Identifying and evaluating COVID-19 effects on short-term statistics

The economic downturn due to lockdown measures at the beginning of the COVID-19 crisis raised the question whether any adaptations to the short-term statistics (STS) were needed to ensure accurate and relevant output. We limit ourselves to STS on turnover and related variables like volume of production. We looked into the different stages of the production process - from data collection to output - and anticipated a number of potential lockdown effects. With respect to output relevance, there was an increased interest in faster and specific output. With respect to the output accuracy, we took measures to check whether the anticipated effects really occurred and measures to mitigate the consequences. Examples of such measures are the calculation of an additional editing score function, alternative imputations and extensions of the regular analysis step. In this paper we give an overview of the anticipated effects, the subsequent measures that we took, we evaluate to what extent the anticipated effects occurred in practice and we mention some unforeseen effects. We end this paper by discussing to what extent the developed measures are also useful to keep after the economy has recovered. © 2023 - IOS Press. All rights reserved.

Quantification of oil and gas methane emissions in the Delaware and Marcellus basins using a network of continuous tower-based measurements

According to the United States Environmental Protection Agency (US EPA), emissions from oil and gas infrastructure contribute 30 % of all anthropogenic methane (CH4) emissions in the US. Studies in the last decade have shown emissions from this sector to be substantially larger than bottom-up assessments, including the EPA inventory, highlighting both the increased importance of methane emissions from the oil and gas sector in terms of their overall climatological impact and the need for independent monitoring of these emissions. In this study we present continuous monitoring of regional methane emissions from two oil and gas basins using tower-based observing networks. Continuous methane measurements were taken at four tower sites in the northeastern Marcellus basin from May 2015 through December 2016 and five tower sites in the Delaware basin in the western Permian from March 2020 through April 2022. These measurements, an atmospheric transport model, and prior emission fields are combined using an atmospheric inversion to estimate monthly methane emissions in the two regions. This study finds the mean overall emission rate from the Delaware basin during the measurement period to be 146–210 Mg CH4 h-1 (energy-normalized loss rate of 1.1 %–1.5 %, gas-normalized rate of 2.5 %–3.5 %). Strong temporal variability in the emissions was present, with the lowest emission rates occurring during the onset of the COVID-19 pandemic. Additionally, a synthetic model–data experiment performed using the Delaware tower network shows that the presence of intermittent sources is not a significant source of uncertainty in monthly quantification of the mean emission rate. In the Marcellus, this study finds the overall mean emission rate to be 19–28 Mg CH4 h-1 (gas-normalized loss rate of 0.30 %–0.45 %), with relative consistency in the emission rate over time. These totals align with aircraft top-down estimates from the same time periods. In both basins, the tower network was able to constrain monthly flux estimates within ±20 % uncertainty in the Delaware and ±24 % uncertainty in the Marcellus. The results from this study demonstrate the ability to monitor emissions continuously and detect changes in the emissions field, even in a basin with relatively low emissions and complex background conditions.

Do the macro and global economic factors drive the nonperforming loans in GCC economies?

PurposeThe current study aims to investigate the determinants of nonperforming loans (NPLs) in the GCC economies during the period spanning 2000 to 2018. It also examines whether the worldwide financial crisis of 2007–2008, which brought the issue of non–performing loans to the greater attention of academics and policymakers, had a substantial impact on NPLs in this region.Design/methodology/approachThe sample consists of 53 conventional banks from GCC countries, and the basic data for the study is obtained from various sources such as Bankscope, IMF World Economic Outlook, World Bank and Chicago Board of Options Exchange Market Volatility Index. The estimations were done by dynamic panel data regression modeling using system generalized methods of moments.FindingsThe findings reveal that both, the non-oil real GDP growth rate and inflation have favorable effects on NPLs. On the other hand, domestic credit to the private sector and the volatility index have an adverse effect on NPLs. Furthermore, the period-wise analysis shows that the relevance and significance of the determinants of NPLs vary between the precrisis and postcrisis periods. It is also reflected through the intercept dummy, which is found to be significant, indicating that the financial crisis, as a global economic factor, had a significant impact on NPLs. A number of robustness tests are applied, which indicate that the results are mostly robust and consistent in terms of the significance of the explanatory variables and the direction of their relationship with the dependent variable.Practical implicationsPolicymakers and bank authorities must strive to maintain a healthy economy and implement macroprudential policies to improve the financial stability of banks and reduce credit risk.Originality/valueTo the best of the authors' knowledge, this is likely the first study that empirically investigates the influence of the financial crisis on NPLs in the context of GCC economies. In addition, the research spans 19 years to produce more conclusive results.

