Number Of COVID-19 Cases Research Articles

Anti-Inflammatory Cytokine (IL-10) Profiles and Ratio of IL-6/IL-10 in Covid-19 Patients

Background: The number of COVID-19 cases in Indonesia has continually increased since the first cases appeared in March 2020. This disease is due to SARS-CoV-2 virus infection in the respiratory system that induces an immune response. The innate and adaptive immune response triggered the secretion of an excessive pro-inflammatory cytokine-caused cytokine storm that became one of the mechanisms of acute respiratory distress (ARDS). The anti-inflammatory cytokines (IL-10, IL-13, and IL-4) were secreted as the immune response in the ARDS condition. Purposes: This study aims to determine the ratio of the IL-6/IL-10 profile as basic information for the therapeutic approach to prevent ARDS. Methods: This cross-sectional study used stored biological material in plasma form from COVID-19 patients in Jakarta Islamic Hospital – Pondok Kopi and Dr. M. Goenawan Partowidigdo Hospital, Cisarua. The plasmas were from severe (n=20) patients and mild to moderate severity (n=25). The negative control sample was collected from 13 healthy subjects. Assessment of IL-10 levels in plasma using ELISA technique. Results: Our analysis showed that IL-10 has no statistical difference between negative control, mild to moderate, and severe categories (p=0.629). Meanwhile, the ratio IL-6/IL-10 presented statistical differences between mild to moderate and severe categories (p=0.011). The average ratio of IL-6/IL-10 in severe categories is two-fold higher than in mild-moderate categories. Conclusion: We conclude that there is a cytokine storm condition in severe COVID-19 patients with an imbalance ratio of pro-and anti-inflammatory cytokines and could be used as basic information for drug development in cytokine storm conditions to prevent ARDS.

Reinfections of COVID-19 with and Without Fourth Dose of Bivalent mRNA Vaccine in the Period from October 2022 to October 2023 in a General Medicine Office in Toledo (Spain)

Background: Clinical-epidemiological differences between cases of covid-19 reinfections in people vaccinated with the 4th dose of bivalent mRNA versus cases of covid-19 reinfections in people not vaccinated with the 4th dose of bivalent mRNA are unknown. Objective: Compare the cases of covid-19 reinfections in people vaccinated with the 4th dose with bivalent mRNA vaccines versus cases of covid-19 reinfection in people not vaccinated with the 4th dose. Methodology: An observational, longitudinal and prospective case series study of adult patients with covid-19 reinfections in general medicine from October 1, 2022 to October 1, 2023. Results: 12 covid-19 reinfections were included in the period from October 2022 to October 2023. Of them, 5 were in people with fourth vaccine dose of bivalent mRNA and 7 in people without fourth vaccine dose of bivalent mRNA. There were no statistically significant differences by sex, age, psychosocial factors, severity of infection, symptoms, or in the comparison of chronic diseases by groups. A statistically significant difference was only found in the total presence of chronic diseases: 100% in covid-19 reinfections with fourth vaccine dose of bivalent mRNA versus 29% in covid-19 reinfections without fourth vaccine dose of bivalent mRNA. Conclusion: In the context of general medicine in Toledo (Spain), reinfections are rare, but the presence of chronic diseases represents a significant risk factor for covid-19 reinfections even if these people have fourth vaccine dose of bivalent mRNA. However, number of reinfections was possibly underestimated. The results should be taken with caution due to the small number of covid-19 cases included.

Time-varying effects of the COVID-19 pandemic on stock markets and economic activity: evidence from the US and Europe

This paper examines the effects of the COVID-19 pandemic on CDS, stock returns, and economic activity in the US and the five European countries that have been most affected: the UK, Germany, France, Italy, and Spain. The sample period covers the period from 11 March 2020 to 19 February 2021. In the empirical analysis, first, we estimate benchmark linear VAR models and then, given the evidence of parameter instability, TVP-VAR models with stochastic volatility, which are ideally suited to capturing the changing dynamics in both financial markets and the real economy. The linear VAR responses of CDS to the number of COVID-19 cases are positive and statistically significant, whilst those of electricity consumption are insignificant and those of stock returns vary across countries in terms of their sign and significance. The results from the TVP-VAR analysis indicate that the effects of shocks on the system variables was more pronounced during the initial stages of the pandemic and then decreased in the following months. Specifically, there was a positive impact of the number of COVID-19 cases on CDS and a negative one on stock returns and economic activity, the latter two being interlinked.

