COVID-19 Death Cases Research Articles

COVID-19 premature mortality in Serbia: Does the place of death matter?

Abstract Background The COVID-19 pandemic has significantly impacted healthcare services provision in Serbia and globally. Many people did not seek or reach healthcare due to various reasons, and a significant number of COVID-19 deaths were not confirmed by laboratory testing. Within the project of the Laboratory for Strengthening Capacity and Performance of Health System and Workforce for Health Equity, we assessed the differences in years of life lost (YLL) of COVID-19 death cases according to the place of death. Methods The Statistical Office of the Republic of Serbia provided average life expectancy and anonymous individual mortality data of the Serbian population in 2020, 2021, and 2022, where COVID-19 was the underlying cause of death (ICD-X codes: laboratory confirmed - U07.1 and clinically diagnosed - U07.2), also disaggregated by the place of death (at home or in a hospital). Analytical statistics and nonparametric tests were used to assess the differences in premature mortality by place of death with a significance level set at p < 0.05. Results In all observed years, a total of 45,632 death cases due to COVID-19 occurred (94.3% in hospital and 5.7% at home). Most COVID-19 deaths were coded as U07.1 (93.8% in hospital, 91.3% at home). An average of 12.12±7.50 YLLs was estimated per death, with a median of 11.50. There was a significant difference (p < 0.001) in the median number of YLLs between those who died in the hospital (11.50) and those who died at home (6.78). Furthermore, deaths occurring at home had fewer YLLs but were more frequently coded as U07.2 (p < 0.001). Conclusions The place of death is relevant when assessing the impact of COVID-19 on premature mortality in Serbia. On average, persons who died at home due to COVID-19 had a lower number of YLLs compared to those who died in a hospital. More research is necessary on the other factors contributing to premature mortality and inequalities in the COVID-19 burden in Serbia. Key messages • During the first three years of the COVID-19 pandemic in Serbia, 5.7% of COVID-19 deaths occurred at home, resulting in an average of 9.89 years of life lost. • The COVID-19 pandemic highlights the need to investigate deaths that occur at home and improve pandemic preparedness.

Assessment of Nutritional status, Anthropometry and Sleep patterns of the Obese patients during pre and post COVID-19 Illness

Background and aimsUntil COVID-19, obesity was the main pandemic of 21 century. To date, there have been 767984989 confirmed cases, and 6943390 include death cases of COVID-19 (WHO). To find the prevalence of Obesity among adults, to adjudge the attitude and behavior of selected subjects towards eating healthy and weight loss during pre- and post-COVID-19. MethodologySubject selection was done through non-probability judgemental sampling. Initially, 70 young adults between 18 - 39 years of age were selected from different localities of Delhi (NCR). The study was conducted based on a questionnaire developed for the survey. Based on the inclusion criteria only 50 subjects were selected for further study. The study was constructed into three phases and the arrangement of subjects was elaborated as under. Phase 1 includes field study, clinical examination, and biochemical analysis, phase II includes the division of experimental and control groups, whereas Phase III includes the pre& post-COVID nutritional status, anthropometric assessment, beverage consumption, and sleep patterns were also assessed. ResultThe nutrition intervention in E3 (balanced diet + nutrient supplement) showed a significant difference with the control group followed by E2 (nutrient supplement) and E1 (balanced diet). Our findings showed that the subjects have Calorie, protein, fat, carbohydrates, sodium, and potassium intake significantly decreased (P= <0.001) during post-intervention, whereas, the fiber, vitamin A, vitamin C, and iron (P= <0.001), and calcium (p=0.006), intake significantly increased during post-intervention. The changes in anthropometric parameters such as waist-to-hip ratio (WHR), body mass index (BMI), and fat% for pre-COVID-19 were higher for both men and women, but after following the dietary recommendation and physical activity found a significant reduction in WHR, BMI, and fat% (P= <0.001) for both men and women. The p-value for subjective sleep quality, sleep latency, habitual sleep efficiency, sleep disturbances, and daytime dysfunction appears to be less than 0.05 which shows that it is significant. The daily consumption of various beverages listed above with a comparison of pre-post intervention, which shows that only for kada the p-value is 0.001 which is less than 0.05 hence it seems significant. The p-value for people with no comorbidity appears less than 0.05, indicating it is significant. majority of participants (12%) with diabetes took comparatively longer than the participants suffering from other comorbidities followed by Hypertension (7%), Hyperthyroidism (4%), Hypothyroidism, and polycystic ovarian disease (PCOD) (3% each) respectively. ConclusionIn the present study, we provided survey data about the key aspects of the nutritional management of COVID-19 (pre-and-post), based on the current knowledge.

