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Mortality statistics is underlay for public health measures and action and consequently it is one of the major indicator in measures of Covid-19 impact on population. This study aim to explore excess mortality during the Covid-19 pandemic in Serbia. Excess mortality compares expected and observed number of deaths during the given period. Analysis in this paper was based on excess deaths and excess mortality rate. Data was downloaded from the national COVID-19 database and obtained from a relevant source from the Statistical Office of the Republic of Serbia. In order to provide better understanding of excess death, the excess mortality rate was calculated for the period January 2015-June 2022. For the period January 2015-February 2020, 38 months were observed without excess deaths, while in months with excess deaths, almost in all months excess mortality rate was below 12%. Since March 2020, the excess mortality rate has increased significantly, with highest values in December 2020 (91.4%), October (84.3) and November (67.8) 2021. [ABSTRACT FROM AUTHOR]

COVID-19, a global pandemic, has spread in about 215 countries of the world. It has caused enormous damage to the lives, economies, and socio-political fabric of the societies. More than 14 million people stand affected world-wide, with death toll crossing 0.75 million. The expert virologists apprehend that the worst situation is yet not over. The IMF, World Bank, and WTO have issued warnings about the impending global recession. It has also been reported that low-income countries and weak segments of the societies have been the major victims of this malady. Keeping in view the fragile economy and unsound healthcare system of Pakistan, WHO and experts of various fields have predicted multiple effects on various aspects of our national life. The paper in hand attempts to analyze the magnitude of the spread of COVID-19 in Pakistan and its implications for our national security, with a view to offering policy recommendations. [ABSTRACT FROM AUTHOR]

Undoubtedly, the COVID-19 pandemic has reimaged many of today’s “taken-forgranted” systems and processes in every sphere of human life – from individual to national to global. In broader senses, these include individualistic lifestyle, public policies especially regarding economic activities, education processes and healthcare systems, and international coordination and cooperation. The United States – one of the most developed and industrialized countries of the world – has become the epicenter of the 2020 coronavirus outbreak. Till to-date, the country continues to top both in terms of the number of positive cases and deaths from COVID-19. At one point, New York City became the ‘city of the dead’ with more than 3,000 deaths per day. Based on secondary information, this paper aims to explore the causal effects of political economy factors in managing the COVID19 pandemic in New York State. It also focuses on the ‘trickle-down effects’ of state-level policy decisions in a small, rather remote city within the New York State. This chosen city, located in Upstate New York, has not been battered by the pandemic like other parts of the State. Yet, the city has been maintaining and managing all the necessary rules and procedures suggested by the State authorities in line with the relevant federal and global agencies. Consequently, this paper highlights some best practices and lessons learned from the lens of political economy and socioeconomics for the others who are still struggling to manage the spread of the pandemic and restrain its adverse impacts as much as possible. [ABSTRACT FROM AUTHOR]

The paper analyses the differences of COVID-19 mortality rates (MR) in 24 European countries. We explain MRs on the available, reliable ex-ante economic, health and social indicators pertaining to the year 2019 – i.e., before the outbreak of the pandemic. Using simple regression equations, we received statistically significant results for 11 such variables out of 28 attempts. Our best model with two ex-ante independent variables explains 0.76 of the variability of our ex-post dependent variable, the logarithm of Cumulative COVID Deaths. The estimated coefficient for the variable Density of Nurses shows that having one more nurse per 1,000 of population decreases cumulative COVID deaths by almost 15%. Similarly, one more unit Consumption of Non-Prescribed Medicine decreases cumulative deaths by 5%. It seems that until now those European countries were successful in minimising the fatalities where the population had a high level of health literacy, people pursue healthier lifestyle and the healthcare systems worked with a relatively large nursing force already prior to the COVID pandemic. [ABSTRACT FROM AUTHOR]

The eruption of the novel virus brought to the global scene the prediction that Africa would be worse hit by the pandemic. This prediction was partly built on the widely recognized fact that Africa is the continent with the weakest public health care system and the lowest budgetary allocations to health. However, contrary to this prediction, the COVID-19 death rate in Africa has been low compared to in other continents. Debates on Africa's low COVID-19 death rate have generated mixed reactions, the majority of which have centred on beliefs and superstition about hot weather and Africa's youth-dominated society. Little or none of these reactions have attributed the low COVID-19 death rate to swift and prudent budgetary adjustment, which partly aided a swift response from some African governments. Indeed, not many studies have examined the swiftness in the response of some African governments and prudent budgetary adjustment in tackling the spread of COVID-19. This paper, through secondary data, advances knowledge on how budget revision aided government response to the COVID-19 pandemic in South Africa and Nigeria. It found that both countries adjusted their budgetary allocations in response to COVID-19. It further indicates that South Africa, through budgetary revision, allocated more funds to government agencies in charge of COVID-19 and various relief packages than Nigeria. Moreover, it indicates that the swift budgetary adjustment by both countries partly aided a quick government response that progressively flattened the curve and, in the long run, partly contributed to fiscal impulse and deferrals. [ABSTRACT FROM AUTHOR]

