A review of some Covid-19 pandemic numbers in European Union, Canada, and Mexico

The rapidity and extent of Covid-19 infections have shown how a phenomenon that initially seemed geographically circumscribed quickly spread worldwide. In 2020, the spread of infection and the containment and management measures taken by local governments have been quite heterogeneous. Therefore, here we investigate the different ways of the spread of the infection in different areas, and specifically in Canada, Mexico, and the European Union states. For this purpose, for each area, official data on infection in 2020 are used to depict, analyze, and compare the monthly contagion's curves and the Rt index, both in absolute and relative terms. © Springer Nature Switzerland AG 2023. All rights reserved.

Can AIS data improve the short-term forecast of weekly dry bulk cargo port throughput? - a machine-learning approach

This study examines the development of a machine-learning model to forecast weekly throughputs of dry bulk cargo in the short term based on automatic identification system (AIS) data. Specifically, the weekly amounts of iron ore exported from several major ports in Australia and Brazil in the latter half of 2019 are forecasted three weeks in advance using a long short-term memory model. We examine many variables extracted from AIS data, including the vessel position, speed, draught, and destination, as the input features of the model. Consequently, we develop a highly accurate forecasting model that uses four influential variables derived from AIS data, namely, vessel traffic around the target port and in the region, vessel traffic at major partner import ports, and vessel traffic at the target port during the past year. Finally, by forecasting the weekly port cargo throughputs in the first half of 2020, which was affected by the COVID-19 pandemic, the applicability of the model is confirmed, even for ports where the throughput fluctuates significantly. In particular, this study demonstrates that AIS data are beneficial not only as a real-time traffic database but also as a database containing various related explanatory variables, including historical vessel traffic. [ FROM AUTHOR] Copyright of Maritime Policy & Management is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Arima Models to Forecast Covid-19 in Kingdom of Saudi Arabia during the Interval (1-2021 to 1-2022) Weekly Using E-Views Program

The main objective of this research is to analyze the fundamental differences in the basic indicators of the emerging corona virus, COVID-19, especially the number of total cases and the number of deaths resulting from it in Kingdom of Saudi Arabia, in order to evaluate the precautionary measures taken by KSA. In this research, time series models were studied to predict the number of cases infected with COVID-19 that can be expected weekly in KSA during a period spanning a whole year using the numbers of weekly infections (WC) in KSA during the period from January 2021 to January 2022. The future values of injuries and deaths resulting from them were predicted using the time series method according to the current and previous values, and the E-Views statistical software package was used, which was specifically designed to process time series data. The study proved that there were statistically significant differences in the number of weekly infections with the corona virus, in addition to the presence of statistically significant differences in the number of weekly deaths resulting from the corona virus in Kingdom of Saudi Arabia. The study also demonstrated the existence of a statistically significant correlation between the number of weekly infections with the corona virus and the deaths resulting from it in Kingdom of Saudi Arabia. The automatic regression integrated moving average (ARIMA) model was used as one of the time series prediction methods and the prediction procedures were determined using the ARIMA model. The results of the analysis showed that the ARIMA(1, 2, 0) model gave the best results for prediction and data analysis. It is highly advised to maintain the social distancing with all safety measures.

Exploring excess mortality during the covid-19 pandemic with seasonal arima models

This chapter analyses the daily and the weekly deaths in Germany, Sweden and Spain between 2016 and 2019. It gives an estimate of the future number of deaths in 2020 in those countries, with a special focus on uncertainty, and thereby presents alternative models and methods for estimating the excess mortality in 2020, the year of the Covid-19 pandemic. Suitable seasonal auto-regressive integrated moving average (ARIMA) models are sought that allow the best possible fit to the available time series, in the sense that the properties of the resulting residual processes are compatible with those of white noise. It can be seen that only deaths in the age class 0-30 can be satisfactorily presented by a binomial mortality model. ARIMA is one of the most widely used forecasting methods for predicting univariate time series data. © ISTE Ltd 2022.

Weekly screening of youth male football players: a 14-week longitudinal investigation of interactions between groin pain and long lever adductor squeeze strength.