Characteristics and comorbidity of confirmed COVID-19 death cases at the Arifin Achmad Hospital in Riau province from March 2020 to September 2021.

To investigate the characteristics and comorbidity among those who died due to coronavirus disease-2019. The cohort retrospective study was conducted at Arifin Achmad Public Hospital, Riau, Indonesia, from January 5 to February 28, 2022, and comprised data of all coronavirus disease-2019 patients who had been treated at the hospital from March 2020 to October 2021. Data was analysed using SPSS version 20. Logistic regression including univariate and bivariate analysis was applied. Of the 1,694 patients, 916(54.1%) were females and 904(53.4%) were aged >50 years. The most frequent comorbidity was type 2 diabetes mellitus 280(16.5%), followed by hypertension 254(14.9%) and chronic renal failure 194(11.4%). Mortality was significantly higher among those aged >50 years and those having diabetes mellitus and hypertension (p<0.05). Patients with comorbidities were at a greater risk of acquiring coronavirus disease-2019 infection.

INAR(1) process with weighted negative binomial Lindley distributed innovations and applications to criminal and COVID-19 data

In this study, we introduce a pliant stationary first-order integer-valued autoregressive (INAR) process with weighted negative binomial Lindley innovations. The main properties of the model are derived. The methods of conditional maximum likelihood, conditional least square and Yule-Walker are used for estimating the process parameters, while the efficiency of these three methods is evaluated through a simulation study. Finally, the practical aspect of the proposed INAR(1) process is discussed on two time series of the monthly number of criminal mischief reports in Pittsburgh and the daily COVID-19 death cases in Paraguay.

Logic mining with hybridized 3-satisfiability fuzzy logic and harmony search algorithm in Hopfield neural network for Covid-19 death cases

&lt;abstract&gt; &lt;p&gt;Since the beginning of the Covid-19 infections in December 2019, the virus has emerged as the most lethally contagious in world history. In this study, the Hopfield neural network and logic mining technique merged to extract data from a model to provide insight into the link between factors influencing the Covid-19 datasets. The suggested technique uses a 3-satisfiability-based reverse analysis (3SATRA) and a hybridized Hopfield neural network to identify the relationships relating to the variables in a set of Covid-19 data. The list of data is to identify the relationships between the key characteristics that lead to a more prolonged time of death of the patients. The learning phase of the hybridized 3-satisfiability (3SAT) Hopfield neural network and the reverse analysis (RA) method has been optimized using a new method of fuzzy logic and two metaheuristic algorithms: Genetic and harmony search algorithms. The performance assessment metrics, such as energy analysis, error analysis, computational time, and accuracy, were computed at the end of the algorithms. The multiple performance metrics demonstrated that the 3SATRA with the fuzzy logic metaheuristic algorithm model outperforms other logic mining models. Furthermore, the experimental findings have demonstrated that the best-induced logic identifies important variables to detect critical patients that need more attention. In conclusion, the results validate the efficiency of the suggested approach, which occurs from the fact that the new version has a positive effect.&lt;/p&gt; &lt;/abstract&gt;

Board of directors, COVID-19, and corporate social responsibility monetary performance: direct and interaction effects analysis

Purpose This study aims to provide archival evidence on the impact of board characteristics on corporate social responsibility (CSR) monetary performance and how they interact with the COVID-19 pandemic in the context of CSR monetary performance. Design/methodology/approach This study analyzes listed companies in Oman’s capital market from 2016 to 2021, using pooled ordinary least squares and unique CSR performance measures such as budgeting and spending. Findings The study finds that companies with more expertise and frequent meetings are more likely to allocate a larger budget for CSR activities. However, this does not apply to larger boards or to independent directors. During the COVID-19 pandemic, the effect of independent directors on CSR budgeting and spending is more pronounced, and boards with more expertise and meetings show a negative interaction with the pandemic. The interaction of board characteristics with COVID-19 in terms of CSR monetary performance varies depending on company size. Board independence and expertise show a significant reaction to COVID-19 infection and death cases when setting CSR budgeting and spending. Research limitations/implications The findings of this study are stimulating, but stem from an emerging country with unique cultural and institutional characteristics. Methodological issues were also encountered during the analysis, so readers should exercise caution when applying the results to other settings. Practical implications This study highlights board involvement in deciding a company’s CSR investment, as it was believed that chief executive officers are considered responsible for CSR activities. Additionally, this research underscores the significance of incorporating the financial aspects of CSR into reporting. Originality/value This study examines the seldom explored relationship between corporate boards and CSR monetary aspects during regular and irregular times, offering theoretical and practical insights that benefit multiple stakeholders.