Background: The outbreak of COVID-19 has created a landslide of publications, from different sources and unequal impact. We considered that the first 3 months are crucial to understand how knowledge has been generated by performing a bibliometric analysis, including the citations to these articles to guide researchers in exploring this field, and to evaluate the relationship between confirmed COVID-19 cases and deaths with the number of papers per country. Methods: Scientific publications were obtained from PubMed (January-March 2020) and their citations during the first 6 months retrieved from the Scopus database. An analysis of the number of papers by country, approach (type and category of publication), and impact was made. A multiple linear regression model was implemented to analyze the correlation between the number of publications and confirmed cases and deaths. Results: A total of 2,530 publications were analyzed with 59,104 citations (23.4 citations/article), written by authors from 67 countries. China was the country with more publications (988, 39%) and more citations (36,416, 63%) followed by the United States with 423 articles (16.7%) and 7,458 citations (12.6%). The coauthorship network identified 10,756 authors. According to the multivariate analysis, both confirmed cases and deaths were significantly correlated with the number of publications per country (corrected by population size and gross domestic product). Conclusion: The correlation with the number of publications suggests that cases and deaths had some impact on the medical literature, reflecting how rapidly the scientific community has been on the frontline in the fight against COVID-19. [ABSTRACT FROM AUTHOR]

This paper presents a forecasting model for the mortality rates of COVID-19 in six of the top most affected countries depending on the hybrid Genetic Algorithm and Autoregressive Integrated Moving Average (GA-ARIMA). It was aimed to develop an advanced and reliable predicting model that provides future forecasts of possible confirmed cases and mortality rates (Total Deaths per 1 million Population of COVID-19) that could help the public health authorities to develop plans required to resolve the crisis of the pandemic threat in a timely and efficient manner. The study focused on predicting the mortality rates of COVID-19 because the mortality rate determines the prevalence of highly contagious diseases. The Genetic algorithm (GA) has the capability of improving the forecasting performance of the ARIMA model by optimizing the ARIMA model parameters. The findings of this study revealed the high prediction accuracy of the proposed (GA-ARIMA) model. Moreover, it has provided better and consistent predictions compared to the traditional ARIMA model and can be a reliable method in predicting expected death rates as well as confirmed cases of COVID-19. Hence, it was concluded that combining ARIMA with GA is further accurate than ARIMA alone and GA can be an alternative to find the parameters and model orders for the ARIMA model. [ABSTRACT FROM AUTHOR]

An outbreak of coronavirus disease 2019 (COVID-19) has quickly spread worldwide from December 2019, thus characterizing a pandemic. Until August 2020, the United States of America (U.S.) accounted for almost one-fourth of the total deaths by coronavirus. In this paper, a new regression is constructed to identify the variables that affected the first-wave COVID-19 mortality rates in the U.S. states. The mortality rates in these states are computed by considering the total of deaths recorded on 30, 90, and 180 days from the 10th recorded case. The proposed regression is compared to the Kumaraswamy and unit-Weibull regressions, which are useful in modeling proportional data. It provides the best goodness-of-fit measures for the mortality rates and explains 76.57 % of its variability. The population density, Gini coefficient, hospital beds, and smoking rate explain the median of the COVID-19 mortality rates in these states. We believe that this article's results reveal important points to face pandemic threats by the State Health Departments in the U.S. [ABSTRACT FROM AUTHOR]

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The COVID-19 pandemic has profoundly affected all aspects of our lives. Through real-time monitoring and rapid vaccine implementation, we succeeded in suppressing the spread of the disease and mitigating its consequences. Finally, conclusions can be summarized and drawn. Here, we use the example of Poland, which was seriously affected by the pandemic. Compared to other countries, Poland has not achieved impressive results in either testing or vaccination, which may explain its high mortality (case fatality rate, CFR 1.94%). Through retrospective analysis of data collected by the COVID-19 Data Portal Poland, we found significant regional differences in the number of tests performed, number of cases detected, number of COVID-19-related deaths, and vaccination rates. The Masovian, Greater Poland, and Pomeranian voivodeships, the country's leaders in vaccination, reported high case numbers but low death rates. In contrast, the voivodeships in the eastern and southern parts of Poland (Subcarpathian, Podlaskie, Lublin, Opole), which documented low vaccination levels and low case numbers, had higher COVID-19-related mortality rates. The strong negative correlation between the CFR and the percentage of the population that was vaccinated in Poland supports the validity of vaccination. To gain insight into virus evolution, we sequenced more than 500 genomes and analyzed nearly 80 thousand SARS-CoV-2 genome sequences deposited in GISAID by Polish diagnostic centers. We showed that the SARS-CoV-2 variant distribution over time in Poland reflected that in Europe. Haplotype network analysis allowed us to follow the virus transmission routes and identify potential superspreaders in each pandemic wave. [ABSTRACT FROM AUTHOR]