OBJECTIVES: To explore relationships between groin pain and adductor squeeze strength in male academy football players over a 14-week period. DESIGN: Longitudinal cohort study. METHODS: Weekly monitoring of youth male football players consisted of reporting groin pain and testing long lever adductor squeeze strength. Players who reported groin pain at any time during the study period were stratified into the "groin pain" group while players who did not report pain remained in the "no groin pain" group. Baseline squeeze strength was retrospectively compared between groups. Players that developed groin pain were examined via repeated measures ANOVA at four timepoints: baseline, last squeeze before pain, pain onset, and return to pain-free. RESULTS: 53 players were included (age 14.4 ±â€¯1.6 years). Baseline squeeze strength was not different between players in the "groin pain" (n = 29, 4.35 ±â€¯0.89 N/kg) versus "no groin pain" group (n = 24, 4.33 ±â€¯0.90 N/kg, p = 0.83). At a group level, players with no groin pain maintained similar adductor squeeze strength throughout 14 weeks (p > 0.05). Compared to baseline (4.33 ±â€¯0.90 N/kg), players with groin pain had decreased adductor squeeze strength at the last squeeze before pain (3.91 ±â€¯0.85 N/kg, p = 0.003) and at pain onset (3.58 ±â€¯0.78 N/kg, p < 0.001). Adductor squeeze strength at the point where pain subsided (4.06 ±â€¯0.95 N/kg) was not different from baseline (p = 0.14). CONCLUSIONS: Decreases in adductor squeeze strength manifest one-week prior to groin pain onset and further decrease at pain onset. Weekly adductor squeeze strength may be an early detector for groin pain in youth male football players.

Potential of TROPOMI for understanding spatio-temporal variations in surface NO2 and their dependencies upon land use over the Iberian Peninsula

In orbit since late 2017, the Tropospheric Monitoring Instrument (TROPOMI) is offering new outstanding opportunities for better understanding the emission and fate of nitrogen dioxide (NO2) pollution in the troposphere. In this study, we provide a comprehensive analysis of the spatio-temporal variability of TROPOMI NO2 tropospheric columns (TrC-NO2) over the Iberian Peninsula during 2018–2021, considering the recently developed Product Algorithm Laboratory (PAL) product. We complement our analysis with estimates of NOx anthropogenic and natural soil emissions. Closely related to cloud cover, the data availability of TROPOMI observations ranges from 30 %–45 % during April and November to 70 %–80 % during summertime, with strong variations between northern and southern Spain. Strongest TrC-NO2 hotspots are located over Madrid and Barcelona, while TrC-NO2 enhancements are also observed along international maritime routes close the strait of Gibraltar, and to a lesser extent along specific major highways. TROPOMI TrC-NO2 appear reasonably well correlated with collocated surface NO2 mixing ratios, with correlations around 0.7–0.8 depending on the averaging time.We investigate the changes of weekly and monthly variability of TROPOMI TrC-NO2 depending on the urban cover fraction. Weekly profiles show a reduction of TrC-NO2 during the weekend ranging from -10 % to -40 % from least to most urbanized areas, in reasonable agreement with surface NO2. In the largest agglomerations like Madrid or Barcelona, this weekend effect peaks not in the city center but in specific suburban areas/cities, suggesting a larger relative contribution of commuting to total NOx anthropogenic emissions. The TROPOMI TrC-NO2 monthly variability also strongly varies with the level of urbanization, with monthly differences relative to annual mean ranging from -40 % in summer to +60 % in winter in the most urbanized areas, and from -10 % to +20 % in the least urbanized areas. When focusing on agricultural areas, TROPOMI observations depict an enhancement in June–July that could come from natural soil NO emissions. Some specific analysis of surface NO2 observations in Madrid show that the relatively sharp NO2 minimum used to occur in August (drop of road transport during holidays) has now evolved into a much broader minimum partly de-coupled from the observed local road traffic counting;this change started in 2018, thus before the COVID-19 outbreak. Over 2019–2021, a reasonable consistency of the inter-annual variability of NO2 is also found between both datasets.Our study illustrates the strong potential of TROPOMI TrC-NO2 observations for complementing the existing surface NO2 monitoring stations, especially in the poorly covered rural and maritime areas where NOx can play a key role, notably for the production of tropospheric O3.

A linear mixed model to estimate COVID-19-induced excess mortality.