Bagaimana Tingkat Kesejahteraan Masyarakat, Pendidikan, dan Layanan Kesehatan Berdampak terhadap Penyebaran Kasus Positif dan Kematian Covid-19 di Jawa Bali?

Many factors cause the distribution of positive cases and deaths of Covid-19 in Indonesia. This study aims to determine the effect of welfare, Education, and health services on positive cases and fatalities of COVID-19 from 128 regencies and municipalities in Java and Bali islands. Data were collected from BPS and kawalcovid19.id. This study applied the Ordinary Least Squared method in two models. The variables are converted into Natural Logarithmic. The results of the regression of Model I show a high determination coefficient value of 0.718 with the value of the Regional GDP income per capita of (-0.389), Regional GDP (0.739), and Education (2.139) affect the distribution of the positive cases of Covid-19, except variable of Poverty (0.045) whose results are not significant. Meanwhile, Model II shows a regression result of Positive Covid-19 cases (0.999), Regional GDP income per capita (1.052), Regional GDP (-0.955) can affect the death cases of Covid-19 (0.151), Education (0.-431), and Health (0.-165) with nonsignificant results. Moreover, in this Model, the independent variable can affect the spread of death cases of Covid-19 by 59.3%.

Potential drug-drug interactions in a multi-center study of death cases of COVID-19: The significance of appropriate drug choice.

The prevalence, types, severity, risk ratings, and common pairs of involved drugs, and the most important potential drug-drug interactions (pDDIs) in coronavirus disease 2019 (-COVID-19) deceased cases were evaluated. We reviewed the medical records of 157 confirmed COVID-19 deceased cases hospitalized in 27 province-wide hospitals. Patients' demographics and clinical data (including comorbidities, vital signs, length of in-hospital survival, electrocardiograms (ECGs), medications, and lab test results) were extracted. The online Lexi-interact database and Stockley's drug interactions reference were used to detect pDDIs retrospectively. The QTc interval and total Tisdale risk score were also calculated. Descriptive analysis, analysis of variance, Fisher exact test, and multivariate analysis were conducted for data analysis. Of 157 study cases, 63% were male, had a mean age of 68 years, and 55.7% had one or more underlying diseases. All patients had polypharmacy, with 69.2% having ≥15 drugs/day. We detected 2,416 pDDIs in patients' records, of which 658 (27.2%) were interactions with COVID drugs. Lopinavir/ritonavir among -COVID drugs and fentanyl among non-COVID drugs were commonly involved in the interactions. pDDIs was significantly higher in the polypharmacy group of ≥15 medications (p<0.001). A majority (83%) had received drug(s) with the QTc prolongation effect, of whom 67% had actual QTc prolongations in their ECGs. The regression analysis showed that by increasing 6.7% in polypharmacy, one day increase in-hospital survival can be expected. Moreover, an increase of 2.3% in white blood cells or 10.5% in serum potassium level decreased in-hospital survival by 1%. The findings underscored the importance of careful drug choice, especially in the hectic search for early treatments in pandemics of novel diseases. Close monitoring of patients' drug choice is warranted for reducing pDDIs and their adverse effects in any new disease outbreak.