This paper examines the uneven geography of COVID‐19‐related excess mortality during the first wave of the pandemic in Europe, before assessing the factors behind the geographical differences in impact. The analysis of 206 regions across 23 European countries reveals a distinct COVID‐19 geography. Excess deaths were concentrated in a limited number of regions—expected deaths exceeded 20% in just 16 regions—with more than 40% of the regions considered experiencing no excess mortality during the first 6 months of 2020. Highly connected regions, in colder and dryer climates, with high air pollution levels, and relatively poorly endowed health systems witnessed the highest incidence of excess mortality. Institutional factors also played an important role. The first wave hit regions with a combination of weak and declining formal institutional quality and fragile informal institutions hardest. Low and declining national government effectiveness, together with a limited capacity to reach out across societal divides, and a frequent tendency to meet with friends and family were powerful drivers of regional excess mortality. [ABSTRACT FROM AUTHOR]

Background and Aims: Hospitals are one of the most important healthcare centers for providing the patients with different medical needs. Several different factors might cause hospitals to downgrade their services or departments or close down overall. One of the most multifaceted reasons for hospital downgrading or closure is infectious disease outbreaks. In this regard, we aimed to evaluate the effects of hospital closure and downgrading due to the COVID‐19 pandemic on the mortality rate of the people residing in Fars province, Iran. Methods: We gathered mortality information, including the cause of death, age, sex, place, and time of death of all deceased cases occurring during a period of 3 years, from February 20, 2018 to March 2021 from the forensic medicine and also the Department of Biostatistics in Shiraz University of Medical Sciences. Results: A total of 71,331 deaths have been reported since 2018 through the first quarter of 2021, with 57.9% of total mortality cases attributed to male gender. The total mortality counts ranged from 4229 to 9809 deaths per quarter, from which the minimum rate was reported in the first quarter of 2018 and the maximum in the fourth quarter of 2020. Based on the causes of death, diseases of the circulatory system were shown to be the all‐time most frequent cause of death, accounting for a total of 42.8% of recorded deaths, followed by neoplasms (9.77%) and diseases of the respiratory system (9.45%). Conclusion: Although the large number of deaths at the time of the pandemic are immediately due to COVID‐19 infection, deaths due to a notable number of other causes have had a significant increase which, along with the specific trend of place and causes of death, shows that the downgrading and closure of hospitals have had a significant impact on overall population mortality. Key points: Several different causes with various natures might cause hospitals to downgrade their services or departments or close down overall, including the COVID‐19 pandemic.Although the large number of deaths at the time of the pandemic are immediately due to COVID‐19 infection, deaths due to a notable number of other causes have had a significant increase which, along with the specific trend of place and causes of death, shows that the downgrading and closure of hospitals have had a significant impact on overall population mortality.Evaluation of the long‐term impact of hospital closure on disease‐related mortalities should be continuously assessed, especially after the reopening of hospitals, to evaluate the significance of the impact of these imposed conditions. [ABSTRACT FROM AUTHOR]

The ongoing CoVID-19 pandemic affected our lives dramatically. Many epidemiological models are developed by scientists to estimate the number of infected individuals and the transmission rate of the CoVID-19 pandemic. In this paper, we analyze the evolution of COVID-19 in Turkey over the period November 16 and December 9, 2020, using the SIR model. The estimation of the reproduction number is found as 1.38. The peak day of the pandemic based on the period used in the SIR model is estimated as the 13th of January. By that date, around a total number of 3530000 individuals would be affected according to the SIR model and among them, approximately 141000 people would be active cases. In total, approximately 35000 people would die, based on a mortality rate of 1%. These predictions are made according to the scenario, which assumes, the latest restrictions weren't announced by the Turkish Ministry of Health. The findings of this study can be used to understand the characteristics of the pandemic at a certain time and estimate the distribution of the disease but are not suggested for any policy change and strategies. [ABSTRACT FROM AUTHOR]

Background: Causes of death other than COVID-19 seem to contribute significantly to the excess mortality observed during the 2020–2022 pandemic. In this study, we explore changes in non-COVID-19 causes of death in Norway during the COVID-19 pandemic from March 2020 to December 2022. Methods: We performed a population-based cross-sectional study on data from the Norwegian Cause of Death Registry. All recorded deaths from 1st January 2010 to 31st December 2022 were included. The main outcome measures were the number of deaths and age-standardised death rate (ASMR) per 100000 population from the major cause of death groups in 2020, 2021 and 2022. The predicted number of deaths and ASMRs were forecasted with a 95% prediction interval constructed from a general linear regression model based on the corresponding number of deaths and rates from the preceding ten prepandemic years (2010–2019). We also examined whether there were deviations from expected seasonality in the pandemic period based on prepandemic monthly data from 2010–2019. The cumulative number of deaths and ASMR were estimated based on monthly mortality data. Results: There was significant excess mortality (number of deaths) in 2021 and 2022 for all causes (3.7% and 14.5%), for cardiovascular diseases (14.3% and 22.0%), and for malignant tumours in 2022 (3.5%). In terms of ASMR, there was excess mortality in 2021 and 2022 for all causes (2.9% and 13.7%), and for cardiovascular diseases (16.0% and 25,8%). ASMR was higher than predicted in 2022 for malignant tumours (2.3%). There were fewer deaths than predicted from respiratory diseases (except COVID-19) in 2020 and 2021, and from dementia in 2021 and 2022. From March 2020 to December 2022, there were cumulatively 3754 (ASMR 83.8) more non-COVID-19 deaths than predicted, of which 3453 (ASMR: 79.6) were excess deaths from cardiovascular disease, 509 (ASMR 4.0) from malignant tumours. Mortality was lower than predicted for respiratory diseases (-1889 (ASMR: -44.3)), and dementia (-530 (ASMR -18.5)). Conclusions: There was considerable excess non-COVID-19 mortality in Norway from March 2020 until December 2022, mainly due to excess cardiovascular deaths. For respiratory diseases and dementia, mortality was lower than predicted. [ABSTRACT FROM AUTHOR]

