COVID-19 second wave mortality in Europe and the United States

Nonlinear shifts and dislocations in financial market structure and composition

This paper develops new mathematical techniques to identify temporal shifts among a collection of US equities partitioned into a new and more detailed set of market sectors. Although conceptually related, our three analyses reveal distinct insights about financial markets, with meaningful implications for investment managers. First, we explore a variety of methods to identify nonlinear shifts in a market sector structure and describe the mathematical connection between the measure used and the captured phenomena. Second, we study a network structure with respect to our new market sectors and identify meaningfully connected sector-to-sector mappings. Finally, we conduct a series of sampling experiments over different sample spaces and contrast the distribution of Sharpe ratios produced by long-only, long-short, and short-only investment portfolios. In addition, we examine the sector composition of the top-performing portfolios for each of these portfolio styles. In practice, the methods proposed in this paper could be used to identify regime shifts, optimally structured portfolios, and better communities of equities.

Estimating hidden relationships in dynamical systems: Discovering drivers of infection rates of COVID-19

Discovering causal influences among internal variables is a fundamental goal of complex systems research. This paper presents a framework for uncovering hidden relationships from limited time-series data by combining methods from nonlinear estimation and information theory. The approach is based on two sequential steps: first, we reconstruct a more complete state of the underlying dynamical system, and second, we calculate mutual information between pairs of internal state variables to detail causal dependencies. Equipped with time-series data related to the spread of COVID-19 from the past three years, we apply this approach to identify the drivers of falling and rising infections during the three main waves of infection in the Chicago metropolitan region. The unscented Kalman filter nonlinear estimation algorithm is implemented on an established epidemiological model of COVID-19, which we refine to include isolation, masking, loss of immunity, and stochastic transition rates. Through the systematic study of mutual information between infection rate and various stochastic parameters, we find that increased mobility, decreased mask use, and loss of immunity post sickness played a key role in rising infections, while falling infections were controlled by masking and isolation.

The impact of the COVID-19 pandemic on primary health care practices and patient management in the Republic of Moldova - results from the PRICOV-19 survey

BACKGROUND

The COVID-19 pandemic has had an enormous impact on health systems in Europe and has generated unprecedented challenges for tertiary care. Less is known about the effects on the activities of local family doctors (FDs), who have shifted tasks and adapted their practice to accommodate the new services brought by the pandemic. The PRICOV-19 study was a multi-country survey aiming to understand the challenges posed by the pandemic in primary health care (PHC) practices around Europe. Within the framework of this study, we assessed the impact of the pandemic on PHC facilities in urban, rural, and mixed urban/rural areas in the Republic of Moldova.

METHODS

We present the results from the PRICOV-19 questionnaire designed at Ghent University (Belgium) and distributed between January and March 2021 to PHC facilities from the 35 districts of the Republic of Moldova. This analysis presents descriptive data on limitations to service delivery, staff role changes, implementation and acceptance of COVID-19 guidelines, and incidents reported on staff and patient safety during the pandemic.

RESULTS

Results highlighted the differences between facilities located in urban, rural, and mixed areas in several dimensions of PHC. Nearly half of the surveyed facilities experienced limitations in the building or infrastructure when delivering services during the pandemic. 95% of respondents reported an increase in time spent giving information to patients by phone, and 88% reported an increase in responsibilities. Few practices reported errors in clinical assessments, though a slightly higher number of incidents were reported in urban areas. Half of the respondents reported difficulties delivering routine care to patients with chronic conditions and a delay in treatment-seeking.

CONCLUSIONS

During the pandemic, the workload of PHC staff saw a significant increase, and practices met important structural and organizational limitations. Consequently, these limitations may have also affected care delivery for vulnerable patients with chronic conditions. Adjustments and bottlenecks need to be addressed, considering the different needs of PHC facilities in urban, rural, and mixed areas.