THE IMPACT OF COVID-19 OUTBREAKS ON INDONESIA’S AUTOMOTIVE AND PAPER INDUSTRY STOCK RETURN

This study was carried out to identify the effect of COVID-19, Large-Scale Social Restriction policies, market capitalization, and stock trading volume on the company's stock returns from 2 March 2020 to 30 December 2020. The subjects in this study are companies in the automotive and component industries as well as the pulp and paper industry which are listed on the Indonesia Stock Exchange (IDX) during 2020. This study uses 3 theories, namely the Signaling Theory, Efficient Market Hypothesis Theory, and Black Swan Theory. Panel data regression analysis was used in the study, using EViews 9 program and Microsoft Excel to process all data. Results show that growth of COVID-19 positive cases and market capitalization have a significant negative effect on the company's stock return. The other results show that the increasing of LSSR (Large-Scale Social Restriction) policies and stock trading volumes have an impact on increasing company’s stock returns, while the growth of death cases of COVID-19 has no impact on increasing or decreasing company’s stock returns.

Factors associated with child marriage during the COVID-19 pandemic in Indonesia

AbstractThe aim of this study is to investigate factors associated with the applications of child marriage dispensation submitted to the Religious Courts during the COVID-19 pandemic in Indonesia, particularly in 2019–2021. The number of child marriage dispensation cases was analyzed together with other socioeconomic variables, including poverty and jobless rates, junior and senior high school completion rates, number of cerai talak (a divorce initiated by the husband) and cerai gugat (a divorce initiated by the wife) applications, number of COVID-19 positive cases, and number of deaths by COVID-19. The differences between 2021 and 2019 were analyzed using the paired t-test and repeated measures analysis of variance (ANOVA). The relationship between variables was analyzed using the Pearson product-moment correlation coefficient. Hierarchical multiple regression with the entering method was used to determine the predictors of the number of child marriage dispensation cases in the years 2019, 2020, and 2021 and the change from 2019 to 2021. New dispensation underage marriage applications in 2019, 2020, and 2021 were 24,457 (M =843, SD =1519), 63,391 (M = 2186, SD = 4085), and 62,167 (M = 2144, SD = 4058), which were significantly different (F(1.0, 28.6) = 7.63, p = .01) and positively correlated with poverty rate, jobless rate, cerai talak, cerai gugat, COVID-19 positive cases, and COVID-19 death cases. The multiple regression analysis for the change of dispensation underage marriage applications in the pandemic and before (years 2021–2019) was statistically significant, (F(6,22) = 117.62, p &lt; .001), which the final model was able to account for 97% of the variance in new dispensation underage marriage applications. Collaborative multi-sector works are needed to decrease the skyrocketing child marriage numbers, especially in a crisis situation like the COVID-19 pandemic.

Mapping Indonesia's Covid-19 Death Case with Comorbidities Using Correspondence Analysis

This research aims to map and identify COVID-19 deaths with comorbidities in Indonesia using correspondence analysis. The data collection technique involved the analysis of 6231 samples of COVID-19 death cases with comorbidities in Indonesia from the official website of the COVID‑19 Response Acceleration Task Force. The variables used were the number of COVID-19 deaths with comorbid hypertension, diabetes mellitus, cardiovascular disease, chronic obstructive pulmonary disease, kidney disease, immune disorders, liver disease, cancer, asthma, pregnancy, tuberculosis, and other respiratory disorders. The findings from this study divide four groups of provinces with characteristics: Group One with the characteristics of COVID-19 death cases with comorbid hypertension, diabetes mellitus, heart disease, kidney disease, lung disease, immune disorders, and cancer; Group Two with the characteristics of COVID-19 death cases with comorbid pregnancy, liver disease, and tuberculosis; Group Three with the characteristics of COVID-19 death cases with comorbid asthma; and Group Four with the characteristics of COVID-19 death cases with other comorbid respiratory disorders.

Sociodemographic features of COVID-19 premature mortality: Serbian individual-level data evidence

