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The COVID-19 pandemich as significantly affectedva nous count ries worl dwide, including Indonesia. This study specifically examines the spatial distribution pattern of COVID-19 cases among sub-districts in DKI Jakarta and its neighboring areas. The study investigates the impact of spatial characteristics such as building density, population density, road network connectivity, and accessibility, as well as infrastructure completeness. A spatial regression model was employed to analyze the influence and pattern of COVID-19 case distribution among sub-districts. Spatial modeling indicates that geo graphic 1ocation has an effect on the data, often referred to as the autocorrelation effect. Moran's Index was used to test the relationship between district locations and the number and growth rate of cases. The study findings reveal a positive spatial autocorrelation in the growth rate pattern of COVID-19 cases among sub-districts and dusters in DKI Jakarta and its surrounding areas. The spatial regression model, specifically the Spatial Autoregressive Model (SAR), identifies road connectivity, number of health centers, building density, and population density as spatial variables that significantly influence the rate of COVID-19 cases. [ABSTRACT FROM AUTHOR]

Before the Fall 2020 semester, college presidents and the NCAA made decisions about playing college football. The current study aims to examine the association between college football games and COVID-19 infections at universities. More than 1,800 college campuses nationwide from The New York Times database on COVID-19 cases on college campuses. Random effects negative binomial regression is used to analyze the association between college football games, membership to college football organizations, and COVID-19 cases at the universities studied. The number of football games played was significantly associated with higher COVID-19 cases at the universities studied. Membership to certain college football organizations was also significantly associated with higher COVID-19 cases, particularly FBS and Power Five conferences. These findings provide a baseline for subsequent analyses across other sports and advocates for continued assessment of protocols that ensure the safety and well-being of student-athletes, fans, and university communities. [ABSTRACT FROM AUTHOR]

The influence of Covid-19 pandemic crisis on rates of return is analyzed in this paper in the light of possible data manipulation related to reporting systems provided by the administration in the USA, Turkey and Poland. The study used various methods of analyzing the relationship of a discrete, non-discrete and dichotomous data nature between the studied variables. As a result, the strongest reaction of the market was observed in Turkey followed by the USA and Poland. It can be concluded that the reaction of the surveyed markets was influenced by the data manipulations. The added value of the article is related to the use of various methods to study phenomena and detect the impact of data manipulation on the markets. [ABSTRACT FROM AUTHOR]

In this ecological study, we aim to establish the role vaccines play in bringing the pandemic under control, as well as the impact of pathogen variants, vaccine hesitancy, and medical resource availability during the process by utilizing publicly available data. The study spans a three-year data collection period for daily hospital admissions due to COVID-19 and the daily reported cases of COVID-19 across all 50 states in the USA. In doing so, we aim to demonstrate the difference in severity of the SARS-CoV-2 pathogen among vaccinated and unvaccinated populations in the USA. The study assesses the correlation of COVID-19 vaccines (e.g., Pfizer, Moderna, and Janssen) and disease outcomes (transmissibility, severity, and deaths) caused by different strains of SARS-CoV-2 and establishes a negative correlation between COVID-19 vaccine and disease outcomes. By considering potential confounders in vaccine hesitancy, medical resource availability and vaccine dosage, we demonstrate the aforementioned to be insubstantial in predicting disease outcomes while the latter displays a contrasting significance in terms of disease outcomes. Between all the major variants of concern, the Delta and Omicron variants in particular have been associated with higher virulence and transmissibility factors respectively. Hence, the CDC continues to encourage the US population to get vaccinated since vaccines are one of the most effective ways to protect the community from potential outbreaks and prevent severe disease manifestations. [ABSTRACT FROM AUTHOR]

ObjectiveTo examine time-dependent variations in serum antibodies of severe acute respiratory disease coronavirus 2 (SARS-CoV-2) among confirmed coronavirus disease 2019 (COVID-19) cases at different stages and inactivated COVID-19 vaccine recipients after inoculation. MethodsThe study was conducted in a city of Henan province. Totally 86 serum samples were collected from 43 confirmed COVID-19 cases at acute stage (1th – 23rd day of onset) and recovery state (25th – 58th day of onset) during January 2020 – October 2021; other 184 serum samples from 46 healthy recipients of inactivated COVID-19 vaccine at 4/8/12/31 weeks after inoculations and 50 serum samples from 50 residents without confirmed SARS-CoV-2 infections were also collected during March – October 2021. Anti-SARS-CoV-2 immunoglobulin A/G/M (IgA/G/M) were detected with magnetic particle chemiluminescence method and enzyme-linked immunosorbent assay for all the serum samples. The levels of the serum antibodies among the three groups′ participants were analyzed statistically. ResultsAmong the confirmed COVID-19 cases, the positivity rates of anti-SARS-CoV-2 IgA, IgM and IgG of the serum samples collected at recovery stage were significantly higher than those at acute stage (all P < 0.05) ; while, the concentrations of anti-SARS-CoV-2 IgM and IgG of the serum samples collected at recovery stage were significantly higher than those at acute stage (both P < 0.05). Among the healthy COVID-19 vaccine recipients 31 weeks after the inoculations, the positivity rate was 63.04% (29/46) for anti-SARS-CoV-2 IgG and 36.96% (17/46) for anti-SARS-CoV-2 IgA, but all samples were negative for anti-SARS-CoV-2 IgM. No significant differences were observed in the absorbance ratios anti-SARS-CoV-2 IgA among the samples collected 4, 8, 12, and 31weeks after the inoculation (F = 2.365, P = 0.073), but there were significant differences in the concentrations of anti-SARS-CoV-2 IgM and IgG (both P < 0.05) among the samples. Both the positivity rate of anti-SARS-CoV-2 IgA and IgM and the concentrations of anti-SARS-CoV-2 IgG and IgM were significantly higher among the confirmed COVID-19 cases than those among the inactivated COVID-19 vaccine recipients during a comparable period of time (P < 0.05 for all). ConclusionThe positivity rate of serum anti-SARS-CoV-2 IgG is the highest during early stage and recovery stage among COVID-19 cases. The positivity of serum anti-SARS-CoV-2 IgA and IgG occur earlier than that of IgM and could maintain till 31 weeks after the inoculation among inactivated COVID-19 vaccine recipients. The concentration of serum anti-SARS-CoV-2 IgG and IgM are higher among COVID-19 cases than those among inactivated COVID-19 vaccine recipients during a comparable period of time.