Mortality reduction in older COVID-19-patients hospitalized in Spain during the second pandemic wave from the SEMI-COVID-19 Registry

In 2020, the COVID-19 pandemic followed a two-wave pattern in most countries. Hospital admission for COVID-19 in one wave or another could have affected mortality, especially among the older persons. The objective of this study was to evaluate whether the admission of older patients during the different waves, before SARS-CoV-2 vaccination was available, was associated with a different mortality. We compared the mortality rates of patients hospitalized during 2020 before (first wave) and after (second wave) July 7, 2020, included in the SEMI-COVID-19 Registry, a large, multicenter, retrospective cohort of patients admitted to 126 Spanish hospitals for COVID-19. A multivariate logistic regression analysis was performed to control for changes in either the patient or disease profile. As of December 26, 2022, 22,494 patients had been included (17,784 from the first wave and 4710 from the second one). Overall mortality was 20.4% in the first wave and 17.2% in the second wave (risk difference (RD) - 3.2%; 95% confidence interval (95% CI) - 4.4 to - 2.0). Only patients aged 70 and older (10,973 patients: 8571 in the first wave and 2386 in the second wave) had a significant reduction in mortality (RD - 7.6%; 95% CI - 9.7 to - 5.5) (unadjusted relative risk reduction: 21.6%). After adjusting for age, comorbidities, variables related to the severity of the disease, and treatment received, admission during the second wave remained a protective factor. In Spain, patients aged 70 years and older admitted during the second wave of the COVID-19 pandemic had a significantly lower risk of mortality, except in severely dependent persons in need of corticosteroid treatment. This effect is independent of patient characteristics, disease severity, or treatment received. This suggests a protective effect of a better standard of care, greater clinical expertise, or a lesser degree of healthcare system overload.

Mortality in Two Waves of COVID-19: A Comparative Analysis of a Tertiary Care Hospital in India

Background COVID-19 has spread as two distinct surges of cases in many countries. Several countries have reported differences in disease severity and mortality in the two waves. Objective Compare the in-hospital mortality in the two COVID-19 waves at a tertiary care hospital in India. Methods We conducted a retrospective data collection. Distinct periods of surges in cases and admissions were defined as the first wave spanning from March 2020 to December 2020 and the second wave from April 2021 to June 21, 2021. The primary outcome of this study was to compare mortality rates in terms of total hospital mortality rate (TMR) and case fatality rate (CFR). Results Mortality rates of wave 2 were approximately 10 times that of wave 1 (TMR of 20.3% in wave 2 versus 2.4% in wave 1 and CFR of 1.5% versus 17.7% in wave 1 and 2, respectively). Mortalities in wave 2 had a larger proportion of severe disease at presentation, faster progression of symptoms to death, and more patients without any chronic comorbid condition dying due to the direct effect of COVID-19 acute respiratory distress syndrome (ARDS). Conclusion Our data matches the worldwide reported pooled hospital mortality figures and shows the comparative difference in disease severity between the two waves.

Impacts of the COVID-19 pandemic on life expectancy at birth in Asia

OBJECTIVE

To investigate the impact of the COVID-19 pandemic on life expectancy at birth (e0) for 51 Asian countries and territories from January 1, 2020 to December 31, 2021.

METHOD

Based on age-sex-specific mortality used for estimating the changes in e0 for years 2019, 2020, and 2021 from the 2022 revision of the World Population Prospects, we employed Arriaga's discrete method to decompose changes in e0 into both absolute and relative contributions of changes in age-specific death rate, and further obtained the age-sex-specific contribution to changes in e0 by country/territory and period (i.e., 2019-2020 and 2020-2021) for Asia.

FINDINGS

The COVID-19 pandemic reduced 1.66 years in e0 of the Asian population from 2019 to 2021, slightly lower than the world average of 1.74 years. South Asia had a high loss of 3.01 years, whereas Eastern Asia had almost no changes. Oman, Lebanon, India, Armenia, Azerbaijan, Indonesia, and the Philippines experienced a high loss of above 2.5 years in e0. Despite significant national and territorial variations, the decline of e0 in Asia was mostly from the age group of 60-79 years, followed by age groups of 80 + and 45-59 years; and age groups of children contributed little (i.e., 0-4 and 5-14 years old). Males suffered more losses than females in this pandemic. Asian nations saw less loss in e0 in the second year of the pandemic, i.e., 2020-2021, than in the first year, i.e., 2019-2020, but this recovery trend was not observed in Southern Asia and South-Eastern Asia. Countries from Central Asia and Western Asia, such as Kazakhstan, Armenia, Azerbaijan, Lebanon, and Oman, had extraordinarily more losses in e0 in the first year at ages around 70.