The Corona Virus Disease (COVID-19) pandemic has increased mortality in countries worldwide. To evaluate the impact of the pandemic on mortality, the use of excess mortality rather than reported COVID-19 deaths has been suggested. Excess mortality, however, requires estimation of mortality under nonpandemic conditions. Although many methods exist to forecast mortality, they are either complex to apply, require many sources of information, ignore serial correlation, and/or are influenced by historical excess mortality. We propose a linear mixed model that is easy to apply, requires only historical mortality data, allows for serial correlation, and down-weighs the influence of historical excess mortality. Appropriateness of the linear mixed model is evaluated with fit statistics and forecasting accuracy measures for Belgium and the Netherlands. Unlike the commonly used 5-year weekly average, the linear mixed model is forecasting the year-specific mortality, and as a result improves the estimation of excess mortality for Belgium and the Netherlands.

Predicting the weekly COVID-19 new cases using multilayer perceptron: An evidence from west Java, Indonesia

COVID-19 is a contagious disease caused by the coronavirus (SARS-CoV-2) that attacks the respiratory tract. On August 14th, 2021, 653,741 persons had been proven positive for COVID-19. The number of patients tends to increase as the number of COVID-19 cases grows. The more infected people, the more cases of COVID-19 there will be. The Bed Occupancy Ratio (BOR) in West Java reached an all-time high of 91.6 percent in June 2021, far exceeding the WHO recommendation of 60 percent, before gradually declining to 30.69 percent in August. Because of the new cases mentioned, the rate of spread of COVID-19 in West Java, the forecast of new cases is very strategic. The number of new cases in this study was predicted using a Multilayer Perceptron (MLP). The data used in this study were sourced from the COVID-19 Task Force. The data is the number of positive and new cases from 34 provinces in Indonesia from March 2nd, 2020, to August 14th, 2021. The results of the evaluation using test data on the number of active cases in the last 19 weeks, namely April 10th - August 14th, 2021, The MLP is accurate in forecasting the number of new cases 18 times for both forecast periods with APE < 15%, with the value MAPE, RMSE and MAE obtained were 5.52%, 1157,61, and 706.811. The results of this study can be helpful for the government as a reference in conditioning hospital bed capacity to deal with active COVID-19 cases in West Java in the next two weeks so that the hospital rejects no COVID-19 patients because the hospital is full. (c) 2022 by the authors;licensee Growing Science, Canada.

Creating a Comprehensive Pandemic Response to Decrease Hospitalist Burnout During COVID-19: Intervention vs Control Results in 2 Comparable Hospitals (HOSP-CPR).

BACKGROUND: Physician burnout increased during the COVID-19 pandemic. OBJECTIVE: To evaluate the effectiveness of a multimodal workplace intervention designed to reduce hospitalist burnout. DESIGN: Participants and setting: Our intervention group was composed of internal medicine hospitalists at Providence Portland Medical Center (64 providers including 58 physicians and 6 nurse practitioners). Our control was composed of internal medicine hospitalists at Providence St Vincent's Hospital (59 physicians and 6 nurse practitioners). MEASUREMENTS: Two surveys were given during, before, and after a 12-month intervention period (October 2020 and again in October 2021). Surveys included demographics, job satisfaction, the Maslach Burnout Inventory, the Pandemic Experiences Survey, and 2 questions about leaving the job. INTERVENTIONS: Three hospitalists designated as wellness warriors created weekly COVID group meetings, providing up-to-date information about COVID-19 infection rates, treatments, and work-flow changes. Discussions included coping and vaccine hesitancy, difficult case debriefs, and intensive care unit updates. Individual coaching was also offered. Meeting minutes were taken and sessions were recorded for asynchronous access. RESULTS: No site differences in burnout or job satisfaction were evident pre-intervention. Post-intervention, the intervention group reported 32% burnout while controls reported 56% (p = .024). Forty-eight percent of the intervention group reported high wellness support vs. 0% of the controls (< .001). Intervention participants attributed 44% of wellness support to Providence alone, vs. controls at 12% (< .001). Regressions controlling sex, work hours, experience, race, and children in the home showed the intervention's positive effects on burnout and job satisfaction remained significant (all p < .02). LIMITATIONS: For privacy reasons, all survey responses were anonymous, meaning that individual pre-post changes could not be tracked. CONCLUSION: We believe the intervention resulted in substantial burnout prevention and is feasible for adoption in most hospitals and clinics.

Did weekly economic index and volatility index impact US food sales during the first year of the pandemic?