The Covid-19 pandemic, which has been affecting the world since December 2019, has become one of the biggest problems of the 21st century. There are studies stating that the contagiousness of the Covid-19 pandemic, which is transmitted from person to person, increases more with environmental factors such as air pollution, and accordingly, there is an increase in the number of cases. In this study, a panel regression model to investigate the effect of air pollution concentrations such as PM10 and SO2 as environmental factors and population density on the monthly mean number of Covid-19 cases for 12 regions at the nomenclature of territorial units for statistics (NUTS) level 1 in Turkey between June 2020 and November 2020, and a linear regression model to investigate the effect at the regional level. we used. Based on the model results, we concluded that a small increase in air pollution indicators led to an increase in the number of Covid-19 cases in Turkey and its regions. It is very important to identify preventable environmental factors in order to prevent and minimize the effects of respiratory tract diseases and rapidly spreading pandemic diseases such as Covid-19. Accordingly, we can conclude that countries should take some measures, especially on air pollution, in order to develop public health and pandemic/disease management strategies and to reduce the risk of respiratory diseases.

The main purpose of the research is to estimate the extent of excess Covid-19 cases and mortalities in India and examine its relationship with the degree of economic progress in various parts of the country especially given the uneven nature of the impact of the pandemic throughout the nation. The main hypotheses of the study were: 1) in areas with a high level of income per capita, the death rate per 100,000 population will be lower; 2) areas with a high level of income per capita tend to be more urbanized, economically active, and therefore quite densely populated, which increases the probability of morbidity and mortality. The object of research is over 20 million Covid-19 cases and over 370,000 deaths in 31 States and Union Territories (UTs) in India beginning in the first months of the pandemic and going through the middle of 2021. The methodological tools of the conducted research were the methods of regression analysis. The study of a relative measure of success in pandemic management (less than one-half of the median death rate as the relative threshold for measuring success) empirically confirms and theoretically proves that India had at least 16.6 million excess Covid-19 cases and over 228,000 excess COVID-19 deaths as of June 18, 2021. The paper presents the results of an empirical analysis of the relationship between excess deaths of the population from Covid-19 and state-level per-capita income (as an explanatory variable), which testified that about 60% of actual and excess deaths can be explained by the per-capita income alone. According to the results of the analysis, it was proved that actual and excess deaths are both higher in richer states. Poorer states did considerably well in keeping Covid-19 mortality low compared to their more affluent counterparts. The positive relationship between Covid-19 mortality and per-capita income does not go away even after controlling for the caseloads used as a proxy for the spread of the pandemic. This augmented model explains about 80% of the actual and excess deaths from the Covid-19 pandemic in India. After controlling for caseloads, a thousand Rupees increase in per-capita income contributed to about 15 additional deaths per 100,000 population. The article presents the results of an empirical analysis of the relationship between economic development (as measured by the per-capita income) and excess mortality from COVID-19, which proved a positive relationship between them and proved a potentially adverse impact of economic progress on human immunity, especially if population density, living conditions, and food security moderate that relationship.

A world-wide immunization project was launched at the peak of COVID-19 pandemic to contain and minimize the adverse effects of SARS-CoV-2 virus. We carried out a series of statistical analyses in this paper to determine, confirm and quantify the impact of the vaccinations on COVID-19 cases and mortalities, amidst critical confounding factors—temperature and solar irradiance. The experiments in this paper were carried out on the world data, data from 21 countries, and the five major continents. The significance of the 2020–2022 vaccinations on the COVID-19 cases and mortalities response data were evaluated via Hypotheses' tests. Correlation coefficient analyses were carried out to determine the extent of the relationship between vaccination coverage and corresponding COVID-19 mortalities data. The impact of vaccination was quantified. The effects of the weather factors—temperature and solar irradiance, on COVID-19 cases and mortalities data were analyzed. The series of hypotheses tests carried out reveal that vaccinations did not affect cases; however, vaccinations significantly impacted the mean daily mortalities in all five major continents and globally. The correlation coefficient analysis results show vaccination coverage to be highly and negatively correlated with daily mortalities in the world—the five major continents and most of the countries studied in this work. The percentage reduction in mortalities as a result of wider vaccination coverage was indeed significant. Temperature and solar irradiance impacted daily COVID-19 cases and mortalities data during the vaccination and post-vaccination periods. Results show that the world-wide vaccination against COVID-19 project had a significant impact in reducing mortalities and minimizing the adverse effects due to COVID-19 globally, in all five (5) major continents of the world and the countries studied in this work, however, temperature and solar irradiance still had effects on COVID-19 response in the vaccination eras. [ABSTRACT FROM AUTHOR]

This article investigates the relationship between school openings and Covid‐19 diffusion when school‐age vaccination becomes available. The analysis relies on a unique geo‐referenced high frequency database on age of vaccination, Covid‐19 cases and hospitalization indicators from the Italian region of Sicily. The study focuses on the change of Covid‐19 diffusion after school opening in a homogeneous geographical territory (i.e., with the same control measures and surveillance systems, centrally coordinated by the Regional Government). The identification of causal effects derives from a comparison of the change in cases before and after school opening in the school year 2020/21, when vaccination was not available, and in 2021/22, when the vaccination campaign targeted individuals of age 12–19 and above 19. Results indicate that, while school opening determined an increase in the growth rate of Covid‐19 cases in 2020/2021, this effect has been substantially reduced by school‐age vaccination in 2021/2022. In particular, we find that an increase of approximately 10% in the vaccination rate of school‐age population reduces the growth rate of Covid‐19 cases after school opening by approximately 1%. [ABSTRACT FROM AUTHOR]