Estimating the number of undetected COVID-19 cases among travellers from mainland China [version 1, peer review: awaiting peer review]

Background: Since the start of the COVID-19 epidemic in late 2019, there have been more than 152 affected regions and countries with over 110,000 confirmed cases outside mainland China. Methods: We analysed COVID-19 cases among travellers from mainland China to different regions and countries, comparing the region- and country-specific rates of detected and confirmed cases per flight volume to estimate the relative sensitivity of surveillance in different regions and countries. Results: Although travel restrictions from Wuhan City and other cities across China may have reduced the absolute number of travellers to and from China, we estimated that more than two thirds (70%, 95% CI: 54% - 80%, compared to Singapore;75%, 95% CI: 66% - 82%, compared to multiple countries) of cases exported from mainland China have remained undetected. Conclusions: These undetected cases potentially resulted in multiple chains of human-to-human transmission outside mainland China.

Benchmarking of COVID-19 testing facilities: a case in the Philippines

PurposeWith the aggressive movement towards testing for COVID-19 across the globe, this study aims to shed light on how testing facilities perform in an operational perspective.Design/methodology/approachWith 102 testing facilities in the Philippines, the relative efficiencies of each facility are quantified using a data envelopment analysis technique. Afterwards, a best-worst method was conducted to assign priority weights to each testing facility.FindingsResults show that the proposed approach effectively prioritizes testing facilities that most likely have high utilization.Research limitations/implicationsThe findings in this study would be significant to the literature in a number of respects. For one, it reveals results that would stimulate the interest among scholars in a wide variety of disciplines such as management, data mining, policymaking, decision science and epidemiology, among others.Originality/valueThis study differs from previous works in a number of respects, particularly, in that to the best of the authors' knowledge, this is the first study to examine the relative efficiencies of COVID-19 testing facilities.

Experiences related to the COVID-19 pandemic among U.S. physicians in office-based settings, 2020-2021

Objective-To assess final estimates of physician experiences related to COVID-19 and to compare preliminary estimates used in NCHS early-release dashboards with final estimates in this report. Methods-Physicians interviewed in periods 3 and 4 (December 15, 2020, through May 5, 2021) of the 2020 National Ambulatory Medical Care Survey (NAMCS) were asked a series of questions about experiences related to COVID-19 (n = 422). This report presents final nationally representative estimates for selected measures of COVID-19-related experiences among physicians in the United States and compares preliminary and final estimates for these measures. Results-Between September 2020 and May 2021, 31.1% of office-based physicians in the United States experienced shortages of personal protective equipment, and 38.4% of physicians had to turn away COVID-19 patients or refer them elsewhere for care. The percentage of physicians using telemedicine for patient care increased from 43.1% before the pandemic to 88.4% after the start of the pandemic. No statistically significant differences were seen between preliminary and final estimates for the measures assessed in this report. Conclusions-By making changes to NAMCS partway through the survey year, the National Center for Health Statistics was able to produce nationally representative estimates of physician experiences related to an emerging health threat, the COVID-19 pandemic. Additionally, the similarity between preliminary and final estimates for measures of interest provides evidence of the value of developing preliminary earlyrelease estimates.

Priorities for strengthening key revenue sources in Asia

This paper discusses the evolution of key taxes in the past 20 years in developing Asia and fiscal challenges that these countries face in light of the coronavirus disease (COVID-19) pandemic. It presents estimates of tax capacity and tax potential, and discusses the productivity of key taxes in the region. The paper finds that developing Asia has potential to raise more revenues-of up to 4% of gross domestic product on average. While corporate income tax productivity is high vis-a-vis other regions, the same does not apply to personal income tax or the value-added tax. There is potential to raise more revenues by improving the compliance and design of the value-added tax. It is important to ensure that the tax systems in developing Asia become more progressive with expansion of personal income and property taxes. Increased allocations and better targeting of social spending would help offset some of the regressivity stemming from indirect taxes. An important source of revenue leakage is tax expenditures granted by countries in the region.