Effects of primary health care and socioeconomic aspects on the dispersion of COVID-19 in the Brazilian Northeast: Ecological study of the first pandemic wave.

The COVID-19 pandemic has had a negative impact on socioeconomic and public health conditions of the population. To measure the temporal evolution of COVID-19 cases in cities near the countryside outside metropolitan areas of northeastern Brazil and the impact of the primary care organization in its containment. This is a time-series study, based on the first three months of COVID-19 incidence in northeastern Brazil. Secondary data were used, the outcome was number of COVID-19 cases. Independent variables were time, coverage and quality score of basic health services, and demographic, socioeconomic and social isolation variables. Generalizable Linear Models with first order autoregression were applied. COVID-19 spreads heterogeneously in cities near the countryside of Northeastern Brazilian cities, showing associations with the city size, socioeconomic and organizational indicators of services. The Family Health Strategy seems to mitigate the speed of progression and burden of the disease, in addition to measures such as social isolation and closure of commercial activities. The spread of COVID-19 reveals multiple related factors, which require coordinated intersectoral actions in order to mitigate its problems, especially in biologically and socially vulnerable populations.

Influence of Environmental Factors and Genome Diversity on Cumulative COVID-19 Cases in the Highland Region of China: Comparative Correlational Study.

The novel coronavirus SARS-CoV-2 caused the global COVID-19 pandemic. Emerging reports support lower mortality and reduced case numbers in highland areas; however, comparative studies on the cumulative impact of environmental factors and viral genetic diversity on COVID-19 infection rates have not been performed to date. The aims of this study were to determine the difference in COVID-19 infection rates between high and low altitudes, and to explore whether the difference in the pandemic trend in the high-altitude region of China compared to that of the lowlands is influenced by environmental factors, population density, and biological mechanisms. We examined the correlation between population density and COVID-19 cases through linear regression. A zero-shot model was applied to identify possible factors correlated to COVID-19 infection. We further analyzed the correlation of meteorological and air quality factors with infection cases using the Spearman correlation coefficient. Mixed-effects multiple linear regression was applied to evaluate the associations between selected factors and COVID-19 cases adjusting for covariates. Lastly, the relationship between environmental factors and mutation frequency was evaluated using the same correlation techniques mentioned above. Among the 24,826 confirmed COVID-19 cases reported from 40 cities in China from January 23, 2020, to July 7, 2022, 98.4% (n=24,430) were found in the lowlands. Population density was positively correlated with COVID-19 cases in all regions (ρ=0.641, P=.003). In high-altitude areas, the number of COVID-19 cases was negatively associated with temperature, sunlight hours, and UV index (P=.003, P=.001, and P=.009, respectively) and was positively associated with wind speed (ρ=0.388, P<.001), whereas no correlation was found between meteorological factors and COVID-19 cases in the lowlands. After controlling for covariates, the mixed-effects model also showed positive associations of fine particulate matter (PM2.5) and carbon monoxide (CO) with COVID-19 cases (P=.002 and P<.001, respectively). Sequence variant analysis showed lower genetic diversity among nucleotides for each SARS-CoV-2 genome (P<.001) and three open reading frames (P<.001) in high altitudes compared to 300 sequences analyzed from low altitudes. Moreover, the frequencies of 44 nonsynonymous mutations and 32 synonymous mutations were significantly different between the high- and low-altitude groups (P<.001, mutation frequency>0.1). Key nonsynonymous mutations showed positive correlations with altitude, wind speed, and air pressure and showed negative correlations with temperature, UV index, and sunlight hours. By comparison with the lowlands, the number of confirmed COVID-19 cases was substantially lower in high-altitude regions of China, and the population density, temperature, sunlight hours, UV index, wind speed, PM2.5, and CO influenced the cumulative pandemic trend in the highlands. The identified influence of environmental factors on SARS-CoV-2 sequence variants adds knowledge of the impact of altitude on COVID-19 infection, offering novel suggestions for preventive intervention.