INTRODUCTION: We investigated whether mortality in memory clinic patients changed due to coronavirus disease 2019 (COVID‐19) pandemic. METHODS: We included patients from the Amsterdam Dementia Cohort: (1) n = 923 pandemic patients (baseline visit: 2017–2018, follow‐up: until 2021), and (2) n = 830 historical control patients (baseline visit: 2015–2016, follow‐up: until 2019). Groups were well‐balanced. We compared mortality during pandemic with historical control patients using Cox regression. Differences in cause of death between groups were explored using Fisher's exact test. RESULTS: Pandemic patients had a higher risk of mortality than historical control patients (hazard ratio [HR] [95% confidence interval {CI}] = 1.34 [1.05–1.70]). Stratified for syndrome diagnosis, the effect remained significant in dementia patients (HR [95% CI] = 1.35 [1.03–1.78]). Excluding patients who died of COVID‐19‐infection, the higher mortality risk in pandemic patients attenuated (HR [95% CI] = 1.24 [0.97–1.58]). Only the difference in cause of death between pandemic patients and historical control patients for death to COVID‐19‐infection (p = 0.001) was observed. CONCLUSION: Memory clinic patients had increased mortality risk during COVID‐19 compared to historical control patients, attributable to dementia patients. Highlights: We investigated if mortality rates in memory clinic patients changed due to COVID‐19 pandemic.We included patients along the cognitive continuum, including SCD, MCI, and dementia.We used a well‐balanced historical control group.Memory clinic patients had higher risk for mortality during COVID‐19 lockdown.Our results indicate that excess mortality is mainly caused by death to COVID‐19 infection. [ABSTRACT FROM AUTHOR]

Background and Aim: The impact of multiple risk factors on COVID-19 mortality has been previously reported in multiple systematic reviews and meta-analyses. The aim of this review is to provide a comprehensive update on the association between hypertension (HTN) and mortality in patients with COVID-19. Methods: A systematic review and meta-analysis were performed and followed the Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines. A search was achieved using PubMed, Scopus, and Cochrane Databases for research publications on hypertension, COVID-19, and mortality published between December 2019 and August 2022. Results: A total of 23 observational studies involving 611,522 patients from 5 countries (China, Korea, the UK, Australia, and the USA) were included in our study. The confirmed number of COVID-19 with HTN cases in each study ranged from 5 to 9964. The mortality ranged from 0.17% to 31% in different studies. Pooled results show that the mortality rate of COVID-19 among the included studies ranges from a minimum of 0.39 (95% CI 0.13–1.12) to a maximum of 5.74 (95% CI 3.77–8.74). Out of the 611,522 patients, 3119 died which resulted in an overall mortality prevalence of 0.5%. Subgroup analyses indicated that patients with COVID-19 who have hypertension and male patients had slightly less risk of mortality than female patients [the percentage of men > 50%; OR 1.33: 95% CI (1.01, 1.76); the percentage of men ≤ 50%: OR 2.26; and 95% CI (1.15, 4.48)]. Meta-regression analysis results also showed a statistically significant association between hypertension and COVID-19 mortality. Conclusion: This systematic review and meta-analysis suggest that hypertension may not be the only risk factor associated with the increased mortality rate during the COVID-19 pandemic. In addition, a combination of other comorbidities and old age appears to increase the risk of mortality from COVID-19. [ABSTRACT FROM AUTHOR]

We conducted an updated meta-analysis to evaluate the 30-day mortality of hip fractures during the COVID-19 pandemic and assess mortality rates by country. We systematically searched Medline, EMBASE, and the Cochrane Library up to November 2022 for studies on the 30-day mortality of hip fractures during the pandemic. Two reviewers used the Newcastle–Ottawa tool to independently assess the methodological quality of the included studies. We conducted a meta-analysis and systematic review including 40 eligible studies with 17,753 patients with hip fractures, including 2280 patients with COVID-19 (12.8%). The overall 30-day mortality rate for hip fractures during the pandemic was 12.6% from published studies. The 30-day mortality of patients with hip fractures who had COVID-19 was significantly higher than those without COVID-19 (OR, 7.10; 95% CI, 5.51–9.15; I2 = 57%). The hip fracture mortality rate increased during the pandemic and varied by country, with the highest rates found in Europe, particularly the United Kingdom (UK) and Spain. COVID-19 may have contributed to the increased 30-day mortality rate in hip fracture patients. The mortality rate of hip fracture in patients without COVID-19 did not change during the pandemic. [ABSTRACT FROM AUTHOR]