Minoritized racial groups in the U.S. have experienced disproportionately higher rates of COVID-19 cases and deaths. Studies have linked structural racism as a critical factor causing these disproportionate health burdens. We analyse the relationships between county-level COVID-19 cases and deaths and five measures of structural racism on Black Americans: Black–White residential segregation, differences in educational attainment, unemployment, incarceration rates, and health insurance coverage between Black and White Americans. When controlling for socioeconomic, demographic, health and behavioural factors significant relationships were found between all measures of structural racism with cases and/or deaths except Black–White differences in health insurance coverage. Black–White disparities in educational attainment and incarceration were the strongest predictors. The results varied greatly across regions of the U.S. We also found strong relationships between COVID-19 and mobility and the proportion of foreign-born non-citizens. This work supports the important need to confront structural racism on multiple fronts to address health disparities. [ABSTRACT FROM AUTHOR]

Background: COVID-19 is a disease that attacks the respiratory system and is highly contagious, so cases of the spread of COVID-19 are increasing every day. The increase in COVID-19 cases cannot be predicted accurately, resulting in a shortage of services, facilities and medical personnel. This number will always increase if the community is not vigilant and actively reduces the rate of adding confirmed cases. Therefore, public awareness and vigilance need to be increased by presenting information on predictions of confirmed cases, recovered cases, and cases of death of COVID-19 so that it can be used as a reference for the government in taking and establishing a policy to overcome the spread of COVID-19. Objective: This research predicts COVID-19 in confirmed cases, recovered cases, and death cases in Lampung Province Method: This study uses the ANN method to determine the best network architecture for predicting confirmed cases, recovered cases, and deaths from COVID-19 using the k-fold cross-validation method to measure predictive model performance. Results: The method used has a good predictive ability with an accuracy value of 98.22% for confirmed cases, 98.08% for cured cases, and 99.05% for death cases. Conclusion: The ANN method with k-fold cross-validation to predict confirmed cases, recovered cases, and COVID-19 deaths in Lampung Province decreased from October 27, 2021, to January 24, 2022. [ABSTRACT FROM AUTHOR]

With a unique and large size of testing results of 1,842 samples collected from 12 wastewater treatment plants (WWTP) for 14 months through from low to high prevalence of COVID-19, the sensitivity of RT-qPCR detection of SARS-CoV-2 RNA in wastewater that correspond to the communities was computed by using Probit analysis. This study determined the number of new COVID-19 cases per 100,000 population required to detect SARS-CoV-2 RNA in wastewater at defined probabilities and provided an evidence-based framework of wastewater-based epidemiology surveillance (WBE). Input data were positive and negative test results of SARS-CoV-2 RNA in wastewater samples and the corresponding new COVID-19 case rates per 100,000 population served by each WWTP. The analyses determined that RT-qPCR-based SARS-CoV-2 RNA detection threshold at 50%, 80% and 99% probability required a median of 8 (range: 4-19), 18 (9-43), and 38 (17-97) of new COVID-19 cases /100,000, respectively. Namely, the positive detection rate at 50%, 80% and 99% probability were 0.01%, 0.02%, and 0.04% averagely for new cases in the population. This study improves understanding of the performance of WBE SARS-CoV-2 RNA detection using the large datasets and prolonged study period. Estimated COVID-19 burden at a community level that would result in a positive detection of SARS-CoV-2 in wastewater is critical to support WBE application as a supplementary warning/monitoring system for COVID-19 prevention and control. [ABSTRACT FROM AUTHOR]

Abstract Background The development of barotrauma in COVID-19 patients who were ventilated and admitted to the intensive treatment unit seemed to have been a problematic issue in the COVID era. This study aimed to explore the possibility of developing the barotrauma-related issues with mechanical ventilation in the cases of individuals suffering from COVID-19. Results Out of 48 patients who developed barotrauma, 30 (62.5%) presented with pneumothorax, 22 (45.8%) with pneumomediastinum, 10 (20.8%) with subcutaneous emphysema, and 2 (4.1%) with pneumopericardium. Of those that developed barotrauma, 45 (93.7%) patients were in acute respiratory distress syndrome. In patients with and without barotrauma, significant factors were white blood cell count (p = 0.001), neutrophil percentage (p = 0.012), and lymphocyte percentage (p = 0.014). There were no statistically significant differences in CRP, procalcitonin, d-dimer test, LDH, or ferritin. Conclusions Patients infected with COVID-19 have a high risk of barotrauma when on mechanical ventilation. As a result, the death rate in this patient group is higher.

Social vulnerability is defined as a characteristic of groups that can threaten their ability to prevent, cope with or recover from the impact of a hazard. By linking social conditions with risk exposure, social vulnerability is useful for looking at the inequality of the social effects experienced by society from health crises such as the Covid-19 pandemic. The purpose of this study was to spatially analyze the vulnerability to Covid-19 in the Palembang City sub-district in 2021 by using the social characteristics of the region. This type of research was descriptive with an ecological study design. The population group studied was 18 sub-districts in the city of Palembang. The study used secondary data such as the Covid-19 variable and the vulnerable population received from the Palembang City Health Office, and the rest of the variable from the government's official website and Google Maps. The analysis process was done through Spatial Multi-Criteria Evaluation (SMCE) using Microsoft Excel and spatial analysis in weighted overlays using QGIS. For the final social vulnerability status, sub-districts with high vulnerability were Sukarami, Ilir Barat I, Plaju, and Ilir Timur I, respectively. For sub-districts with low vulnerability were Bukit Kecil and Sematang Borang. One way to reduce the level of vulnerability in an area was by taking preventive measures in the community whose needs needed to be further considered and maximizing the implementation of 3T activities and vaccinations.