Territorial patterns of COVID-19 in Iran

Motives: Spatial analysis has become an essential tool in understanding the underlying factors that contribute to the distribution of viral pandemics, diseases, injuries, and mortality patterns. By visualizing geographical data in spatial maps, researchers can identify local distribution patterns and potential drivers behind these patterns. In health and medical sciences, there has been a growing recognition that spatial analysis and mapping techniques are helpful in addressing various challenges related to the allocation of healthcare resource in both urban and rural areas. Aim: The objective of this study was to analyze the spatial distribution pattern of the COVID-19 pandemic and the Index of Proximity Distribution (IPD) across 31 provinces of Iran between February 2019 and February 2023. A two-stage sampling method combining convenience and cluster sampling was used to examine COVID-19 distribution patterns in 31 provinces of Iran between 22 February 2020 and 22 February 2023. COVID-19 and IPD data were collected as part of this panel study. Data were analyzed using t-tests, chi-square tests, and analysis of variance (ANOVA) in SPSS version 28 (α = 0.05). Subsequently, daily COVID-19 infection data for each province in the analyzed period were processed in ArcGIS software, and the spatial distribution pattern of the pandemic in Iran were visualized by point density analysis. Standard distance and standard deviation ellipse techniques were employed to assess the density or dispersion of infected individuals and to determine the spatial distribution pattern of COVID-19 in Iran. A spatial autocorrelation (Moran’s I) analysis was conducted to identify the spatial distribution pattern of COVID-19 in Iran. Additionally, distance-based spatial autocorrelation was used to examine the prevalence of COVID-19 infection across Iranian provinces. In a grouping analysis, 31 Iranian provinces were classified into five groups based on the number of COVID-19 cases, and spatial statistics were used to examine the prevalence of COVID-19 within each group. A hot spot analysis and a standard distance (SD) analysis were conducted to explore spatial correlations in the number of individuals affected by COVID-19 in each province. Results: Based on the Moran index, a random spatial pattern with a Z-Score of 1.485 was identified in March 2019, whereas a clustered distribution of COVID-19 with a Z-Score of 3.039 was determined in February 2023. The distance-based spatial autocorrelation analysis revealed a positive value of the Moran index (0.136627) at a distance of 383.3 kilometers from Tehran, which points to positive spatial autocorrelation and a higher number of COVID-19 cases in nearby regions. Conversely, the Moran index assumed a negative value of 0.040246 at a distance of 726.6 kilometers from Tehran, which suggests that the number of pandemic cases decreased over distance from Tehran. Moreover, based on the results of the hot spot analysis, Tehran province was identified as a hot cluster with a higher prevalence of COVID-19 cases in that region. In contrast, Bushehr province was classified as a cold cluster with a lower prevalence of COVID-19 cases in comparison with the surrounding regions. These findings provide valuable insights into the spatial distribution and clustering of COVID-19 cases in Iran. The shift from a random spatial pattern in 2019 to clustered distribution in 2023 indicates that the pandemic spread rate increased over time. The positive spatial autocorrelation near Tehran highlights the role of proximity and population movement in the transmission of the virus. Furthermore, the identification of hot spots and cold spots in a country can inform targeted interventions and resource allocation to effectively manage and control the pandemic. Overall, this study demonstrates the value of spatial analysis in identifying the spatial distribution patterns and the dynamics of the COVID-19 pandemic in Iran. The integration of spatial analysis techniques with epidemiological data contributes to a better understanding of spatial-temporal patterns, facilitates effective public health responses and resource allocation strategies. These findings contribute to the growing body of knowledge on the spatial epidemiology of COVID-19 and can aid in informing future preparedness and response efforts in Iran and other regions that face similar challenges.

The Mysterious Risk of Arterial Thrombosis With COVID-19: A Case Series of Acute Limb Ischaemia in Vaccinated Patients.