Background: This study aimed to assess the mortality of PsA before and during the COVID-19 pandemic. Methods: From the prospective, multicenter PsART-ID (Psoriatic Arthritis Registry-International Database), patients from Turkey were analyzed by linking the registry to the Turkish Cause of Death Registry. The outcome of interest was death from any cause, pre-pandemic (since the onset of registry—March 2014–March 2020), and during the pandemic (March 2020–May 2021). The crude mortality rate and standardized mortality ratio (SMR) were determined. Results: There were 1216 PsA patients with a follow-up of 7500 patient-years. Overall, 46 deaths (26 males) were observed. In the pre-pandemic period, SMR for PsA vs the general population was 0.95 (0.61–1.49), being higher in males [1.56 (0.92–2.63)] than females [0.62 (0.33–1.17)]. The crude mortality rate in PsA doubled during the pandemic (pre-pandemic crude mortality rate: 5.07 vs 10.76 during the pandemic) with a higher increase in females (2.9 vs 8.72) than males (9.07 vs 14.73). Conclusion: The mortality in PsA was found similar to the general population in the pre-pandemic era. The mortality rates in PsA doubled during the pandemic. Whether PsA patients have more risk of mortality than the general population due to COVID-19 needs further studies. Key Points • Decrease in mortality in PsA might be expected with the more effective treatment options and better disease control. • A crude mortality rate is comparable to the general population and not increased until the pandemic. • Currently, there is a 2-fold increase in crude mortality rate possibly due to the COVID-19. [ABSTRACT FROM AUTHOR]

Background: Ensuring health equity, especially for vulnerable populations in less developed settings with poor health system is essential for the current and future global health threats. This study examined geographical variations of COVID-19 mortality and its association with population health characteristics, health care capacity in responding pandemic, and socio-economic characteristics across 514 districts in Indonesia. Methods: This nationwide ecological study included aggregated data of COVID-19 cases and deaths from all 514 districts in Indonesia, recorded in the National COVID-19 Task Force database, during the first two years of the epidemic, from 1 March 2020 to 27 February 2022. The dependent variable was district-level COVID-19 mortality rate per 100,000 populations. The independent variables include district-level COVID-19 incidence rate, population health, health care capacity, and socio-demographics data from government official sources. We used multivariable ordinal logistic regression to examine factors associated with higher mortality rate. Results: Of total 5,539,333 reported COVID-19 cases, 148,034 (2.7%) died, and 5,391,299 (97.4%) were recovered. The district-level mortality rate ranged from 0 to 284 deaths per 100,000 populations. The top five districts with the highest mortality rate were Balikpapan (284 deaths per 100,000 populations), Semarang (263), Madiun (254), Magelang (250), and Yogyakarta (247). A higher COVID-19 incidence (coefficient 1.64, 95% CI 1.22 to 1.75), a higher proportion of ≥ 60 years old population (coefficient 0.26, 95% CI 0.06 to 0.46), a higher prevalence of diabetes mellitus (coefficient 0.60, 95% CI 0.37 to 0.84), a lower prevalence of obesity (coefficient -0.32, 95% CI -0.56 to -0.08), a lower number of nurses per population (coefficient -0.27, 95% CI -0.50 to -0.04), a higher number of midwives per population (coefficient 0.32, 95% CI 0.13 to 0.50), and a higher expenditure (coefficient 0.34, 95% CI 0.10 to 0.57) was associated with a higher COVID-19 mortality rate. Conclusion: COVID-19 mortality rate in Indonesia was highly heterogeneous and associated with higher COVID-19 incidence, different prevalence of pre-existing comorbidity, healthcare capacity in responding the pandemic, and socio-economic characteristics. This study revealed the need of controlling both COVID-19 and those known comorbidities, health capacity strengthening, and better resource allocation to ensure optimal health outcomes for vulnerable population. [ABSTRACT FROM AUTHOR]

There is significant cross-country variation in COVID-19 fatalities worldwide. In this study, we analyze the relationship between political trust and fatalities from the COVID-1s9 pandemic. By performing a cross-country analysis and controlling for other determinants, we find that government trust is negatively associated with the excess mortality rate during the pandemic. [ABSTRACT FROM AUTHOR]