CONCLUSION

The COVID-19 pandemic had significantly affected e0 of Asian populations, and most contribution to the reduction of e0 came from the three older age groups, 60-79 years, 80 + years, and 45-59 years, with great variations across countries/territories. Our findings could have important implications for development of more resilient public health systems in Asian societies with better policy interventions for vulnerable demographic groups.

Determinants of WHO recommended COVID-19 prevention measures among pregnant women attending antenatal care during the third wave of COVID-19 in eastern Ethiopia, 2021

BACKGROUND

The novel coronavirus disease has emerged as the most pressing global health issue. In women with COVID-19 disease, pregnancy confers a substantial additional risk of morbidity and mortality.

OBJECTIVE

This study aimed to assess WHO-recommended COVID-19 prevention practices and determinant factors among pregnant women attending antenatal care during the third wave of COVID-19 in eastern Ethiopia.

METHODS

An Institutional-based cross-sectional study was conducted among 422 pregnant women attending antenatal care in Harar, from October 10 to November 10, 2021. The sample size was proportionally allocated to all healthcare facilities, then the study participants were selected using systematic random sampling. Descriptive summary statistics were done. Logistic regression analyses were computed to identify associations between dependent and independent variables. Variables with a p-value < 0.05 were declared statistically significant.

RESULT

Out of 422 pregnant women, 61.6% of them had good WHO Recommended COVID-19 Prevention Practices. Those with age 25-34 years (AOR: 9.7, 95%CI: 4.8, 19.3), age 35-44 years (AOR:4.8, 95%CI: 2.6, 9.03), monthly income > 10,000 ETB (AOR: 9.4, 95%CI: 2.1, 42.1), being a student (AOR: 10, 95%CI: 2.3, 47.1), having a good level of knowledge (AOR: 2.3, 95%CI:1.4, 3.8), and having ≥10 family members (AOR: 0.24, 95%CI: 0.06, 0.9) were found to have a significant association with WHO recommended prevention practice among pregnant women.

CONCLUSION

Overall, the WHO-recommended COVID-19 prevention practice among pregnant women attending antenatal care was good, but it needs improvement. In order to improve prevention practices among pregnant women, Harari Regional Health Bureau and other stakeholders should provide repeated, targeted, and tailored information to pregnant women and the community at large through different media.

Identifying age- and sex-specific COVID-19 mortality trends over time in six countries

OBJECTIVES

The COVID-19 pandemic is characterized by successive waves that each developed differently over time and through space. We aim to provide an in-depth analysis of the evolution of COVID-19 mortality during 2020 and 2021 in a selection of countries.

METHODS

We focus on five European countries and the United States. Using standardized and age-specific mortality rates, we address variations in COVID-19 mortality within and between countries, and demographic characteristics and seasonality patterns.

RESULTS

Our results highlight periods of acceleration and deceleration in the pace of COVID-19 mortality, with substantial differences across countries. Periods of stabilization were identified during summer (especially in 2020) among the European countries analyzed but not in the United States. The latter stands out as the study population with the highest COVID-19 mortality at young ages. In general, COVID-19 mortality is highest at old ages, particularly during winter. Compared with women, men have higher COVID-19 mortality rates at most ages and in most seasons.

CONCLUSION

There is seasonality in COVID-19 mortality for both sexes at all ages, characterized by higher rates during winter. In 2021, the highest COVID-19 mortality rates continued to be observed at ages 75+, despite vaccinations having targeted those ages specifically.

Coupling between infectious diseases leads to synchronization of their dynamics

Interactions between different diseases may change their dynamics. Thus, these interactions represent a source of uncertainty in the modeling of empirical data when the symptoms of both infections are hard to distinguish. We recall previously proposed models of interacting infections, generalizing them to non-symmetric scenarios, showing that both cooperative and competitive interactions lead to synchronization of the maximum fraction of infected individuals in their dynamics. We exemplify this framework with a model coupling the dynamics of COVID-19 and seasonal influenza, simulating cooperation, competition, and asymmetric interactions. We find that the coupling synchronizes both infections, with a stronger influence on the dynamics of influenza.