Applying a successful prediction of the confirmed, recovered and deaths is thought to be the basic requirement to successfully control the spreading rate of diseases. Time series models have extensively been considered as the suitable methods to forecast the confirmed, recovered and deaths because of the virus. The aim of this research is to apply an autoregressive Integrated Moving Average (ARIMA) modeling approach for projectingCOVID-19 confirmed, recovered and deaths cases in Ethiopia. Over strict follows of allphase of Box-Jenkins strategy, ARIMA (1,1,1), ARIMA (16,1,2) and ARIMA (0,1,1)models for confirmed case, recovered and death case respectively was selected as the best models for predicting coronavirus cases of Ethiopia. Using these models, a forecast of five-month ahead of the future situation of COVID-19 confirmed case, recovered and death case (Jan 3, 2021 to May 3, 2021) has made. The results showed that in the coming five months from Jan 3, 2020 to May 3, 2021, the number of COVID-19 confirmed, recovered and deaths cases in Ethiopia may reach up to 320,597; 168,912 and 4438, respectively. Generally, the size of the coronavirus distribution was increased from time to time in the past ten months, until 3 rd Jan, 2021, and it is expected to continue quicker than before for the coming 5-months, until the end of May 2021, in Ethiopia and more rapidly than before while the peak will remain unknown yet. Therefore, effective implementation of preventive measures and rigorous compliance by avoiding negligence with the rules such as prohibiting public gatherings, travel restrictions, personal protection measures, and social distancing may improve the spreading rates of the virus. Further, through updating more new data with the continuous reconsideration of the predictive model provide useful and more precise prediction

Covid-19 has damaged the global economy, as the series of lockdowns had negative impacts on the global economy. Investors around the world invested heavily in gold, and as a result, steep increase occurred. This study was intended to determine the relationship between COVID-19 infection rates and the price of gold, using different statistical analyses. Johansen cointegration test revealed that cointegration existed from a lag of 0 to 21 days, yet no relationship was observed from day 22 onwards. Also, Granger causality test revealed that gold price had a significant response to increasing COVID-19 cases. Conversely, gold price did not have Granger causes on the COVID-19 infection rates. Furthermore, the Vector Error Correction Model (VECM) result indicated that a 1% increase in COVID-19 cases led to a 0.0166% increase in gold price. Considering the exponential growth in COVID-19 cases of 17.38% average growth per day during the period from 1 January 2020 to 31 March 2022, 0.2885% increase in gold price per day has been attributed from COVID-19 cases. The sequential methodology presented in this paper is useful to potential gold investors in understanding the responsiveness of gold price to global pandemics and forecasting future gold prices during such global crises. [ABSTRACT FROM AUTHOR]

The purpose of this research is to classify time-series data on the number of daily COVID-19 cases based on the dynamics. This research aims to evaluate the effectiveness of community activity restrictions in suppressing the number of new cases of COVID-19 in cities and regencies in West Java. We performed time-series clustering on daily positive case data for COVID-19 in 27 cities and regencies in West Java Province, Indonesia for this study. The k-medoids clustering algorithm was used for clustering, with shape-based lock step measures, specifically, the cross correlation-based distance. We used daily new infected cases data for COVID-19 in 27 cities and regencies in West Java Province during the worst situation. We used data from 1 July 2021 to 31 September 2021 and from 1 January 2022 to 31 May 2022, during the Emergency Community Activity Restriction period (PPKM). According to our findings, the optimal number of clusters that could be formed from the data we had was 4 clusters for the first period and 2 clusters for the second period, with silhouette value of 0.2633 and 0.6363, respectively. For the first period, we discovered that PPKM was successful in clusters 1 and 2, namely in 25 cities/districts in West Java, except for Bogor and Depok, while for the second period, we found PPKM to be effective in reducing the number of COVID-19 cases throughout cities and regencies in West Java. This shows there is an improvement from the implementation of PPKM in the first period. We also found that the cluster that was formed was not only influenced by the effectiveness of the PPKM, but also by geography. The closer a city is to a hotspot region for the spread of COVID-19, the earlier the increase in the number of new COVID-19 cases will occur. [ABSTRACT FROM AUTHOR]

Background: The development of barotrauma in COVID-19 patients who were ventilated and admitted to the intensive treatment unit seemed to have been a problematic issue in the COVID era. This study aimed to explore the possibility of developing the barotrauma-related issues with mechanical ventilation in the cases of individuals suffering from COVID-19. Results: Out of 48 patients who developed barotrauma, 30 (62.5%) presented with pneumothorax, 22 (45.8%) with pneumomediastinum, 10 (20.8%) with subcutaneous emphysema, and 2 (4.1%) with pneumopericardium. Of those that developed barotrauma, 45 (93.7%) patients were in acute respiratory distress syndrome. In patients with and without barotrauma, significant factors were white blood cell count (p = 0.001), neutrophil percentage (p = 0.012), and lymphocyte percentage (p = 0.014). There were no statistically significant differences in CRP, procalcitonin, d-dimer test, LDH, or ferritin. Conclusions: Patients infected with COVID-19 have a high risk of barotrauma when on mechanical ventilation. As a result, the death rate in this patient group is higher. [ABSTRACT FROM AUTHOR]

Vaccination has been the most effective approach in the fight against COVID-19 pandemic. More than half of the world's population has been vaccinated and sufficient data is available to analyze the impact of COVID-19 vaccines around the globe. In this paper, we present a correlation analysis between administered vaccine doses and COVID-19 cases/deaths in Europe. The correlation analysis is performed considering different types of vaccinations, different age groups and different COVID-19 variants (including the prevalent Delta and Omicron variants). We present a detailed analysis for 30 European countries giving various insights such as efficacy of six different vaccines, effect of vaccinating different age groups and how the correlation evolves as different COVID-19 variants emerge. [ABSTRACT FROM AUTHOR]

Since the beginning of 2020, COVID-19 has been the biggest public health crisis in the world. To help develop appropriate public health measures and deploy corresponding resources, many governments have been actively tracking COVID-19 in real time within their jurisdictions. However, one of the key unresolved issues is whether COVID-19 was distributed differently among different age groups and between the two sexes in the ongoing pandemic. The objectives of this study were to use publicly available data to investigate the relative distributions of COVID-19 cases, hospitalizations, and deaths among age groups and between the sexes throughout 2020; and to analyze temporal changes in the relative frequencies of COVID-19 for each age group and each sex. Fifteen countries reported age group and/or sex data of patients with COVID-19. Our analyses revealed that different age groups and sexes were distributed differently in COVID-19 cases, hospitalizations, and deaths. However, there were differences among countries in both their age group and sex distributions. Though there was no consistent temporal change across all countries for any age group or either sex in COVID-19 cases, hospitalizations, and deaths, several countries showed statistically significant patterns. We discuss the potential mechanisms for these observations, the limitations of this study, and the implications of our results on the management of this ongoing pandemic.