After initially having low levels of SARS-CoV-2 infections for much of the year, Bulgaria experienced a major epidemic surge at the end of 2020, which caused the highest recorded excess mortality in Europe, among the highest in the word (Excess Mortality Rate, or EMR ∼0.25%). Two more major waves followed in 2021, followed by another one in early 2022. In this study, we analyze the temporal and spatial patterns of excess mortality at the national and local levels and across different demographic groups in Bulgaria and compare those to the European levels. Bulgaria has continued to exhibit the previous pattern of extremely high excess mortality, as measured both by crude mortality metrics (an EMR of ∼1.05%, up to the end of March 2022) and by standardized ones—Potential Years of Life Lost (PYLL) and Aged-Standardized Years of life lost Rate (ASYR). Unlike Western Europe, the bulk of excess mortality in Bulgaria, as well as in several other countries in Eastern Europe, occurred in the second year of the pandemic, likely related to the differences in the levels of vaccination coverage between these regions. We also observe even more extreme levels of excess mortality at the regional level and in some subpopulations (e.g., total EMR values for males ≥ 2% and EMR values for males aged 40–64 ≥ 1% in certain areas). We discuss these observations in light of the estimates of infection fatality rate (IFR) and eventual population fatality rate (PFR) made early in the course of the pandemic. [ABSTRACT FROM AUTHOR]

Objective: We explain evolving policy choices made by all 50 states in response to the coronavirus (COVID‐19) pandemic in the United States against the background of each state's political and public health landscape. Method: We create an index of eight state preventative measures and explain variation in that index by infection and death rates, vaccination rates, support for President Trump in 2020, and the political party of the governor. We control for population density and the health vulnerability of each state. Results: State response was largely driven by three factors: the death rate from COVID‐19, Trump votes in 2020 and Republican control of the governorship. Conclusion: Understanding state response to the pandemic requires going beyond a partisan lens and considering the shifting onus of responsibility for taking action to protect against the virus from states to individual citizens in an increasingly politicized sphere of pandemic response. [ABSTRACT FROM AUTHOR]

There are no widely accepted, quantitative definitions for the end of a pandemic such as COVID‐19. The end of the pandemic due to a new virus and the transition to endemicity may be defined based on a high proportion of the global population having some immunity from natural infection or vaccination. Other considerations include diminished death toll, diminished pressure on health systems, reduced actual and perceived personal risk, removal of restrictive measures and diminished public attention. A threshold of 70% of the global population having being vaccinated or infected was probably already reached in the second half of 2021. Endemicity may still show major spikes of infections and seasonality, but typically less clinical burden, although some locations are still hit more than others. Death toll and ICU occupancy figures are also consistent with a transition to endemicity by end 2021/early 2022. Personal risk of the vast majority of the global population was already very small by end 2021, but perceived risk may still be grossly overestimated. Restrictive measures of high stringency have persisted in many countries by early 2022. The gargantuan attention in news media, social media and even scientific circles should be tempered. Public health officials need to declare the end of the pandemic. Mid‐ and long‐term consequences of epidemic waves and of adopted measures on health, society, economy, civilization and democracy may perpetuate a pandemic legacy long after the pandemic itself has ended. [ABSTRACT FROM AUTHOR]

Objectives: To identify international and periodically updated models of the COVID-19 epidemic, compile and visualize their estimation results at the global, regional, and country levels, and periodically update the compilations. This compilation can serve as an early warning mechanism for countries about future surges in cases and deaths. When one or more models predict an increase in daily cases or infections and deaths in the next one to three months, technical advisors to the national and subnational decision-makers can consider this early alarm for assessment and suggestion of augmentation of preventive measures and interventions. Data description: Five international and periodically updated models of the COVID-19 pandemic were identified, created by: (1) Massachusetts Institute of Technology, Cambridge, (2) Institute for Health Metrics and Evaluation, Seattle, (3) Imperial College, London, (4) Los Alamos National Laboratories, Los Alamos, and (5) University of Southern California, Los Angeles. Estimates of these five identified models were gathered, combined, and graphed at global and two country levels. Canada and Iran were chosen as countries with and without subnational estimates, respectively. Compilations of results are periodically updated. Three Github repositories were created that contain the codes and results, i.e., "CovidVisualizedGlobal" for the global and regional levels, "CovidVisualizedCountry" for a country with subnational estimates–Canada, and "covir2" for a country without subnational estimates–Iran. [ABSTRACT FROM AUTHOR]