Abstract Background In Serbia, premature mortality due to COVID-19 usually has been assessed using aggregated mortality data, thus overlooking the impact of sociodemographic factors. Within the European Burden of Disease Network (COST Action CA18218), we examined the association between several sociodemographic factors and COVID-19 years of life lost (YLL) to reveal potential social inequalities. Methods The Statistical Office of the Republic of Serbia (SORS) has provided disaggregated, anonymized, and individual data on COVID-19 deaths in 2020 and 2021 from death certificates having U07.1 and U07.2 ICD codes as the main cause of death. Additionally, SORS remaining life expectancy and population size estimates were used along with sociodemographic data for each YLL case (sex, time and place of death, type of residential settlement, educational attainment, marital status, and economic activity). Results In the observed two-year period, 38,098 COVID-19 death cases were assessed, of which 72.8% were registered in 2021. The average YLL per death case was 12.51±7.62, and the median YLL was 11.50 (12.60±7.44 for men and 12.39±7.85 for women, p &amp;lt; 0.006). The multivariate linear regression indicated a significant association between the number of YLL and sex (B = 1.02, p &amp;lt; 0.01), residential area (B = 0.21, p &amp;lt; 0.01), educational attainment (B = 0.84, p &amp;lt; 0.01), marital status (B = 1.65, p &amp;lt; 0.01), employment (B=-7.02, p &amp;lt; 0.01) and place of death (B=-1.76, p &amp;lt; 0.01). Conclusions This study demonstrates social inequalities in COVID-19 premature mortality associated with the highest YLL among men, single persons, retired persons, and those with higher education status. We identify significant differences in YLL when comparing different regions. Key messages • Individual-level data reveals differences in premature mortality aggravated by social disparities. • Policymakers should consider sociodemographic factors when designing COVID-19 prevention measures.

Bayesian Analysis for the Modified Frechet–Exponential Distribution with Covid-19 Application

In this manuscript, the maximum likelihood estimators and Bayes estimators for the parameters of the modified Frechet–exponential distribution. Because the Bayes estimators cannot be obtained in closed forms, the approximate Bayes estimators are computed using the idea of Lindley’s approximation method under squared-error loss function. Then, the approximate Bayes estimates are compared with the maximum likelihood estimates in terms of mean square error and bias values using Monte Carlo simulation. Finally, real data sets belonging to COVID-19 death cases in Europe and China to are used to demonstrate the emprical results belonging to the approximate Bayes estimates, the maximum likelihood estimates.

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Lassa fever (LF) is endemic in West Africa and Nigeria in particular. Since 1969 when the disease was discovered, a seasonal outbreak is often reported in Nigeria. Many researchers have reported inconsistent or varying numbers of suspected, confirmed and death cases since 2012 to date. To enhance this reportage, and due to the high mortality rate associated with LF, it is pertinent to design a suitable and robust model that could predict or estimate the number of LF cases based on the onset data. To achieve these, we proposed a recursive prediction (RP) model that could do predictions with the onset data. The Pearson correlation coefficient (R) and R2 are applied to determine the performance analysis of the model. The RP model predicted 96.7% confirmed cases and 89.6% death cases for the first three months of 2022 based on the onset data. The model was also applied to predict COVID-19 death cases during the six weeks of the outbreak in India. The result showed a comparable prediction with the regression output for the COVID-19 death cases. This study demonstrated that the proposed model could be applied to perform prediction for any disease of unknown etiology during the onset of the disease outbreak without any treatment similar to the COVID-19 outbreak. The performance analysis of the RP showed that the model is useful to predict the increasing trend of an outbreak of a disease with unknown etiology without prior treatment experience and vaccines.

Analysis of COVID-19 Death Cases Using Machine Learning.

COVID-19 has threatened the existence of human life for more than the last 2 years. More than 460 million confirmed cases and 6 million deaths have been reported worldwide due to COVID-19. To measure the severity of the COVID-19, the mortality rate plays an important role. Understanding the nature of COVID-19 and forecasting the death cases of COVID-19 require more investigation of the real effect for different risk factors. In this work, various regression machine learning models are proposed to extract the relationship between different factors and the death rate of COVID-19. The optimal regression tree algorithm employed in this work estimates the impact of essential causal variables that significantly affect the mortality rates. We have generated a real-time forecast for the death case of COVID-19 using machine learning techniques. The analysis is evaluated with the well-known regression models XGBoost, Random Forest, and SVM on the data sets of the US, India, Italy, and three continents Asia, Europe, and North America. The results show that the models can be used to forecast the death cases for the near future in case of an epidemic like Novel Coronavirus.

An assessment of the relationship between national rates of Covid-19 incidence and mortality as reported to an international comparison database: An ecological study.