COVID-19 forecasting and investigating the impact of the lockdown in India

This study aimed to forecast the growth pattern of COVID-19 in India and evaluate the impact of the lockdown on its transmission and mortality. Different models were compared for short-term forecasts, and it was found that the hybrid autoregressive integrated moving average (ARIMA) with error-remodelling using fast Fourier transform produced more accurate estimates. Furthermore, the study utilized data from the first phase of the lockdown, which generated more precise predictions. The impact analysis revealed a significant trend break on 3 March for confirmed cases and 11 March for deaths. Overall, the study highlights the effectiveness of the lockdown measures in reducing the spread of COVID-19 in India and emphasizes the need for continued monitoring and surveillance to control its transmission.

Families' Job Characteristics and Economic Self-Sufficiency: Differences by Income, Race-Ethnicity, and Nativity

Policy debates about whether wages and benefits from work provide enough resources to achieve economic self-sufficiency rely on data for workers, not working families. Using data from the Current Population Survey, we find that almost two-thirds of families working full time earn enough to cover a basic family budget, but that less than a quarter of low-income families do. A typical low-income full-time working family with wages below a family budget would need to earn about $11.00 more per hour to cover expenses. This wage gap is larger for black, Hispanic, and immigrant families. Receipt of employer-provided benefits varies—health insurance is more prevalent than pension plans—and both are less available to low-income families, and black, Hispanic, and immigrant working families. Findings suggest that without policies to decrease wage inequality and increase parents' access to jobs with higher wages and benefits, child opportunity gaps by income, race-ethnicity, and nativity will likely persist.

Large-eddy simulations to define building-specific similarity relationships for natural ventilation flow rates

Natural ventilation can play an important role towards preventing the spread of airborne infections in indoor environments. However, quantifying natural ventilation flow rates is a challenging task due to significant variability in the boundary conditions that drive the flow. In the current study, we propose and validate an efficient strategy for using computational fluid dynamics to assess natural ventilation flow rates under variable conditions, considering the test case of a single-room home in a dense urban slum. The method characterizes the dimensionless ventilation rate as a function of the dimensionless ventilation Richardson number and the wind direction. First, the high-fidelity large-eddy simulation (LES) predictions are validated against full-scale ventilation rate measurements. Next, simulations with identical Richardson numbers, but varying dimensional wind speeds and temperatures, are compared to verify the proposed similarity relationship. Last, the functional form of the similarity relationship is determined based on 32 LES. Validation of the surrogate model against full-scale measurements demonstrates that the proposed strategy can efficiently inform accurate building-specific similarity relationships for natural ventilation flow rates in complex urban environments.

Feature-Modified SEIR Model for Pandemic Simulation and Evaluation of Intervention Approaches

SEIR (susceptible-exposed-infected-recovered) model has been widely used to study infectious disease dynamics. For instance, there have been many applications of SEIR analyzing the spread of COVID to provide suggestions on pandemic/epidemic interventions. Nonetheless, existing models simplify the population, regardless of different demographic features and activities related to the spread of the disease. This paper provides a comprehensive SEIR model to enhance the prediction quality and effectiveness of intervention strategies. The new SEIR model estimates the exposed population via a new approach involving health conditions (sensitivity to disease) and social activity level (contact rate). To validate our model, we compare the estimated infection cases via our model with actual confirmed cases from CDC and the classic SEIR model. We also consider various protocols and strategies to utilize our modified SEIR model on many simulations and evaluate their effectiveness. © 2022 IEEE.