Integration of e-learning approaches in a post-pandemic learning environment - Norwegian nursing students' recommendations from an action research study

The covid-19 pandemic has profoundly led to changes in use of digital platforms, online teaching, and e-learning strategies. The aim of the present study was to examine how future pedagogical approaches can promote constructive learning environments and facilitate nursing students learning processes in future post-pandemic scenarios based on an action research study, which were conducted through several steps from January 2021 through January 2022 in a Bachelor of Nursing program at UiT the Arctic University of Norway. Students from the 2020 enrollment were invited to focus group interviews in March 2021. The interviews were transcribed, and content analyzed, resulting in concrete advice, which were implemented for the next enrollment. Students from the 2021 enrollment were invited to similar focus groups, resulting in refined advice, which will be presented. The pandemic situation resulted in extensive use of digital platforms for the 2020 enrollment. Students from this cohort described shock and disappointment connected to their study start. They expected a new life, meeting new friends and to develop a student identity, but their expectations were broken. The loss of social connections led to isolation and a weak link to the program and to the nursing profession. They recommended integrated training of theoretical and practical skills in small groups combined with short, well-prepared digital lectures equivalent to "flipped classroom" approaches. Implementing their advice, the 2021 enrollment's experience became different. This group was prepared for extensive use of digital platforms and gave future advise on increased flexibility and balance between the various learning activities centered around the clinical sessions. Based on these experiences during the pandemic, we suggest that digital platforms and e-learning strategies facilitate nursing students learning in combination with active and social learning environments.

Trends in gastrointestinal disease hospitalizations and outcomes during the first year of the coronavirus pandemic

BACKGROUND

The impact of the coronavirus on hospitalizations for gastrointestinal (GI) disease, particularly at a population level is understudied.

AIM

To investigate trends in hospitalizations, inpatient endoscopy resource utilization, and outcomes during the first year of the coronavirus pandemic and subsequent lockdowns.

METHODS

Using the California State Inpatient Database for 2018-2020, we explored year-to-year and 2020 month-to-month trends in hospitalizations, length of stay, and inpatient mortality (all-cause & viral pneumonia-specific) for common inpatient GI diagnoses including acute pancreatitis, diverticulitis, cholelithiasis, non-infectious gastroenteritis, upper and lower GI bleeding (LGIB), Clostridium difficile, viral gastroenteritis, inflammatory bowel disease, and acute cholangitis.

RESULTS

Disease-specific hospitalizations decreased for all included conditions except nonvariceal upper GI bleeding (NVUGIB), LGIB, and ulcerative colitis (UC) (ptrend < 0.0001). All-cause inpatient mortality was higher in 2020 vs 2019, for acute pancreatitis (P = 0.029), diverticulitis (P = 0.04), NVUGIB (P = 0.003), and Crohn’s disease (P = 0.004). In 2020, hospitalization rates were lowest in April, November, and December. There was no significant corresponding increase in inpatient mortality except in UC (ptrend = 0.048). Viral pneumonia and viral pneumonia complicated by respiratory failure increased (P < 0.001) among GI hospitalizations. Endoscopy utilization within 24 h of admission was unchanged for GI emergencies except NVUGIB (P < 0.001).

CONCLUSION

Our findings suggest that hospitalization rates for common GI conditions significantly declined in California during the COVID pandemic, particularly in April, November and December 2020. All-cause mortality was significantly higher among acute pancreatitis, diverticulitis, NVUGIB, and Crohn’s disease hospitalizations. Emergency endoscopy rates were mostly comparable between 2020 and 2019.

Were hospitals with sustained high performance more successful at reducing mortality during the pandemic's second wave?

BACKGROUND

In 2019, the COVID-19 pandemic emerged. Variation in COVID-19 patient outcomes between hospitals was later reported.