Background: The ongoing COVID-19 pandemic hit South America badly with multiple waves. Different COVID-19 variants have been storming across the region, leading to more severe infections and deaths even in places with high vaccination coverage. This study aims to assess the spatiotemporal variability of the COVID-19 pandemic and estimate the infection fatality rate (IFR), infection attack rate (IAR) and reproduction number ( R 0 ) for twelve most affected South American countries. Methods: We fit a susceptible-exposed-infectious-recovered (SEIR)-based model with a time-varying transmission rate to the reported COVID-19 deaths for the twelve South American countries with the highest mortalities. Most of the epidemiological datasets analysed in this work are retrieved from the disease surveillance systems by the World Health Organization, Johns Hopkins Coronavirus Resource Center and Our World in Data. We investigate the COVID-19 mortalities in these countries, which could represent the situation for the overall South American region. We employ COVID-19 dynamic model with-and-without vaccination considering time-varying flexible transmission rate to estimate IFR, IAR and R 0 of COVID-19 for the South American countries. Results: We simulate the model in each scenario under suitable parameter settings and yield biologically reasonable estimates for IFR (varies between 0.303% and 0.723%), IAR (varies between 0.03 and 0.784) and R 0 (varies between 0.7 and 2.5) for the 12 South American countries. We observe that the severity, dynamical patterns of deaths and time-varying transmission rates among the countries are highly heterogeneous. Further analysis of the model with the effect of vaccination highlights that increasing the vaccination rate could help suppress the pandemic in South America. Conclusions: This study reveals possible reasons for the two waves of COVID-19 outbreaks in South America. We observed reductions in the transmission rate corresponding to each wave plausibly due to improvement in nonpharmaceutical interventions measures and human protective behavioral reaction to recent deaths. Thus, strategies coupling social distancing and vaccination could substantially suppress the mortality rate of COVID-19 in South America. [ABSTRACT FROM AUTHOR]

Background: This study aimed to investigate overall and sex-specific excess all-cause mortality since the inception of the COVID-19 pandemic until August 2020 among 22 countries.Methods: Countries reported weekly or monthly all-cause mortality from January 2015 until the end of June or August 2020. Weekly or monthly COVID-19 deaths were reported for 2020. Excess mortality for 2020 was calculated by comparing weekly or monthly 2020 mortality (observed deaths) against a baseline mortality obtained from 2015-2019 data for the same week or month using two methods: (i) difference in observed mortality rates between 2020 and the 2015-2019 average and (ii) difference between observed and expected 2020 deaths.Results: Brazil, France, Italy, Spain, Sweden, the UK (England, Wales, Northern Ireland and Scotland) and the USA demonstrated excess all-cause mortality, whereas Australia, Denmark and Georgia experienced a decrease in all-cause mortality. Israel, Ukraine and Ireland demonstrated sex-specific changes in all-cause mortality.Conclusions: All-cause mortality up to August 2020 was higher than in previous years in some, but not all, participating countries. Geographical location and seasonality of each country, as well as the prompt application of high-stringency control measures, may explain the observed variability in mortality changes. [ABSTRACT FROM AUTHOR]

The emergence of the global COVID-19 pandemic has continued to cause havoc and untold suffering worldwide. The pandemic's pattern of spreading has varied between continents and indeed within countries. Studies looking at the factors affecting the dynamics of COVID-19 in African countries are limited. In this study we attempt to assess and understand the differential spread of COVID-19 in Africa by looking at South Africa (Southern Africa), Senegal (West Africa), Egypt and Morocco (North Africa), Rwanda (Central Africa) and Kenya (East Africa). We observed a high prevalence of COVID-19 in South Africa, Morocco and Egypt. Furthermore, South Africa ranks first in the number of reported confirmed COVID-19 cases and related hospitalisations and deaths in Africa. Senegal (Western Africa) shows lower confirmed cases and death rates. Based on the findings, it is crucial for these countries' governments and health authorities to be vigilant in mobilising effective medical resources and to continue educating their citizens in order to curb the spread of the coronavirus. [ABSTRACT FROM AUTHOR]

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Background: The coronavirus disease 2019 (COVID-19) pandemic has significantly influenced epidemiology, yet its impact on out-of-hospital cardiac arrest (OHCA) remains unclear. We aimed to evaluate the impact of the pandemic on the incidence and case fatality rate (CFR) of OHCA. We also evaluated the impact on intermediate outcomes and clinical characteristics. Methods: PubMed, EMBASE, Web of Science, Scopus, and Cochrane Library databases were searched from inception to May 3, 2021. Studies were included if they compared OHCA processes and outcomes between the pandemic and historical control time periods. Meta-analyses were performed for primary outcomes [annual incidence, mortality, and case fatality rate (CFR)], secondary outcomes [field termination of resuscitation (TOR), return of spontaneous circulation (ROSC), survival to hospital admission, and survival to hospital discharge], and clinical characteristics (shockable rhythm and etiologies). This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42021253879). Results: The COVID-19 pandemic was associated with a 39.5% increase in pooled annual OHCA incidence (p < 0.001). Pooled CFR was increased by 2.65% (p < 0.001), with a pooled odds ratio (OR) of 1.95 for mortality [95% confidence interval (95%CI) 1.51–2.51]. There was increased field TOR (OR = 2.46, 95%CI 1.62–3.74). There were decreased ROSC (OR = 0.65, 95%CI 0.55–0.77), survival to hospital admission (OR = 0.65, 95%CI 0.48–0.89), and survival to discharge (OR = 0.52, 95%CI 0.40–0.69). There was decreased shockable rhythm (OR = 0.73, 95%CI 0.60–0.88) and increased asphyxial etiology of OHCA (OR = 1.17, 95%CI 1.02–1.33). Conclusion: Compared to the pre-pandemic period, the COVID-19 pandemic period was significantly associated with increased OHCA incidence and worse outcomes. [ABSTRACT FROM AUTHOR]