Climate finance and carbon pricing are regarded as sustainable policy mechanisms for mitigating negative environmental externalities via the development of green financing projects and the imposition of taxes on carbon pollution generation. Financial literacy indicates that it is beneficial to invest in cleaner technology to advance the environmental sustainability goal. The current wave of the COVID-19 epidemic has had a detrimental effect on the world economies' health and income. The pandemic crisis dwarfs previous global financial crises in terms of scope and severity, collapsing global financial markets. The study's primary contribution is constructing a climate funding index (CFI) based on four critical factors: inbound foreign direct investment, renewable energy usage, research and development spending, and carbon damages. In a cross-sectional panel of 43 nations, the research evaluates the effect of climate funding, financial literacy, and carbon pricing in lowering exposure to coronavirus cases. The study utilized Newton–Raphson and Marquardt steps to estimate the current parameter estimates while evaluating the COVID-19 prediction model with level regressors using the robust least squares regression model (S-estimator). Additionally, the innovation accounting matrix predicts estimations over a specific period. The findings indicate that climate finance significantly reduces coronavirus exposure by introducing green financing initiatives that benefit human health, which eventually strengthens the immune system's ability to fight infectious illnesses. Financial literacy and carbon pricing, on the other hand, are ineffectual in controlling coronavirus infections due to rising economic activity and densely inhabited areas that enable the transmission of coronavirus cases across countries. Similar findings were obtained using the alternative regression apparatus. The COVID-19 predicted variable was used as a "response variable," and climate financing was shown to have a favorable impact on containing coronavirus exposure. As shown by the innovation accounting matrix, carbon pricing would drastically decrease coronavirus cases' exposure over a time horizon. The study concludes that climate finance and carbon pricing were critical in improving air quality indicators, which improved countries' health and wealth, allowing them to reduce coronavirus infections via sustainable healthcare reforms. [ABSTRACT FROM AUTHOR]

By applying the data from 59 countries to simultaneous equations models with 3SLS and GMM estimations, we analyse the effects and counter-effects of stringency of movement restrictions on unemployment rate and COVID-19 infection cases. On the one hand, our results show that the unemployment rate rises when the governments impose more stringent movement restrictions. On the other hand, a higher unemployment rate leads the government to lower stringency of movement restrictions. Our findings provide evidence on the tradeoff between unemployment rates and infection cases under the government's virus containment intervention policies. This allows the policymakers to find a balance between public health emergencies and the economy. We also find that the stringency is affected positively by infection cases, negatively by the number of hospital beds per capita and healthcare spending per capita; the number of infection cases is affected positively by urban population density and negatively by an individualism index; and the unemployment rate is affected negatively by GDP. [ABSTRACT FROM AUTHOR]

Background: Coronavirus disease 2019 (COVID‐19) is a new pandemic disease, associated with substantial morbidity and mortality. Its diagnosis requires centralized facilities and time. Aims: To describe the exposure history and clinical picture of the COVID‐19 patients, to study the SARS‐CoV‐2 Virus load and some determinants that may correlate with its prognosis, and to evaluate the role of inflammatory index NLR as an early predictor of COVID‐19 prognosis. Methodology: A prospective follow‐up study included laboratory‐confirmed 179 COVID‐19 cases out of 660 suspected COVID‐19 cases, at El‐Madinah El‐Monawarah General Hospital in April 2020. Confirmed cases were managed by the Saudi Protocol and followed up every 2 weeks by PCR, neutrophil to lymphocyte ratio (NLR) for 1 month. Data were collected through a validated questionnaire and by qualified infection control staff. Results: The majority of the COVID‐19 cases were 67 (37.4%) aged 30 to <45 years, 157 (87.7%) males, 76.0% working outside the medical field. 38.0% were asymptomatic and 26.3% had severe symptoms, while the main presenting symptoms were fever and dry cough (49.7% and 43.6%), respectively. The case fatality was 7.8%. The male, nonmedical occupation, and low level of education had a statistically significant relationship with the baseline PCR. There was an inverse significant correlation between baseline PCR readings and the recovery duration and health status outcomes. NLR was noted to be significantly higher among old age, illiterate nonmedical occupation, case with severe symptoms, MICU admission, and worst health status outcomes, but it was paradoxically higher among nonadmitted positive cases. Conclusion: Admitted COVID‐19 cases outcomes (disease severity, ICU admission, and mortality) significantly correlated to NLR and not to the baseline PCR viral load. NLR could be a beneficial prognostic and triaging parameter especially old nonmedical COVID‐19 patients. [ABSTRACT FROM AUTHOR]