Cost of treating coronavirus disease 2019 (COVID-19) patients in hospitals and influencing factors: a systematic literature review

Background: Increasing beds for COVID-19 patients is not a simple matter for hospitals because hospitals cannot directly increase the number of existing beds due to limited facilities, infrastructure, equipment, and resources. Careful calculations are needed in terms of preparing the room and treating COVID patients, especially the estimated costs needed to treat COVID patients in the hospital. This expenditure is important so that it can be an illustration of the hospital how much expenditure is needed. This study aimed to determine the number of costs incurred for treating COVID-19 patients in hospitals and the factors that influence these costs. Methods: This study was conducted by systematic literature review using the PRISMA statement conducted May-June 2021. An article search was conducted on Pubmed, Scopus, Proquest, and Google Scholar with the inclusion criteria, namely research related to the cost of treating COVID-19 patients in hospitals and the factors that influence the amount of these costs. Results: A systematic search obtained six articles. Studies vary greatly in study design and perspective included in the cost category. Estimated costs for COVID-19 care range from $63/day to $2,990/day. Factors that affect the cost of care include age, previous medical history, degree of infection or severity of COVID-19, length of stay, place of care, and need for a ventilator. Conclusion: There is a considerable economic burden associated with the incident of COVID-19. Several factors affect the cost of COVID-19, namely the length of the treatment period and the need for intensive rooms and ventilators.

Near-real-time CO2 fluxes from CarbonTracker Europe for high-resolution atmospheric modeling

We present the CarbonTracker Europe High-Resolution (CTE-HR) system that estimates carbon dioxide (CO2) exchange over Europe at high resolution (0.1 × 0.2∘) and in near real time (about 2 months' latency). It includes a dynamic anthropogenic emission model, which uses easily available statistics on economic activity, energy use, and weather to generate anthropogenic emissions with dynamic time profiles at high spatial and temporal resolution (0.1×0.2∘, hourly). Hourly net ecosystem productivity (NEP) calculated by the Simple Biosphere model Version 4 (SiB4) is driven by meteorology from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) dataset. This NEP is downscaled to 0.1×0.2∘ using the high-resolution Coordination of Information on the Environment (CORINE) land-cover map and combined with the Global Fire Assimilation System (GFAS) fire emissions to create terrestrial carbon fluxes. Ocean CO2 fluxes are included in our product, based on Jena CarboScope ocean CO2 fluxes, which are downscaled using wind speed and temperature. Jointly, these flux estimates enable modeling of atmospheric CO2 mole fractions over Europe.We assess the skill of the CTE-HR CO2 fluxes (a) to reproduce observed anomalies in biospheric fluxes and atmospheric CO2 mole fractions during the 2018 European drought, (b) to capture the reduction of anthropogenic emissions due to COVID-19 lockdowns, (c) to match mole fraction observations at Integrated Carbon Observation System (ICOS) sites across Europe after atmospheric transport with the Transport Model, version 5 (TM5) and the Stochastic Time-Inverted Lagrangian Transport (STILT), driven by ECMWF-IFS, and (d) to capture the magnitude and variability of measured CO2 fluxes in the city center of Amsterdam (the Netherlands).We show that CTE-HR fluxes reproduce large-scale flux anomalies reported in previous studies for both biospheric fluxes (drought of 2018) and anthropogenic emissions (COVID-19 pandemic in 2020). After applying transport of emitted CO2, the CTE-HR fluxes have lower median root mean square errors (RMSEs) relative to mole fraction observations than fluxes from a non-informed flux estimate, in which biosphere fluxes are scaled to match the global growth rate of CO2 (poor person's inversion). RMSEs are close to those of the reanalysis with the CTE data assimilation system. This is encouraging given that CTE-HR fluxes did not profit from the weekly assimilation of CO2 observations as in CTE.We furthermore compare CO2 concentration observations at the Dutch Lutjewad coastal tower with high-resolution STILT transport to show that the high-resolution fluxes manifest variability due to different emission sectors in summer and winter. Interestingly, in periods where synoptic-scale transport variability dominates CO2 concentration variations, the CTE-HR fluxes perform similarly to low-resolution fluxes (5–10× coarsened). The remaining 10 % of the simulated CO2 mole fraction differs by >2 ppm between the low-resolution and high-resolution flux representation and is clearly associated with coherent structures ("plumes”) originating from emission hotspots such as power plants. We therefore note that the added resolution of our product will matter most for very specific locations and times when used for atmospheric CO2 modeling. Finally, in a densely populated region like the Amsterdam city center, our modeled fluxes underestimate the magnitude of measured eddy covariance fluxes but capture their substantial diurnal variations in summertime and wintertime well.We conclude that our product is a promising tool for modeling the European carbon budget at a high resolution in near real time. The fluxes are freely available from the ICOS Carbon Portal (CC-BY-4.0) to be used for near-real-time monitoring and modeling, for example, as an a priori flux product in a CO2 data assimilation system. The data are available at 10.18160/20Z1-AYJ2 .