Introduction Coronavirus-19 (COVID-19) plays a vital role in viral-induced hypercoagulability through the initiation of a cytokine storm. This mechanism has been found to predispose unvaccinated patients to systemic complications including arterial thrombosis (AT) with poor 30-day amputation-free survival rates. There remains, however, little understanding regarding the incidence in patients who have received a COVID-19 vaccination. This study aims to assess the incidence, management and outcomes of vaccinated patients with COVID-19 who develop thrombotic complications to reduce amputation and direct mortality. Methods The case notes of all emergency patients with COVID-19 referred to the vascular services in a tertiary referral centre between November 2021 and April 2022 were reviewed. Patients who were unvaccinated or admitted with stroke or coronary thrombosis were excluded. The study was undertaken to measure 30-day outcomes. Results Between November 2021 and April 2022, 167,290 people tested positive for COVID-19 in Norfolk. Thirty-one patients under the vascular service had COVID-19, of which, one patient was unvaccinated. Only one vaccinated patient was referred with AT and had a positive COVID-19 result two days after admission. Above-knee amputation was performed within 30 days and he survived. Seventeen percent of patients contracted COVID-19 during their hospital admission. Conclusion The incidence of acute limb ischaemia in vaccinated patients is low; however, the 30-day outcomes remain poor. Compared to unvaccinated patients, there was a significant reduction in the presentation of AT in vaccinated patients during that timeframe, despite a higher background number of COVID-19 cases. Therefore, vaccination may minimise the risk of AT.

COVID-19 impact on taxi-metro integrated usage: Changes and role of built environment, taking Lanzhou, China as an example

The COVID-19 pandemic has had a profound impact on how and how often people travel. The extent of changes in taxi-metro integrated usage (cooperative trips and competitive trips) before and after the pandemic is still unclear. Therefore, this paper aims to explore the impacts of COVID-19 on taxi-metro integrated usage and the relationship with built environment. This paper develops a novel method to infer the impact of COVID-19 on taxi-metro integrated usage and measures the resilience of cooperative and competitive ridership using a series of Bayesian structural time series models. The bootstrap aggregating regression tree model is applied to investigate the contribution of built environment on the relative impacts and resilience. Partial Dependence Plots (PDP) analysis is employed to explore the nonlinear effects of built environment elements. Results suggests: (1) The travel restrictions have a more significant impact than the number of COVID-19 cases on taxi-metro integrated usage. (2) Built environment factors contribute similarly to resisting the impact of COVID-19 on cooperative and competitive ridership during short-term and long-term period. (3) The recovery of the ridership shows a significant spatial heterogeneity. (4) Variables such as residential ratio, road network density, and bus stop density show nonlinear and threshold effects on the resilience of the ridership. Our findings have implications for travel policies during recovery from the pandemic.

Benefit–risk assessment for the Novavax COVID-19 vaccine (NVX-CoV2373)

A benefit–risk assessment of NVX-CoV2373, a vaccine to prevent COVID-19, was conducted to determine if the benefits of vaccination outweigh the risks of myocarditis/pericarditis.This analysis used data on myocarditis/pericarditis cases observed in the NVX-CoV2373 clinical studies, real-world data of mRNA COVID vaccine effectiveness against predominant SARS-CoV-2 strains in early 2023, and recent COVID-19 burden of disease data from the United States. The benefits of NVX-CoV2373 vaccination were estimated as the number of COVID-19 cases, hospitalizations, and deaths prevented. The risks of myocarditis/pericarditis cases and related hospitalizations and deaths occurring within 7 days of vaccination were also estimated.In our analysis, vaccination with NVX-CoV2373, per 100,000 vaccinated, resulted in an estimated 1805 COVID-19 cases prevented compared with an estimated 5.3 excess myocarditis/pericarditis cases. The number of COVID-19 hospitalizations and deaths prevented were also greater than vaccine-associated myocarditis/pericarditis hospitalizations and deaths.Our analysis indicates a positive benefit–risk balance for NVX-CoV2373.

Predicting health behaviors during the COVID-19 pandemic: A longitudinal study.