Making a judgment only based on formal national reports can be misleading. We aimed to assess the relationship between countries' development indicators and reported coronavirus disease 2019 (Covid-19)-related incidences and death. Covid-19 related incidence and death cases were extracted from the updated Humanitarian Data Exchange Website on October 8, 2021. Univariable and multivariable negative binomial regression were utilized to investigate the relationship between development indicator and incidence and mortality from Covid-19 by calculating the Incidence rate ratio (IRR), mortality rate ratio (MRR), and fatality risk ratio (FRR). Very high human development index (HDI) compared with low HDI (IRR:3.56; MRR:9.04), the proportion of physicians (IRR:1.20; MRR:1.16), besides extreme poverty (IRR:1.01; MRR:1.01) were independently correlated with the mortality and incidence rate of Covid-19. Very high HDI and population density were inversely correlated with the fatality risk (FRRs of 0.54 and 0.99). The cross-continental comparison shows Europe and the North Americas, had significantly higher incidence and mortality rates with IRR of 3.56 and 1.84 as well as MRRs of 6.65 and 3.62, respectively. Also, they inversely correlated with the fatality (FRR:0.84 and 0.91, respectively). A positive correlation between the fatality rate ratio based on countries' development indicators and the reverse for the incidence and mortality rate was found. Developed countries with sensitive healthcare systems can diagnose infected cases as soon as possible. Also, the mortality rate of Covid-19 will be accurately registered and reported. Due to more access to diagnostic tests, patients are diagnosed at the initial stages and will have a better opportunity to receive treatment. This leads to higher reports of incidence/and/or mortality rates and lower fatality of COVID-19. In conclusion, more Covid-19 incidence and mortality cases in developed countries can result from a more comprehensive care system and a more accurate recording procedure.

Peculiar weather patterns effects on air pollution and COVID-19 spread in Tokyo metropolis

As a pandemic hotspot in Japan, between March 1, 2020–October 1, 2022, Tokyo metropolis experienced seven COVID-19 waves. Motivated by the high rate of COVID-19 incidence and mortality during the seventh wave, and environmental/health challenges we conducted a time-series analysis to investigate the long-term interaction of air quality and climate variability with viral pandemic in Tokyo. Through daily time series geospatial and observational air pollution/climate data, and COVID-19 incidence and death cases, this study compared the environmental conditions during COVID-19 multiwaves. In spite of five State of Emergency (SOEs) restrictions associated with COVID-19 pandemic, during (2020–2022) period air quality recorded low improvements relative to (2015–2019) average annual values, namely: Aerosol Optical Depth increased by 9.13% in 2020 year, and declined by 6.64% in 2021, and 12.03% in 2022; particulate matter PM2.5 and PM10 decreased during 2020, 2021, and 2022 years by 10.22%, 62.26%, 0.39%, and respectively by 4.42%, 3.95%, 5.76%. For (2021–2022) period the average ratio of PM2.5/PM10 was (0.319 ± 0.1640), showing a higher contribution to aerosol loading of traffic-related coarse particles in comparison with fine particles. The highest rates of the daily recorded COVID-19 incidence and death cases in Tokyo during the seventh COVID-19 wave (1 July 2022–1 October 2022) may be attributed to accumulation near the ground of high levels of air pollutants and viral pathogens due to: 1) peculiar persistent atmospheric anticyclonic circulation with strong positive anomalies of geopotential height at 500 hPa; 2) lower levels of Planetary Boundary Layer (PBL) heights; 3) high daily maximum air temperature and land surface temperature due to the prolonged heat waves (HWs) in summer 2022; 4) no imposed restrictions. Such findings can guide public decision-makers to design proper strategies to curb pandemics under persistent stable anticyclonic weather conditions and summer HWs in large metropolitan areas.

Modelling the volatility of African capital markets in the presence of the Covid-19 pandemic: evidence from five emerging economies in Africa

The growing concern over the global effects of the COVID-19 pandemic on every aspect of human endeavour has necessitated a continuous modelling of its impact on socio-economic phenomena, allowing the formulation of policies aimed at sustaining future economic growth and mitigating the looming recession. The study employed Exponential Generalised Autoregressive Conditional Heteroscedasticity (EGARCH) procedures to develop stock volatility models for the pre- and COVID-19 era. The Fixed-Effects Two Stage Least Square (TSLS) technique was utilised to establish an empirical relationship between capital market volatility and the COVID-19 occurrence based on equity market indices and COVID-19 reported cases of five emerging African economies: Nigeria, Egypt, South Africa, Gabon and Tanzania. The stock series was made stationary at the first order differencing and the model results indicated that the stock volatility of all the countries responded sharply to the outbreak of COVID-19 with the average stock returns of Nigeria and Gabon suffering the most shocks. The forecast values indicated a constant trend of volatility shocks for all the countries in the continuous presence of the COVID-19 pandemic. Additionally, the confirmed and death cases of COVID-19 were found to increase stock prices while recovered cases bring about a reduction in the stock prices in the studied periods.