PURPOSE

This study aims to determine whether sustainers-hospitals with sustained high performance on Hospital Value-Based Purchasing Total Performance Score (HVBP-TPS)-more effectively responded to the pandemic and therefore had better patient outcomes.

METHODOLOGY

We calculated hospital-specific risk-standardized event rates using deidentified patient-level data from the UnitedHealth Group Clinical Discovery Database. HVBP-TPS from 2016 to 2019 were obtained from Centers for Medicare & Medicaid Services. Hospital characteristics were obtained from the American Hospital Association Annual Survey Database (2019), and county-level predictors were obtained from the Area Health Resource File. We use a repeated-measures regression model assuming an AR(1) type correlation structure to test whether sustainers had lower mortality rates than nonsustainers during the first wave (spring 2020) and the second wave (October to December 2020) of the pandemic.

RESULTS

Sustainers did not have significantly lower COVID-19 mortality rates during the first wave of the pandemic, but they had lower COVID-19 mortality rates during the second wave compared to nonsustainers. Larger hospitals, teaching hospitals, and hospitals with higher occupancy rates had higher mortality rates.

CONCLUSION

During the first wave of the pandemic, mortality rates did not differ between sustainers and nonsustainers. However, sustainers had lower mortality rates than nonsustainers in the second wave, most likely because of their knowledge management capabilities and existing structures and resources that enable them to develop new processes and routines to care for patients in times of crisis. Therefore, a consistently high level of performance over the years on HVBP-TPS is associated with high levels of performance on COVID-19 patient outcomes.

PRACTICE IMPLICATIONS

Investing in identifying the knowledge, processes, and resources that foster the dynamic capabilities needed to achieve superior performance in HVBP might enable hospitals to utilize these capabilities to adapt more effectively to future changes and uncertainty.

Backcasting COVID-19: a physics-informed estimate for early case incidence

It is widely accepted that the number of reported cases during the first stages of the COVID-19 pandemic severely underestimates the number of actual cases. We leverage delay embedding theorems of Whitney and Takens and use Gaussian process regression to estimate the number of cases during the first 2020 wave based on the second wave of the epidemic in several European countries, South Korea and Brazil. We assume that the second wave was more accurately monitored, even though we acknowledge that behavioural changes occurred during the pandemic and region- (or country-) specific monitoring protocols evolved. We then construct a manifold diffeomorphic to that of the implied original dynamical system, using fatalities or hospitalizations only. Finally, we restrict the diffeomorphism to the reported cases coordinate of the dynamical system. Our main finding is that in the European countries studied, the actual cases are under-reported by as much as 50%. On the other hand, in South Korea-which had a proactive mitigation approach-a far smaller discrepancy between the actual and reported cases is predicted, with an approximately 18% predicted underestimation. We believe that our backcasting framework is applicable to other epidemic outbreaks where (due to limited or poor quality data) there is uncertainty around the actual cases.

Evolving mortality and clinical outcomes of hospitalized subjects during successive COVID-19 waves in Catalonia, Spain

BACKGROUND

The changes in shield strategies, treatments, emergence variants, and healthcare pathways might shift the profile and outcome of patients hospitalized with COVID-19 in successive waves of the outbreak.

METHODS

We retrospectively analysed the characteristics and in-hospital outcomes of all patients admitted with COVID-19 in eight university hospitals of Catalonia (North-East Spain) between Feb 28, 2020 and Feb 28, 2021. Using a 7-joinpoint regression analysis, we split admissions into four waves. The main hospital outcomes included 30-day mortality and admission to intensive care unit (ICU).

FINDINGS

The analysis included 17,027 subjects admitted during the first wave (6800; 39.9%), summer wave (1807; 10.6%), second wave (3804; 22.3%), and third wave (4616; 27.1%). The highest 30-day mortality rate was reported during the first wave (17%) and decreased afterwards, remaining stable at 13% in the second and third waves (overall 30% reduction); the lowest mortality was reported during the summer wave (8%, 50% reduction). ICU admission became progressively more frequent during successive waves. In Cox regression analysis, the main factors contributing to differences in 30-day mortality were the epidemic wave, followed by gender, age, diabetes, chronic kidney disease, and neoplasms.