We explore the impacts of economic and financial dislocations caused by COVID-19 pandemic shocks on food sales in the United States from January 2020 to January 2021. We use the US weekly economic index (WEI) to measure economic dislocations and the Chicago Board Options Exchange volatility index (VIX) to capture the broader stock market dislocations. We validate the NARDL model by testing a battery of models using the autoregressive distributed lags (ARDL) methodology (ARDL, NARDL, and QARDL specifications). Our study postulates that an increase in WEI has a significant negative long-term effect on food sales, whereas a decrease in WEI has no statistically significant (long-run) effect. Thus, policy responses that ignore asymmetric effects and hidden cointegration may fail to promote food security during pandemics.

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.

Peculiar COVID-19 effects in the Greater Tokyo Area revealed by spatiotemporal variabilities of tropospheric gases and light-absorbing aerosols

This study investigated the spatiotemporal variabilities in nitrogen dioxide (NO2), formaldehyde (HCHO), ozone (O3), and light-absorbing aerosols within the Greater Tokyo Area, Japan, which is the most populous metropolitan area in the world. The analysis is based on total tropospheric column, partial tropospheric column (within the boundary layer), and in situ observations retrieved from multiple platforms as well as additional information obtained from reanalysis and box model simulations. This study mainly covers the 2013–2020 period, focusing on 2020 when air quality was influenced by the coronavirus 2019 (COVID-19) pandemic. Although total and partial tropospheric NO2 columns were reduced by an average of about 10 % in 2020, reductions exceeding 40 % occurred in some areas during the pandemic state of emergency. Light-absorbing aerosol levels within the boundary layer were also reduced for most of 2020, while smaller fluctuations in HCHO and O3 were observed. The significantly enhanced degree of weekly cycling of NO2, HCHO, and light-absorbing aerosol found in urban areas during 2020 suggests that, in contrast to other countries, mobility in Japan also dropped on weekends. We conclude that, despite the lack of strict mobility restrictions in Japan, widespread adherence to recommendations designed to limit the COVID-19 spread resulted in unique air quality improvements.

Synergistic influence of air temperature and vaccination on COVID-19 transmission and mortality in 146 countries or regions.

OBJECTIVE: We aimed to determine the influence of vaccination and air temperature on COVID-19 transmission and severity. METHODS: The study data in 146 countries from January 6, 2020 to July 28, 2022 were aggregated into 19,856 weeks. Country-level weekly incidence, time-varying reproduction number (Rt), mortality, and infection-fatality ratio (IFR) were compared among groups of these weeks with different vaccination rates and air temperatures. RESULTS: Weeks with <15 °C air temperature and 60% vaccination showed the highest incidence (mean, 604; SD, 855; 95% CI, 553-656, unit, /100,000 persons; N = 1073) and the highest rate of weeks with >1 Rt (mean, 41.6%; SD, 1.49%; 95% CI, 39.2-45.2%; N = 1090), while weeks with >25 °C and <20% showed the lowest incidence (mean, 24; SD, 75; 95% CI, 22-26; N = 5805) and the lowest rate of weeks with >1 Rt (mean, 15.3%; SD, 0.461%; 95% CI, 14.2-16.2%; N = 6122). Mortality in weeks with <15 °C (mean, 2.1; SD, 2.8; 95% CI, 2.0-2.2, unit, /100,000 persons; N = 4365) was five times of the mortality in weeks with >25 °C (mean, 0.44; SD, 1; 95% CI, 0.41-0.46; N = 7741). IFR ranged between 2% and 2.6% (SD, 1.9%-2.4%; 95% CI, 2.0-2.7%) at < 20% vaccination level, 1.8% (SD, 2%-2.2%; 95% CI, 1.7-2.0%) at 20-60% vaccination level, and 0.7%-1% (SD, 1%-1.8%; 95% CI, 0.7-1.1%) at > 60% vaccination level and at all air temperatures (all P < 0.001). CONCLUSIONS: Vaccination was insufficient to mitigate the transmission since the significantly elevated weekly incidence and >1 Rt rate in weeks with high vaccination, while IFR was reduced by high vaccination. Countries with long-term low air temperature were affected by high transmission and high mortality.