Estimation of Transmission Rate and Recovery Rate of SIR Pandemic Model Using Kalman Filter

COVID-19 is a global pandemic that significantly impacts all aspects. The number of victims who died makes this disease so terrible. Various policies continue to be pursued to reduce the spread and impact of COVID-19. The spread of a disease can be modeled in differential equation modeling. This differential equation modeling is known as the SIR Model. A differential equation can be expressed in a state-space model. The state-space model is a model that is widely used to design a modern control system. This research carried out the transmission rate and recovery rate estimates in the SIR pandemic model. Estimation of the transmission rate and recovery rate in this study poses a challenge to the value of the number of people confirmed as infected. The experimental result shows that the transmission and recovery rates can be estimated using the data for the infected and recovered persons. Estimates of infected and recovered people were conducted using the Kalman Filter.

THE EXTRACTIVE REGIONS OF THE RUSSIAN ARCTIC dURING THE PANdEMIC: ECONOMIC ANd STATISTICAL ASSESSMENTS

The comparative dynamics of the main indicators characterizing production, retail trade, labor market of the extractive regions of the Russian Arctic was clarified by economic-statistical methods in the context of the all-Russian situation in the development of the crisis generated by the COVID-19 pandemic. We confirmed the hypothesis of the research that the regions of the Russian Arctic show distinctive regional specifics of impact on the crisis, typically demonstrating greater resilience of industrial production (Nenets AO is an exception), labor markets and retail trade as compared to the national situation in the context of the COVID-19 pandemic crisis. This is explained not only by the large-scale support of the corporate sector for socio-economic processes in the territories of its presence, but also by the phenomenon of the Arctic invariant (a group of invariable factors of the Arctic - the simplicity of the economy;weak development of small businesses;underdeveloped trade and services sector in comparison with the general Russian situation, etc.). © T.P. Skufina, S.V. Baranov, 2022.

Identifying and evaluating COVID-19 effects on short-term statistics

The economic downturn due to lockdown measures at the beginning of the COVID-19 crisis raised the question whether any adaptations to the short-term statistics (STS) were needed to ensure accurate and relevant output. We limit ourselves to STS on turnover and related variables like volume of production. We looked into the different stages of the production process – from data collection to output – and anticipated a number of potential lockdown effects. With respect to output relevance, there was an increased interest in faster and specific output. With respect to the output accuracy, we took measures to check whether the anticipated effects really occurred and measures to mitigate the consequences. Examples of such measures are the calculation of an additional editing score function, alternative imputations and extensions of the regular analysis step. In this paper we give an overview of the anticipated effects, the subsequent measures that we took, we evaluate to what extent the anticipated effects occurred in practice and we mention some unforeseen effects. We end this paper by discussing to what extent the developed measures are also useful to keep after the economy has recovered. [ FROM AUTHOR]

CARES and crime

Purpose>The US signed the Coronavirus Aid, Relief, and Economic Security (CARES) Act in March 2020 to alleviate the harsh economic effects of the pandemic and related shutdowns. A substantial part of the bill expanded and increased unemployment insurance payments, where a growing area of research estimates strong anti-poverty effects. The authors examine the effect of these policies on crime.Design/methodology/approach>The authors use new event study and difference-in-differences techniques to estimate the effect of increasing unemployment insurance payments on property crime and violent crime. Then, the authors estimate the effect of expanded unemployment qualification programs on crime. The authors use a rich set of controls including unemployment, contemporaneous policies and mobile device tracking data to estimate the degree to which people stayed at home.Findings>They find that increasing unemployment insurance payments decreased crime by 20%, driven by a 24% decrease in property crime. The authors also find suggestive evidence that expanding unemployment qualifications decreases crime.Practical implications>The authors find a new and substantial benefit of expanded unemployment insurance beyond their antipoverty effects.Originality/value>To the authors' knowledge, this is the first study that directly examines the impact of the CARES Act on crime.