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread to more than 200 countries with a current case fatality ratio (CFR) of more than 2% globally. The concentration of air pollutants is considered a critical factor responsible for transmitting coronavirus disease among the masses. The photochemical process and coal combustions create respiratory disorders that lead to coronavirus disease. Based on the crucial fact, the study evaluated the impact of nitrous oxide (N2O) emissions, coal combustion, and traffic emissions on COVID-19 cases in a panel of 39 most affected countries of the world. These three air pollution factors are considered to form a lethal smog that negatively affects the patient's respiratory system, leading to increased susceptibility to coronavirus worldwide. The study used the Markov two-step switching regime regression model for obtaining parameter estimates. In contrast, an innovation accounting matrix is used to assess smog factors' intensity on possibly increasing coronavirus cases over time. The results show that N2O emissions, coal combustion, and traffic emissions increase COVID-19 cases in regime-1. On the other hand, N2O emissions significantly increase coronavirus cases in regime-2. The innovation accounting matrix shows that N2O emissions would likely have a more significant share of increasing coronavirus cases with a variance of 33.902%, followed by coal combustion (i.e., 6.643%) and traffic emissions (i.e., 2.008%) over the time horizon. The study concludes that air quality levels should be maintained through stringent environmental policies, such as carbon pricing, sustainable urban planning, green technology advancement, renewable fuels, and pollution less accessible vehicles. All these measures would likely decrease coronavirus cases worldwide. [ABSTRACT FROM AUTHOR]

Since the COVID-19 outbreak, diagnostic imaging has been providing valuable radiological support for disease diagnosis and prognosis prediction. Radiological staff, especially radiographers working on the front line in the battle against the COVID-19 outbreak. They are in direct contact with the patients, bearing the responsibility and pressure of both the infection prevention and control and the radiation protection. A number of relevant professionals and experts reached a consensus, which clarifies the detailed implementing rules with respective to infection control and protection of the radiation workers, disinfection of diagnostic radiology equipment and workplace, and radiological protection. The aim is to further normalize the clinical procedures of radiological staff, reduce the infection risk, especially the radiation risk to medics and patients.

Applying a successful prediction of the confirmed, recovered and deaths is thought to be the basic requirement to successfully control the spreading rate of diseases. Time series models have extensively been considered as the suitable methods to forecast the confirmed, recovered and deaths because of the virus. The aim of this research is to apply the Autoregressive Integrated Moving Average (ARIMA) modelling approach for projectingCOVID-19 confirmed, recovered and deaths cases in Ethiopia. Over strict follows of all phases of Box-Jenkins strategy, ARIMA (1,1,1), ARIMA (16,1,2), and ARIMA (0,1,1)models for confirmed case, recovered and death case, respectively were selected as the best models for predicting coronavirus cases of Ethiopia. Using these models, a forecast of five-month a heads future situation of COVID-19 confirmed case, recovered and death case (Jan 3, 2021 to May 3, 2021) has been made. The results showed that in the coming five months from Jan 3, 2020 to May 3, 2021, the number of COVID-19 confirmed, recovered and deaths cases in Ethiopia may reach up to 320,597; 168,912 and 4438, respectively. Generally, the size of the coronavirus distribution was increased from time to time in the past ten months, until 3rd Jan, 2021, and it is expected to continue quicker than before for the coming 5-months, until the end of May, 2021, in Ethiopia and more rapidly than before while the peak will remain unknown yet. Therefore, effective implementation of the preventive measures and a rigorous compliance by avoiding negligence with the rules such as prohibiting public gatherings, travel restrictions, personal protection measures, and social distancing may improve the spreading rates of the virus. Further, through updating more new data with a continuous reconsideration of predictive model provide useful and more precise prediction

Abstract Background Coronavirus disease 2019 (COVID‐19) is a new pandemic disease, associated with substantial morbidity and mortality. Its diagnosis requires centralized facilities and time. Aims To describe the exposure history and clinical picture of the COVID‐19 patients, to study the SARS‐CoV‐2 Virus load and some determinants that may correlate with its prognosis, and to evaluate the role of inflammatory index NLR as an early predictor of COVID‐19 prognosis. Methodology A prospective follow‐up study included laboratory‐confirmed 179 COVID‐19 cases out of 660 suspected COVID‐19 cases, at El‐Madinah El‐Monawarah General Hospital in April 2020. Confirmed cases were managed by the Saudi Protocol and followed up every 2 weeks by PCR, neutrophil to lymphocyte ratio (NLR) for 1 month. Data were collected through a validated questionnaire and by qualified infection control staff. Results The majority of the COVID‐19 cases were 67 (37.4%) aged 30 to

The novel coronavirus 2019 (COVID-19) emerges from the Chinese city Wuhan and its spread to the rest of the world, primarily affected economies and their businesses, leading to a global depression. The explanatory and cross-sectional regression approach assesses the impact of COVID-19 cases on healthcare expenditures, logistics performance index, carbon damages, and corporate social responsibility in a panel of 77 countries. The results show that COVID-19 cases substantially increase healthcare expenditures and decrease corporate social responsibility. On the other hand, an increase in the coronavirus testing capacity brings positive change in reducing healthcare expenditures, increased logistics activities, and corporate social responsibility. The cost of carbon emissions increases when corporate activities begin to resume. The economic affluence supports logistics activities and improves healthcare infrastructure. It linked to international cooperation and their assistance to supply healthcare logistics traded equipment through mutual trade agreements. The greater need to enhance global trade and healthcare logistics supply helps minimize the sensitive coronavirus cases that are likely to provide a safe and healthy environment for living. [ABSTRACT FROM AUTHOR]

The purpose of this research is to classify time-series data on the number of daily COVID-19 cases based on the dynamics. This research aims to evaluate the effectiveness of community activity restrictions in suppressing the number of new cases of COVID-19 in cities and regencies in West Java. We performed time-series clustering on daily positive case data for COVID-19 in 27 cities and regencies in West Java Province, Indonesia for this study. The k-medoids clustering algorithm was used for clustering, with shape-based lock step measures, specifically, the cross correlation-based distance. We used daily new infected cases data for COVID-19 in 27 cities and regencies in West Java Province during the worst situation. We used data from 1 July 2021 to 31 September 2021 and from 1 January 2022 to 31 May 2022, during the Emergency Community Activity Restriction period (PPKM). According to our findings, the optimal number of clusters that could be formed from the data we had was 4 clusters for the first period and 2 clusters for the second period, with silhouette value of 0.2633 and 0.6363, respectively. For the first period, we discovered that PPKM was successful in clusters 1 and 2, namely in 25 cities/districts in West Java, except for Bogor and Depok, while for the second period, we found PPKM to be effective in reducing the number of COVID-19 cases throughout cities and regencies in West Java. This shows there is an improvement from the implementation of PPKM in the first period. We also found that the cluster that was formed was not only influenced by the effectiveness of the PPKM, but also by geography. The closer a city is to a hotspot region for the spread of COVID-19, the earlier the increase in the number of new COVID-19 cases will occur.