We investigated the social, emotional, and cognitive predictors of adherence to four health behaviors (handwashing, mask wearing, social contact limitations, and physical distancing) during one critical phase of the COVID-19 pandemic. We collected data (N = 5803, mean age = 53; 57% women) in Belgium at five time points between April and July 2021, a time during which infections evolved from high (third wave of the pandemic) to low numbers of COVID-19 cases. The results show that the social, emotional, and cognitive predictors achieved high levels of explained variance (R2 > .60). In particular, the central components of behavioral change (attitudes, intentions, control, habits, norms, and risk) were the strongest and most consistent predictors of health behaviors over time. Likewise, autonomous motivation and empathetic emotions (e.g., attentive, compassionate) had a positive impact on health behavior adherence, whereas it was the opposite for lively emotions (e.g., active, enthusiastic). These results offer policymakers actionable insights into the most potent and stable factors associated with health behaviors, equipping them with effective strategies to curtail the spread of future infectious diseases.

Social and economic variables explain COVID-19 diffusion in European regions

At the beginning of 2020, Italy was the country with the highest number of COVID-19 cases, not only in Europe, but also in the rest of the world, and Lombardy was the most heavily hit region of Italy. The objective of this research is to understand which variables have determined the prevalence of cases in Lombardy and in other highly-affected European regions. We consider the first and second waves of the COVID-19 pandemic, using a set of 22 variables related to economy, population, healthcare and education. Regions with a high prevalence of cases are extracted by means of binary classifiers, then the most relevant variables for the classification are determined, and the robustness of the analysis is assessed. Our results show that the most meaningful features to identify high-prevalence regions include high number of hours spent in work environments, high life expectancy, and low number of people leaving from education and neither employed nor educated or trained.

Description of the COVID-19 epidemiology in Malaysia.

Since the COVID-19 pandemic began, it has spread rapidly across the world and has resulted in recurrent outbreaks. This study aims to describe the COVID-19 epidemiology in terms of COVID-19 cases, deaths, ICU admissions, ventilator requirements, testing, incidence rate, death rate, case fatality rate (CFR) and test positivity rate for each outbreak from the beginning of the pandemic in 2020 till endemicity of COVID-19 in 2022 in Malaysia. Data was sourced from the GitHub repository and the Ministry of Health's official COVID-19 website. The study period was from the beginning of the outbreak in Malaysia, which began during Epidemiological Week (Ep Wk) 4 in 2020, to the last Ep Wk 18 in 2022. Data were aggregated by Ep Wk and analyzed in terms of COVID-19 cases, deaths, ICU admissions, ventilator requirements, testing, incidence rate, death rate, case fatality rate (CFR) and test positivity rate by years (2020 and 2022) and for each outbreak of COVID-19. A total of 4,456,736 cases, 35,579 deaths and 58,906,954 COVID-19 tests were reported for the period from 2020 to 2022. The COVID-19 incidence rate, death rate, CFR and test positivity rate were reported at 1.085 and 0.009 per 1,000 populations, 0.80 and 7.57%, respectively, for the period from 2020 to 2022. Higher cases, deaths, testing, incidence/death rate, CFR and test positivity rates were reported in 2021 and during the Delta outbreak. This is evident by the highest number of COVID-19 cases, ICU admissions, ventilatory requirements and deaths observed during the Delta outbreak. The Delta outbreak was the most severe compared to other outbreaks in Malaysia's study period. In addition, this study provides evidence that outbreaks of COVID-19, which are caused by highly virulent and transmissible variants, tend to be more severe and devastating if these outbreaks are not controlled early on. Therefore, close monitoring of key epidemiological indicators, as reported in this study, is essential in the control and management of future COVID-19 outbreaks in Malaysia.

Regional trends in the number of COVID-19 cases

AbstractIn this study, we analysed the novel coronavirus disease (COVID-19) cases data to investigate the regional infection trends in Japan. There had been seven outbreaks by October 2022 in Japan. In each outbreak, the number of COVID-19 cases has increased at different rates in different regions. The prefectural infection ratio is defined using COVID-19 cases data. We calculate the prefectural infection ratio and study the characteristic of each pandemic wave. The prefectural order of infection progression is estimated in each past wave of the COVID-19 pandemic. This study shows that the infection spread from the Kanto region in the fourth pandemic wave and the infection spread simultaneously from four regions in the sixth wave. It is also found that the infection situation trend in Okinawa differs from that in the other regions.