Assessing the impact of the russian war in Ukraine on COVID-19 transmission in Spain: a machine learning-based study

COVID-19 pandemic has significantly impacted the world, with millions of infections and deaths, healthcare systems overwhelmed, economies disrupted, and daily life changed. Simulation has been recognized as a valuable tool in combating the pandemic, helping to model the spread of the virus, evaluate the impact of interventions, and inform decision-making processes. The accuracy and effectiveness of simulations depend on the quality of the underlying data, assumptions, and modeling techniques. Ongoing efforts to improve and refine simulation approaches can enhance their value in addressing future public health emergencies. The Russian full-scale military invasion of Ukraine on February 24, 2022, has created a significant humanitarian and public health crisis, with disrupted healthcare services, shortages of medical supplies, and increased demand for emergency care. The ongoing conflict has displaced millions of people, with Spain ranking 5th in the world for the number of registered refugees from Ukraine. The research aims to estimate the impact of the Russian war in Ukraine on COVID-19 transmission in Spain using means of machine learning. The research is targeted at COVID-19 epidemic process during the war. The research subjects are methods and models of epidemic process simulation based on machine learning. To achieve the study's aim, we used forecasting methods and built a model of COVID-19 epidemic process based on the XGBoost method. As a result of the experiments, the accuracy of forecasting new cases of COVID-19 in Spain for 30 days was 99.79 %, and the death cases of COVID-19 in Spain – were 99.86 %. The model was applied to data on the incidence of COVID-19 in Spain for the first 30 days of the war escalation (24.02.2022 – 25.03.2022). The calculated forecasted values showed that the forced migration of the Ukrainian population to Spain, caused by the full-scale invasion of Russia, is not a decisive factor affecting the dynamics of the epidemic process of COVID-19 in Spain. Conclusions. The paper describes the results of an experimental study assessing the impact of the Russian full-scale war in Ukraine on COVID-19 dynamics in Spain. The developed model showed good performance to use it in public health practice. The analysis of the obtained results of the experimental study showed that the forced migration of the Ukrainian population to Spain, caused by the full-scale invasion of Russia, is not a decisive factor affecting the dynamics of the epidemic process of COVID-19 in Spain.

Impact of COVID-19 on online grocery shopping discussion and behavior reflected from Google Trends and geotagged tweets

People express opinions, make connections, and disseminate information on social media platforms. We considered grocery-related tweets as a proxy for grocery shopping behaviors or intentions. We collected data from January 2019 to January 2022, representing three typical times of the normal period before the COVID-19 pandemic, the outbreak period, and the widespread period. We obtained grocery-related geotagged tweets using a search term index based on the top 10 grocery chains in the US and compiled Google Trends online grocery shopping data. We performed a topic modeling analysis using the Latent Dirichlet Allocation (LDA), and verified that most of the collected tweets were related to grocery-shopping demands or experiences. Temporal and geographical analyses were applied to investigate when and where people talked more about groceries, and how COVID-19 affected them. The results show that the pandemic has been gradually changing people’s daily shopping concerns and behaviors, which have become more spread throughout the week since the pandemic began. Under the causal impact of COVID-19, people first experienced panic buying groceries followed by pandemic fatigue a year later. The normalized tweet counts show a decrease of 40% since the pandemic began, and the negative causal effect can be considered statistically significant (p-value = 0.001). The variation in the quantity of grocery-related tweets also reflects geographic diversity in grocery concerns. We found that people in non-farm areas with less population and relatively lower levels of educational attainment tend to act more sensitively to the evolution of the pandemic. Utilizing the COVID-19 death cases and consumer price index (CPI) for food at home as background information, we proposed an understanding of the pandemic’s impact on online grocery shopping by assembling, geovisualizing, and analyzing the evolution of online grocery behaviors and discussion on social media before and during the pandemic.