The spread of the COVID-19 pandemic has a simultaneous temporal and spatial component. This pattern results from a complex combination of factors, including social ones, that lead to significant differences in the evolution of space-time distributions, both between and within countries. The aim of this study was to assess changes in the regularity of the spatial distribution of the number of diagnosed COVID-19 cases in Poland over more than a year of the pandemic. The analysis utilized daily and weekly data for 380 counties (poviats), using the local -- Poisson risk semivariogram -- measure of spatial autocorrelation. Despite the heterogeneity and errors in the source data, it was possible to identify clear patterns of temporal changes in the spatial distribution of COVID-19 cases, manifested by differences in the nature and extent of their autocorrelation. [ABSTRACT FROM AUTHOR]

Background: Novel Coronavirus or better known as Corona Virus Disease (COVID) 19 is a disease that first appeared in China in early December 2019. The disease quickly spread throughout China to Europe, the United States, Southeast Asia and Africa. Covid-19 is spread through droplets (particles) that come out through the human mouth when talking to others. This virus can infect everyone if the person is in contact with a covid-19 positive sufferer. So much for the ability of this virus to infect and spread is proven by the record that so far there are still many people infected with Covid-19 with increasing numbers. Method: This research method is a juridical-normative legal analysis. Legal research is based on the legal concept used. Results: The key to limiting the Covid-19 virus is that after knowing that something has been confirmed positive, tracing is immediately done to anyone who comes into contact with the patient. After all tracing is found, then a new examination is carried out whether they are also infected or not. Conclusion: There are several laws and regulations governing the opening of Patient Medical Rahasi, namely Law No. 36 of 2009 on Health and Regulation of the Minister of Health No. 269 of 2008 on Medical Records, which explicitly explains that related to the Covid-19 Pandemic is allowed to be opened Medical Secrets of patients concerning the interests and safety of thepublic. [ABSTRACT FROM AUTHOR]

The pandemic has affected almost 74 million people worldwide as of 17 December 2020. This is the first study that attempts to examine the nexus between the confirmed COVID-19 cases, deaths, meteorological factors, and the air pollutant namely PM2.5 in six South Asian countries, from 1 March 2020 to 30 June 2020, using the advanced econometric techniques that are robust to heterogeneity across nations. Our findings confirm (1) a strong cross-sectional dependence and significant correlation between COVID-19 cases, deaths, meteorological factors, and air pollutant; (2) long-term relationship between all the meteorological variables, air pollutant, and COVID-19 death cases; (3) temperature, air pressure, and humidity exhibit a significant impact on the COVID-19 confirmed cases, while COVID-19 confirmed cases and air pollutant PM2.5 have a statistically significant impact on the COVID-19 death cases. In this way, the conclusion that high temperature and high humidity increase the transmission of the COVID-19 infections can also be applied to the regions with greater transmission rates, where the minimum temperature is mostly over 21 °C and humidity ranges around 80% for months. From the findings, it is evident that majority of the meteorological factors and air pollutant PM2.5 exhibit significant negative and positive effects on the number of COVID-19 confirmed cases and death cases in the six countries under study. Air pollutant PM 2.5 provides more particle surface for the virus to stick and get transported longer distances. Hence, higher particulate pollution levels in the air increase COVID-19 transmission in these six South Asian countries. This information is vital for the government and public health authorities in formulating relevant policies. The study contributes both practically and theoretically to the concerned field of pandemic management. [ABSTRACT FROM AUTHOR]

Since the beginning of 2020, COVID-19 has been the biggest public health crisis in the world. To help develop appropriate public health measures and deploy corresponding resources, many governments have been actively tracking COVID-19 in real time within their jurisdictions. However, one of the key unresolved issues is whether COVID-19 was distributed differently among different age groups and between the two sexes in the ongoing pandemic. The objectives of this study were to use publicly available data to investigate the relative distributions of COVID-19 cases, hospitalizations, and deaths among age groups and between the sexes throughout 2020; and to analyze temporal changes in the relative frequencies of COVID-19 for each age group and each sex. Fifteen countries reported age group and/or sex data of patients with COVID-19. Our analyses revealed that different age groups and sexes were distributed differently in COVID-19 cases, hospitalizations, and deaths. However, there were differences among countries in both their age group and sex distributions. Though there was no consistent temporal change across all countries for any age group or either sex in COVID-19 cases, hospitalizations, and deaths, several countries showed statistically significant patterns. We discuss the potential mechanisms for these observations, the limitations of this study, and the implications of our results on the management of this ongoing pandemic. [ABSTRACT FROM AUTHOR]

Background: Serosurveillance study focusing on antibodies against SARS-CoV2 among the Covid19 cases can add value in the scientific knowledge & help in formulating valid predictions regarding immunity status in the post-covid period. Objectives: To estimate seropositivity among covid19 cases and to identify various factors affecting seropositivity. Methods: During second half of October 2020, a population based serosurvey on Covid19 cases was carried out in Ahmedabad. Covid-Kavach test kits were used and estimated seroprevalence was compared with available demographic and covid19 case related parameters to identify factors affecting seropositivity in the post-covid period. Simple proportions and Z-test were used as appropriate. Results: As on October 2020, the sero-positivity among Covid19 cases in Ahmedabad was 54.51% [95% Confidence Interval (CI) 52.14-56.86%]. Females have higher positivity (54.78%) as compared to males (54.30%) but the difference was statistically not significant (Z=0.19, P=0.84). Among children and elderly, the positivity is high and from young adults to elderly the seropositivity has an increasing trend. Severity of clinical illness and longer duration of hospitalization are associated with higher seropositivity. Conclusion: With 54.51% seropositivity among covid19 cases, it is clear that all the covid19 cases may not have developed IgG antibodies, have undetectable level or might have disappeared during the post- covid period. Comparison of seropositivity with age group and clinical case details clearly suggest close correlation with the severity of clinical symptoms. The seronegative cases indicate the need for further in-depth scientific research to identify the factors affecting immunity and to uncover the reasons behind the same. [ABSTRACT FROM AUTHOR]

