. The Air Quality Index (AQI) is a standard method of informing the public about air pollution levels. The AQI is based on the quantitative value of pollutants' constituent parts, such as particulate matter (PM). Thus, the focus of this study was on how the AQI values change in the Ratna Park suburb of the Kathmandu district in the Kathmandu Valley and how those fluctuations are related to lockdown and non-lockdown times during the COVID-19 pandemic and comparing them to identify crucial information’s. This study shows that during the lockdown, the air quality in the Kathmandu Valley dramatically improved, with average AQI values for PM2.5 and PM10 rising to 21.34 and 64.42 from 50.74 and 106.51, respectively. Furthermore, we investigate the positive correlation between relative humidity, a meteorological component, and air quality.

Tourism forecasting by mixed-frequency machine learning

This study aims to establish the nonlinear relationship between high frequency Baidu search volumes, low frequency tourist arrivals and dummy variables and proposes a mixed-frequency machine learning model: the Bidirectional Long Short-Term Memory Mixed Frequency Data Sampling (BiLSTM-MIDAS) model. The empirical results of forecasting weekly tourist arrivals to Kulangsu and Jiuzhaigou Valley in China demonstrate that (1) BiLSTM-MIDAS can outperform benchmark models, which is also confirmed during the COVID-19 pandemic period; (2) Compared with the MIDAS model, establishing the nonlinear relationship between high frequency Baidu search volumes, low frequency tourist arrivals and dummy variables using BiLSTM-MIDAS can improve the roles of high-frequency search engines in forecasting tourism demand. This study represents the first attempt to apply machine learning methods for tourism demand forecasting with mixed-frequency data.

Opioid-related deaths in Northern Ontario in the early COVID-19 pandemic period.

In the first year of pandemic measures, opioid-related deaths across Ontario's (ON) 34 public health units (PHUs) increased by 60%. Death rates for all seven Northern ON PHUs were above the provincial average. This study describes and compares factors surrounding opioid-related deaths before and after pandemic measures were introduced, for Northern ON compared to the rest of ON. Aggregate data were provided for Northern ON and the rest of the province by the Office of the Chief Coroner/Ontario Forensic Pathology Services. Opioid-related deaths were cohorted by date of death for the year before and after pandemic measures were introduced on March 16, 2020. Chi-square tests were used to compare between cohorts and geographies to determine significant differences for each variable, and for dichotomized levels within variables. P-values < 0.05 were considered statistically significant a priori. In Northern ON, the number of opioid-related deaths approximately doubled from the pre-pandemic cohort (n = 185) to the early pandemic cohort (n = 365). Compared to the rest of ON, higher proportions of deaths occurred in Northern ON among individuals who lived and died in private residences, among women (although the majority of decedents were male) and among individuals employed in mining, quarrying, and oil and gas industries. Compared to the pre-pandemic year, in Northern ON, higher proportions of opioid-related deaths involved fentanyl and stimulants as direct contributors, and the majority involved evidence of inhaled drugs. Differences between the circumstances of death in Northern ON andin the rest of ON suggest opportunities to tailor interventions.

Trends and cycles during the COVID-19 pandemic period

We perform several trend-cycle decompositions through the lens of two unobserved components models, herein estimated for Portugal and the euro area. Our procedure copes with the COVID-19’s consequences by explicitly considering potentially larger second moments during that period. This is achieved through a set of pandemic-specific shocks affecting only the 2020–21 period and embedded into estimation through a piecewise linear Kalman filter. Our methodology generates negligible historical revisions in key smoothed variables when the sample period is expanded until 2021:4, since pandemic shocks absorb a great deal of data volatility with minimal impacts on filtered data revisions or estimated parameters. Furthermore, non-pandemic shock volatility remains largely unaffected by the pandemic period. Innovations affecting the cycle in our preferred model are the key propellers of GDP developments during the COVID-19 pandemic period.