Covid19 Hazard Index: A Spatiotemporal Risk Forecast Tool

Background: In each geographic region, risk of new cases of COVID19 are driven by internal factors such as agent, host, and environment characteristics, as well as external factors, such as population mobility and cross border transmission of disease. COVID19 control measures are best implemented when local governments and health teams are well aware of these internal and external risks. These risks are dynamic in nature and hence need to be reviewed at regular intervals. The study conducted to develop a composite spatiotemporal Hazard Index comprising of three factors – presence of susceptible population, population density and presence of active cases with corresponding growth rates, to rank areas within an administrative boundary by their fortnightly risk of active COVID19 cases. Methods: Using Principal Component Analysis, the weights of each of these factors were determined and applied to transformed values of factors in the districts of Gujarat state for months of January to July 2021. Hazard Index thus obtained was used to rank the districts. Results: Spearman correlation between the Hazard Index and number of active cases 15 days later was moderate and significant (p<0.01) throughout the study period. Conclusion: Hazard Index can predict Districts at highest risk of active cases in the given time period. These districts with high Hazard Index would require different control measures, depending on the factor that resulted in higher index value. © 2022, MedSci Publications. All rights reserved.

A weekly-diary study of students' schoolwork motivation and parental support.

BACKGROUND: Parental support plays an important role in children's schoolwork motivation and may have been even more important during the first UK COVID-19 pandemic lockdown because all schoolwork was completed at home. When examining the effect of parental support on children's schoolwork motivation, research has typically focused on comparing families with each other (i.e., difference between families). In reality, however, the effect unfolds as a transactional, bidirectional process between parents and children over time (i.e., a within family process). This research trend can result in imprecise conclusions about the association between parental support and schoolwork motivation. OBJECTIVES: We examined bidirectional effects of parental schoolwork support and children's schoolwork motivation at both the between-family and within-family level. METHODS: This study reports findings from a weekly-diary study conducted during the first UK COVID-19 school lockdown. Cross-lagged within and between multilevel modelling was used to analyse data from UK secondary school students (N = 98) in Years 7-9. RESULTS: Between-family results show no evidence of association between motivation and parental support. Within-family results indicate that higher motivation (assessed as higher expectations of success) predicted more support from parents. However, in contrast with predictions, weekly levels of parental support did not predict children's weekly fluctuations in motivation. CONCLUSIONS: Within-family results were not consistent with between-family results. This study is novel in showing that child-driven effects appear to be important in eliciting parental support within families over time.

Preface: How hydrologists helped respond to the SARS-CoV-2 pandemic

There are a range of spatial scales addressed in the papers, from the individual building scale to the cargo ship (Coxon et al., 'Exploring opportunities for sewage testing on cargo ships as a tool to screen seafarers for COVID-19'), subcatchment to sewershed (Nicoll et al., 'Neighbourhood-scale wastewater-based epidemiology for COVID-19'), and city to national (Gilpin et al., A pilot study of wastewater monitoring for SARS-CoV-2 in New Zealand' and Trowsdale et al., 'Establishing New Zealand's national pathogen surveillance system using wastewater-based epidemiology');and all the papers make use of the international literature to provide context for what is a global pandemic. Marrying such information with environmental water samples collected on a daily or weekly basis is problematic. Repurposing data also raises important ethical questions of ownership, confidentiality and responsibility that need to be asked to ensure what we scientists do is both good and right (Price and Trowsdale, 'The ethics of wastewater surveillance for public health').

Job demands and well-being in universities in the pandemic: A longitudinal study

Concerns about work intensification within universities have been an issue over the past decade and the Covid-19 pandemic may have accelerated any trend toward excessive job demands associated with work-related stress. This paper reports a longitudinal study conducted in two English universities based on observations at 11 time points from March 2020 to February 2021, covering academic and nonacademic workers. The results show that four measures of job demands increased during the period and that blended learning has contributed to these increases. Various measures of well-being are negatively associated with work intensity, while work-nonwork conflict is positively related to it and mediates the demands-well-being relationship. The study also shows that the use of a variety of methods of accommodating the increased demands-increasing total hours, working at weekends, extending the work day and forsaking breaks, normal holidays and exercise-are associated with increased work intensity. The policy implications of the study are that interventions aimed at employee well-being should be focused on the causes of stress and, particularly, job demands, rather than coping with stress and that future decisions about homeworking should take account of these causes and not simply the satisfaction or performance levels of homeworkers.