Do stress and overstatement in the news affect the stock market? Evidence from COVID-19 news in The Wall Street Journal

This study investigated the effect of media coverage on financial markets in response to COVID-19-related news. By collecting data from the United States Centers for Disease Control and Prevention, we study the effect of The Wall Street Journal's coverage and tone on stock markets. In particular, we attempted to measure media overstatements by comparing the number of articles and stress words with the number of COVID-19 cases. We obtained three main findings. First, investors discount macroeconomic news and objective measures of COVID-19. Second, excessive stress in the media tone leads to greater uncertainty and lower returns. Third, these results hold across sectors, although heterogeneity exists. Overall, long periods of high-stress sentiment and uncertainty affect investors more than single-day news.

Studying the Impact of the COVID-19 Pandemic on Bikeshares as a Mode of Shared Micromobility in Major Cities: A Case Study of Houston

A bikeshare system offers a convenient and cost-effective transportation service, providing shared bicycles for short-term use by individuals. It promotes affordability for users while fostering a healthier environment. By offering an alternative for those without access to private vehicles, it helps mitigate the rise in private car usage. Bike sharing also provides an important first-mile/last-mile commuting option. This study focuses on investigating the effects of the COVID-19 pandemic outbreak on bikeshare ridership, with a specific case study centered around Houston, Texas. The employed methodology involves a descriptive analysis and Negative Binomial regression modeling to uncover the relationship between the dependent variable (ridership) and the independent variables. The descriptive analysis revealed an overall increase in ridership during the COVID-19 period in 2020. Notably, longer duration trips were substantially higher in 2020 compared to 2019. Furthermore, the majority of trips occurred during off-peak hours, followed by evening and morning peak periods. Through regression analysis, this study found that the COVID-19 pandemic had a statistically significant positive impact on average daily ridership, with the number of COVID-19 cases positively influencing ridership levels. Additionally, the weekend indicator had a statistically significant positive impact on the average daily ridership. On the other hand, the temperature indicator did not show any significant impact on the average daily ridership, while precipitation had a statistically significant negative impact, leading to decreased ridership levels. The study highlights the significance of various factors in influencing bikeshare usage, contributing to a better understanding of urban transportation dynamics during such unprecedented times.

Shifting Sands: Evaluating the Influence of Macroeconomic Forces, Commodity Fluctuations, and COVID-19 on the FTSE Bursa Malaysia Hijrah Shariah Index

The COVID-19 epidemic has caused tremendous instability in the global financial ecosystem, impacting macroeconomic indicators and commodity prices as well as stock markets worldwide. The objective of this study is to quantify the impact of these factors on the FTSE Bursa Malaysia Hijrah Shariah Index, a key indicator of the performance of the Islamic finance market in Malaysia. This study uses a methodological approach that combines documentary and archival analysis to examine the relationship between the index and numerous economic parameters such as interest rates, exchange rates, gold and oil prices as well as COVID-19 case numbers from January 25, 2020 to December 1, 2021. The data was obtained from credible sources such as Investing.com, Central Bank of Malaysia and Our World in Data and analyzed using SPSS statistical software. This study adds to the current literature by specifically examining the FTSE Bursa Malaysia Hijrah Shariah Index during a crisis and provides useful insights for investors, policymakers, and academics. It highlights the vulnerability of the index to certain macroeconomic and health-related events and identifies opportunities for further research on the resilience and adaptation of Islamic finance.