The recent pandemic of COVID‐19 revealed that a highly transmissible virus threatens all humanity because extensive mobility, migration, and millions of passengers who travel worldwide shape our globalized environment and make containment of a virus more difficult. In a war between humans and viruses, we should have the necessary weapons, such as masks, gloves, ventilators, and so forth. However, during the COVID‐19 outbreak, there was a shortage of this basic medical and personal protective equipment (MAPPE), even for the health workers. This note focuses on this issue and suggests that a global organization, which stores and renews basic MAPPE would be beneficial in the fight against the next pandemic and that such an organization can be established without significantly increasing the public expenses of the countries. Highlights: •During the COVID‐19 outbreak, there was a shortage of basic medical equipment.•There is a need for a global organization to stockpile medical equipment.•This stockpile should be renewed regularly to ensure modern equipment.•We suggest a procedure that satisfies the abovementioned and involves low cost.•The organization can reduce the economic consequences of the pandemic and death toll, as well as promote solidarity. [ABSTRACT FROM AUTHOR]

The aim of this paper is to develop an information-modeling method for assessing and predicting the consequences of the COVID-19 pandemic. To this end, a detailed analysis of official statistical information provided by global and national organizations is carried out. The developed method is based on the algorithm of multi-channel big data processing considering the demographic and socio-economic information. COVID-19 data are analyzed using an instability indicator and a system of differential equations that describe the dynamics of four groups of people: susceptible, infected, recovered and dead. Indicators of the global sustainable development in various sectors are considered to analyze COVID-19 data. Stochastic processes induced by COVID-19 are assessed with the instability indicator showing the level of stability of official data and the reduction of the level of uncertainty. It turns out that the number of deaths is rising with the Human Development Index. It is revealed that COVID-19 divides the global population into three groups according to the relationship between Gross Domestic Product and the number of infected people. The prognosis for the number of infected people in December 2020 and January-February 2021 shows negative events which will decrease slowly. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the current pandemic of coronavirus disease 2019 (COVID-19). This pandemic is characterized by a high variability in death rate (defined as the ratio between the number of deaths and the total number of infected people) across world countries. Several possible explanations have been proposed, but it is not clear whether this variability is due to a single predominant factor or instead to multiple causes. Here we addressed this issue using multivariable regression analysis to test the impact of the following factors: the hospital stress (defined as the ratio between the number of infected cases and the total number of hospital beds), the population median age, and the quality of the National Health System (NHS). For this analysis, we chose countries of the world with over 3000 infected cases as of April 1, 2020. Hospital stress was found to be the crucial factor in explaining the variability of death rate, while the others had negligible relevance. Different procedures for quantifying cases of infection and death for COVID-19 could affect the variability in death rate across countries. We therefore applied the same statistical approach to Italy, which is divided into 20 Regions that share the same protocol for counting the outcomes of this pandemic. Correlation between hospital stress and death rate was even stronger than that observed for countries of the world. Based on our findings and the historical trend for the availability of hospital beds, we propose guidelines for policy-makers to properly manage future pandemics. [ABSTRACT FROM AUTHOR]

Since its initial outbreak in China, in December, 2019, COVID-19 has spread rapidly across the globe. At the time of writing, on April 29, 2020, infections had been confirmed in more than 200 countries and regions and 3,018,681 infections and 207,973 deaths had been recorded. In Japan, the first confirmed case of SARS-CoV-2 infection was reported on January 16, 2020, since when, the number of domestic infections and the death toll have reached 13,852 and 389, respectively, representing exponential increases. Moreover, both domestically and internationally, the shortage of medical resources and the spread of infection among medical personnel, caused by nosocomial transmissions have become problematic. The pathology of COVID-19, including the exact infection route, remains largely unknown. Given the unavailability of an effective cure and vaccine, people are required to respond to this adversity without becoming complacent. The global efforts against the COVID-19 pandemic are ongoing endeavors, far from over. New epidemiological data and clinical findings are emerging on a daily basis, making it critical to always refer to the latest information. [ABSTRACT FROM AUTHOR]

Since early 2020, the COVID-19 pandemic has caused an excess in morbidity and mortality rates worldwide. Containment strategies rely firstly on rapid and sensitive laboratory diagnosis, with molecular detection of the viral genome in respiratory samples being the gold standard. The reliability of diagnostic protocols could be affected by SARS-CoV-2 genetic variability. In fact, mutations occurring during SARS-CoV-2 genomic evolution can involve the regions targeted by the diagnostic probes. Following a review of the literature and an in silico analysis of the most recently described virus variants (including the UK B 1.1.7 and the South Africa 501Y.V2 variants), we conclude that the described genetic variability should have minimal or no effect on the sensitivity of existing diagnostic protocols for SARS-CoV-2 genome detection. However, given the continuous emergence of new variants, the situation should be monitored in the future, and protocols including multiple targets should be preferred. [ABSTRACT FROM AUTHOR]