Cross-country use of participatory research methods in practice to enhance inclusive decision-making

Purpose>Rethinking participation in disaster research and practice could be facilitated when practitioners are provided with opportunities to pause and reflect deeply on their work outside of the context of their own individual projects and organizational networks. The article draws from an extended collaboration between researchers from multiple countries and disciplines in a working group, which aimed at exploring ethics, participation and power in disaster management.Design/methodology/approach>Under responsible engineering science and technology for disaster risk management, the authors undertook weekly meetings over four months to discuss various facets of adopting participatory methods in their individual projects in Nepal, India, the Philippines and the USA. The article develops a critical reflection of practice using an auto-ethnographical and poly-vocal approach.Findings>The voluntary, digital, sustained, unstructured, recurring and inter-disciplinary characteristics of the authors' working group created an opportunity for researchers and practitioners from different fields and different national, cultural and linguistic backgrounds to come together and collectively issues related to participation, ethics and power.Research limitations/implications>In the paper, the authors do not offer a systematic evaluation of what was a fairly unique process. The paper offers no evaluation of the working group or others like it that focus on questions of replicability, scale and sustainability.Originality/value> To the best of the authors' knowledge, the current work is a unique paper that focuses on situating multi-disciplinary practice within disaster risk management (DRM) and enhancing networks, capacities and expertise for professional education for engineers, physical and social scientists who are involved in research and practice. The polyvocal character of the presentation will help readers access the particular experiences of the participants, which reflect the deeply personal character of the subject matter.

Predicting the weekly COVID-19 new cases using multilayer perceptron: An evidence from west Java, Indonesia

COVID-19 is a contagious disease caused by the coronavirus (SARS-CoV-2) that attacks the respiratory tract. On August 14th, 2021, 653,741 persons had been proven positive for COVID-19. The number of patients tends to increase as the number of COVID-19 cases grows. The more infected people, the more cases of COVID-19 there will be. The Bed Occupancy Ratio (BOR) in West Java reached an all-time high of 91.6 percent in June 2021, far exceeding the WHO recommendation of 60 percent, before gradually declining to 30.69 percent in August. Because of the new cases mentioned, the rate of spread of COVID-19 in West Java, the forecast of new cases is very strategic. The number of new cases in this study was predicted using a Multilayer Perceptron (MLP). The data used in this study were sourced from the COVID-19 Task Force. The data is the number of positive and new cases from 34 provinces in Indonesia from March 2nd, 2020, to August 14th, 2021. The results of the evaluation using test data on the number of active cases in the last 19 weeks, namely April 10th-August 14th, 2021, The MLP is accurate in forecasting the number of new cases 18 times for both forecast periods with APE < 15%, with the value MAPE, RMSE and MAE obtained were 5.52%, 1157,61, and 706.811. The results of this study can be helpful for the government as a reference in conditioning hospital bed capacity to deal with active COVID-19 cases in West Java in the next two weeks so that the hospital rejects no COVID-19 patients because the hospital is full. © 2022 by the authors;licensee Growing Science, Canada.

What should be the baseline when calculating excess mortality? New approaches suggest that we have underestimated the impact of the COVID-19 pandemic and previous winter peaks.

Excess mortality has been used to measure the impact of COVID-19 over time and across countries. But what baseline should be chosen? We propose two novel approaches: an alternative retrospective baseline derived from the lowest weekly death rates achieved in previous years and a within-year baseline based on the average of the 13 lowest weekly death rates within the same year. These baselines express normative levels of the lowest feasible target death rates. The excess death rates calculated from these baselines are not distorted by past mortality peaks and do not treat non-pandemic winter mortality excesses as inevitable. We obtained weekly series for 35 industrialized countries from the Human Mortality Database for 2000-2020. Observed, baseline and excess mortalities were measured by age-standardized death rates. We assessed weekly and annual excess death rates driven by the COVID-19 pandemic in 2020 and those related to seasonal respiratory infections in earlier years. There was a distinct geographic pattern with high excess death rates in Eastern Europe followed by parts of the UK, and countries of Southern and Western Europe. Some Asia-Pacific and Scandinavian countries experienced lower excess mortality. In 2020 and earlier years, the alternative retrospective and the within-year excess mortality figures were higher than estimates based on conventional metrics. While the latter were typically negative or close to zero in years without extraordinary epidemics, the alternative estimates were substantial. Cumulation of this "usual" excess over 2-3 years results in human losses comparable to those caused by COVID-19. Challenging the view that non-pandemic seasonal winter mortality is inevitable would focus attention on reducing premature mortality in many countries. As SARS-CoV-2 is unlikely to be the last respiratory pathogen with the potential to cause a pandemic, such measures would also strengthen global resilience in the face of similar threats in the future.