From pandemic to endemic: Divergence of COVID-19 positive-tests and hospitalization numbers from SARS-CoV-2 RNA levels in wastewater of Rochester, Minnesota

Traditionally, public health surveillance relied on individual-level data but recently wastewater-based epidemiology (WBE) for the detection of infectious diseases including COVID-19 became a valuable tool in the public health arsenal. Here, we use WBE to follow the course of the COVID-19 pandemic in Rochester, Minnesota (population 121,395 at the 2020 census), from February 2021 to December 2022. We monitored the impact of SARS-CoV-2 infections on public health by comparing three sets of data: quantitative measurements of viral RNA in wastewater as an unbiased reporter of virus level in the community, positive results of viral RNA or antigen tests from nasal swabs reflecting community reporting, and hospitalization data. From February 2021 to August 2022 viral RNA levels in wastewater were closely correlated with the oscillating course of COVID-19 case and hospitalization numbers. However, from September 2022 cases remained low and hospitalization numbers dropped, whereas viral RNA levels in wastewater continued to oscillate. The low reported cases may reflect virulence reduction combined with abated inclination to report, and the divergence of virus levels in wastewater from reported cases may reflect COVID-19 shifting from pandemic to endemic. WBE, which also detects asymptomatic infections, can provide an early warning of impending cases, and offers crucial insights during pandemic waves and in the transition to the endemic phase.

‘I’ve never been so stressed in my life’: a qualitative analysis of young adults’ lived experience amidst pandemic restrictions in Prince Edward Island, Canada

ObjectivesThis qualitative study aimed to explore how young adults experienced the COVID-19 pandemic in relation to extensive lockdowns, social isolation and psychosocial distress. Specifically, this research focused on how the...

Empirical analysis of government interventions on Jordan's stock market during the COVID-19 pandemic: An ARDL approach

The objective of this study was to examine the impact of government actions on the Amman Stock Market's performance over the period from March 1, 2020, to June 30, 2021. The research uses the ARDL technique to examine the immediate and long-term connections between the variables. The findings indicate that government actions have an impact on the Amman stock market. From a larger standpoint, the research found that government actions and the stringency index had a detrimental impact on the Amman stock market in the long term. In contrast, the Containment Health Index had a favourable impact on the Amman Stock Market. Our analysis found a negative correlation between the number of new COVID-19 cases and the performance of the Amman Stock Market. These results highlight the complex relationship between government activities, market strictness, and public health initiatives in influencing the behaviour of financial markets. This finding is consistent with and strengthens the outcomes of prior research, enhancing our comprehension of how pandemic-related variables impact the performance of the stock market. The study's results are very relevant for politicians, decision-makers, and investors, offering crucial insights to inform their decision-making processes in times of future crises and uncertainty.

Profil Penggunaan Antibiotik untuk Pengobatan Pasien COVID-19 di Salah Satu Rumah Sakit Kota Bitung

Background: The overuse and misuse of antibiotics during the COVID-19 pandemic is one of the biggest concern that needs attention, considering antimicrobioal resistance has been a major threat to global health. According to WHO, this condition can make the infection difficult to treat, increasing the risk of disease spread and mortality. The significant increase in the number of COVID-19 cases in North Sulawesi and the excessive use of antibiotics in COVID-19 patients, shows that research that aims to describe the use of antibiotics in COVID-19 is needed as an early detection of irrational use of antibiotics.Methods: This study was carried out in one of the COVID-19 Referral Hospital in the city of Bitung. The data of this study was collected by purposive sampling and conducted retrospectively. Sources of secondary data is from the patient medical records and patient treatment records. In this study 148 samples that meet the inclusion criteria are used. The inclusion criteria is hospitalized patients which is confirmed with COVID-19 during the period of July-December 2021, received antibiotic therapy during the treatment period, and medical record data.Result: The result shows that the mostly used antibiotics for COVID-19 theraphy are Azithromycin tablets 47.97% from the macrolide groups; Ceftriaxone injection 20.32% and Cefixime tablets 16.67% from the cephalosporin groups; and Levofloxacin infusion 8.94% from the fluoroquinolones group.Conclusion : Patients at the productive age is most likely to get exposed with COVID-19. The clinical symptoms frequently occur in the mild category (68.92%). The use of antibiotics in COVID-19 patients at one of the COVID-19 Referral Hospitals in Bitung City is in accordance with the 3rd edition of the COVID-19 Management Guidelines, and the mostly used antibiotics is Azithromycin tablets as many as 118 patients (47.97%).