The US government imposed two travel restriction policies to prevent the spread of the COVID-19 but may have funneled asymptomatic air travelers to selected major airports and transportation hubs. Using the most recent JHU COVID-19 database, American Community Survey, Airport and Amtrak data form Bureau of Transportation Statistics from 3132 US counties we ran negative binomial regressions and Cox regression models to explore the associations between COVID-19 cases and death rates and proximity to airports, train stations, and public transportation. Counties within 25 miles of an airport had 1.392 times the rate of COVID-19 cases and 1.545 times the rate of COVID-19 deaths in comparison to counties that are more than 50 miles from an airport. More effective policies to detect and isolate infected travelers are needed. Policymakers and officials in transportation and public health should collaborate to promulgate policies and procedures to protect travelers and transportation workers from COVID-19. • Number of COVID-19 cases and deaths are positively related to proximity to and passenger volume of the nearest airport. • Length of time until first COVID-19 cases and deaths are positively related to proximity to nearest international airport and passenger volume. • Numbers of deaths and cases were positively correlated with the number of airports, number of train stations, and the percentage of adults using public transportation. [ABSTRACT FROM AUTHOR]

This article aims at answering the following questions: (1) What is the influence of age structure on the spread of coronavirus disease 2019 (COVID-19)? (2) What can be the impact of stringency policy (policy responses to the coronavirus pandemic) on the spread of COVID-19? (3) What might be the quantitative effect of development levelincome and number of hospital beds on the number of deaths due to the COVID-19 epidemic? By employing the methodologies of generalized linear model, generalized moments method, and quantile regression models, this article reveals that the shares of median age, age 65, and age 70 and older population have significant positive impacts on the spread of COVID-19 and that the share of age 70 and older people in the population has a relatively greater influence on the spread of the pandemic. The second output of this research is the significant impact of stringency policy on diminishing COVID-19 total cases. The third finding of this paper reveals that the number of hospital beds appears to be vital in reducing the total number of COVID-19 deaths, while GDP per capita does not affect much the level of deaths of the COVID-19 pandemic. Finally, this article suggests some governmental health policies to control and decrease the spread of COVID-19.

This study modelled the reported daily cumulative confirmed, discharged and death Coronavirus disease 2019 (COVID-19) cases using six econometric models in simple, quadratic, cubic and quartic forms and an autoregressive integrated moving average (ARIMA) model. The models were compared employing R-squared and Root Mean Square Error (RMSE). The best model was used to forecast confirmed, discharged and death COVID-19 cases for October 2020 to February 2021. The predicted number of confirmed and death COVID-19 cases are alarming. Good planning and innovative approaches are required to prevent the forecasted alarming infection and death in Ivory Coast. The applications of findings of this study will ensure that the COVID-19 does not crush the Ivory Coast’s health, economic, social and political systems.

Countries around the world are announcing stimulus packages in response to the COVID 19 pandemic. This research attempts to measure the extent and progress of stimulus packages by proposing a multidimensional index that standardizes governments' economic responses and allows us to examine the differences in economic policies from country to country. We apply the Euclidean distance formula to develop the new index and then identify the determinants of the economic stimulation of COVID-19 through beta-regression. The results show that Chile, Switzerland, Croatia, Sweden and the Netherlands responded more strongly to the COVID-19 pandemic, while the remaining countries responded slightly to the pandemic. Empirical results also indicate that most countries increased COVID-19 economic support, although not significantly. Finally, the results of the beta regression show that the median age of the population, the number of hospitals, beds per capita, the number of total COVID-19 cases, GDP, health care expenditure and the index of the severity of the government's response is significantly related to the level of the countries' stimulus packages.

This article aims at answering the following questions: (1) What is the influence of age structure on the spread of coronavirus disease 2019 (COVID-19)? (2) What can be the impact of stringency policy (policy responses to the coronavirus pandemic) on the spread of COVID-19? (3) What might be the quantitative effect of development levelincome and number of hospital beds on the number of deaths due to the COVID-19 epidemic? By employing the methodologies of generalized linear model, generalized moments method, and quantile regression models, this article reveals that the shares of median age, age 65, and age 70 and older population have significant positive impacts on the spread of COVID-19 and that the share of age 70 and older people in the population has a relatively greater influence on the spread of the pandemic. The second output of this research is the significant impact of stringency policy on diminishing COVID-19 total cases. The third finding of this paper reveals that the number of hospital beds appears to be vital in reducing the total number of COVID-19 deaths, while GDP per capita does not affect much the level of deaths of the COVID-19 pandemic. Finally, this article suggests some governmental health policies to control and decrease the spread of COVID-19. [ABSTRACT FROM AUTHOR]

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This article presents five select cases of small and medium-sized enterprises (SMEs) from China, India, Bahrain, and the US, grouped into three archetypes namely the Pandemic Warrior, the Survivor and the Digital Native, based on the fundamental nature of their business so as to highlight the unique challenges faced by each of them as they pass through the COVID-19 crisis. The strategies adopted by these firms to cope with the challenges faced are also presented. The lessons distilled from the cases are used to derive a conceptual framework that would guide SMEs toward a pandemic readiness audit, a process that would enable them to systematically evaluate their preparedness to face a pandemic. Additionally, we present a list of suggested audit questions that would help SMEs to assess the vulnerabilities of their key business components. The rationale is that such an audit would equip SMEs to face yet another pandemic crisis as well as help them in preparing for the emerging post-COVID new normal. [ABSTRACT FROM AUTHOR]