Estimating Societal Costs Associated With Vision Loss And Delayed Cataract Surgery: The Potential Impact Of The COVID-19 Pandemic

IntroductionCataract surgery is the most commonly performed surgical procedure in the UK (approx. 472,000 annually). The suspension of interventions due to the COVID-19 pandemic, has had a devastating impact on patients' access to care. In the UK a complete cessation of elective cataract surgery during the crisis has been an unfortunate reality and encompassed a 14 week hiatus to services in the National Health Service. Patients on prolonged waiting lists may experience negative outcomes during the wait period, including vision loss, increased risk of falls, and ultimately, poorer health-related quality of life (HRQoL). The objective of this research was to estimate the potential societal costs associated with vision-loss related to prolonged waiting times for cataract surgery, as a consequence of COVID-19 in the UK.MethodsIn this analysis, we present estimates relating to two cohorts: a hypothetical cohort of 1,000 cataract surgeries and quarterly estimates of cataract surgeries in the UK. Quarterly estimates (n=122,969) were chosen to reflect a suspension of cataract surgeries for 14 weeks during the COVID-19 crisis. UK cataract surgery numbers were attained from EUROSTAT. Estimates for decreasing visual acuity for those waiting for surgery were attained from the literature, as were the cost estimates associated with cataract-related sight-loss, which were made up of direct, indirect and intangible costs. Five scenarios (at 20% intervals) were simulated for the cost estimates, assuming from 20 percent to 100 percent clearing of waiting lists.ResultsFor cohort 1 (1,000 patients), the societal costs associated patients remaining on waiting list for one year, ranged between GBP 237,765 (EUR 279,533) (20% of patients remain untreated) to GBP 1.18m (EUR 1.39m) (100% remain untreated). For cohort 2 (n=122,969) cost estimates are in the region of GBP 29.23m to GBP 146.18m (EUR 34.36m to EUR 171.73m). Estimates consist of direct (15.6%), indirect (28.7%) and intangible costs (55.6%).ConclusionsCataract surgery is a sight saving procedure and its impact on HRQoL is overwhelmingly positive. Prolonged waiting times for cataract patients due to COVID-19 is likely to be associated with significant societal costs.

Relaxation of the parameter independence assumption in the bootComb R package

BackgroundThe bootComb R package allows researchers to derive confidence intervals with correct target coverage for arbitrary combinations of arbitrary numbers of independently estimated parameters. Previous versions (<1.1.0) of bootComb used independent bootstrap sampling and required that the parameters themselves are independent—an unrealistic assumption in some real-world applications.FindingsUsing Gaussian copulas to define the dependence between parameters, the bootComb package has been extended to allow for dependent parameters.ImplicationsThe updated bootComb package can now handle cases of dependent parameters, with users specifying a correlation matrix defining the dependence structure. While in practice it may be difficult to know the exact dependence structure between parameters, bootComb allows running sensitivity analyses to assess the impact of parameter dependence on the resulting confidence interval for the combined parameter.AvailabilitybootComb is available from the Comprehensive R Archive Network (https://CRAN.R-project.org/package=bootComb).

Monitoring the newly infected cases of COVID-19 data weekly: A Survival Data Analysis (SDA) perspective

This paper attempts to fit the best survival model distribution for the Malaysian COVID-19 new infections experience of Wave I/II and Wave III using the well-known Survival Data Analysis (SDA) procedures. The purpose of fitting such models is to reduce the complexity and frequency of the COVID-19 new infections data into a single measure of scale and shape parameters to enable monitoring of weekly trends, undertake short term forecasts and estimate duration when the virality will be contained. The analysis showed a Weibull distribution is the best statistical fit for Malaysia’s new infections COVID-19 data. The estimates of scale and shape parameters for Wave I/II was 0.05901 and 2.48956 and for Wave III was 0.06463 and 2.5693, respectively. Much higher hazard force in Wave III is due to weaker control in the implementation of cordon sanitaire measures imposed in containing the virality spread. Based on the survival function the short-term forecasts showed that the number of new infections projected to decline from 23,282 cases in 28th week to 22,017 cases in 31st week. Similarly, based on the cumulative hazard function the duration estimated for containing the virality completely projected to stretch over another 19.6 weeks under the prevailing conditions. © 2021 – IOS Press. All rights reserved.