INTERPRETATION

Although in-hospital COVID-19 mortality remains high, it decreased substantially after the first wave and is highly dependent of patient's characteristics and ICU availability. Highest mortality reductions occurred during a wave characterized by younger individuals, an increasingly frequent scenario as vaccination campaigns progress.

FUNDING

This work did not receive specific funding.

Evaluating the potential of suburban and rural areas for tourism and recreation, including individual short-term tourism under pandemic conditions

Limited mobility and restrictions in social life caused by the COVID-19 pandemic changed people's recreational behavior and made them seek more contact with nature. As a result, the provision of new recreational spaces in the vicinity of cities gained importance. In conditions of social distancing, rural and suburban areas can be an attractive alternative to individual short-term tourism, satisfying the need for recreation and mental and physical health restoration of urban residents. In the study a methodology for assessing the tourist and recreational potential of the area (METPRET) concerning the recreational behaviors identified in the pandemic was proposed. It includes the Recreational Potential Index (RPI), which comprises four criteria: landscape values and socio-economic conditions; environmental protection; air quality; transportation accessibility. The application of the methodology is exemplified in the Mazovia Voivodeship, Poland. The research allows the determination of characteristics that potential recreation areas should have under pandemic conditions. The RPI makes it possible to select new rural and suburban areas attractive for short-term tourism. Designating additional recreational areas may contribute to the dispersion of users in existing green areas in cities, which is particularly important during a pandemic.

Dual-domain analysis of gun violence incidents in the United States

This paper applies new and recently introduced approaches to study trends in gun violence in the United States. We use techniques in both the time and frequency domain to provide a more complete understanding of gun violence dynamics. We analyze gun violence incidents on a state-by-state basis as recorded by the Gun Violence Archive. We have numerous specific phenomena of focus, including periodicity of incidents, locations in time where behavioral changes occur, and shifts in gun violence patterns since April 2020. First, we implement a recently introduced method of spectral density estimation for nonstationary time series to investigate periodicity on a state-by-state basis, including revealing where periodic behaviors change with time. We can also classify different patterns of behavioral changes among the states. We then aim to understand the most significant shifts in gun violence since numerous key events in 2020, including the COVID-19 pandemic, lockdowns, and periods of civil unrest. Our dual-domain analysis provides a more thorough understanding and challenges numerous widely held conceptions regarding the prevalence of gun violence incidents.

Analyzing the GHSI puzzle of whether highly developed countries fared worse in COVID-19

Global Health Security Index (GHSI) categories are formulated to assess the capacity of world countries to deal with infectious disease risks. Thus, higher values of these indices were expected to translate to lower COVID-19 severity. However, it turned out to be the opposite, surprisingly suggesting that higher estimated country preparedness to epidemics may lead to higher disease mortality. To address this puzzle, we: (i) use a model-derived measure of COVID-19 severity; (ii) employ a range of statistical learning approaches, including non-parametric machine learning methods; (iii) consider the overall excess mortality, in addition to official COVID-19 fatality counts. Our results suggest that the puzzle is, to a large extent, an artifact of oversimplified data analysis and a consequence of misclassified COVID-19 deaths, combined with the higher median age of the population and earlier epidemics onset in countries with high GHSI scores.

Comparison of the characteristics, morbidity and mortality of COVID-19 between first and second/third wave in a hospital setting in Lombardy: a retrospective cohort study

Coronavirus disease 2019 (COVID-19) represents a major health problem in terms of deaths and long-term sequelae. We conducted a retrospective cohort study at Montichiari Hospital (Brescia, Italy) to better understand the determinants of outcome in two different COVID-19 outbreaks. A total of 634 unvaccinated patients admitted from local emergency room to the Internal Medicine ward with a confirmed diagnosis of SARS-CoV-2 infection and a moderate-to-severe COVID-19 were included in the study. A group of 260 consecutive patients during SARS-CoV-2 first wave (from February to May 2020) and 374 consecutive patients during SARS-CoV-2 2nd/3rd wave (from October 2020 to May 2021) were considered. Demographic data were not significantly different between waves, except a lower prevalence of female sex during first wave. Mortality was significantly higher during the 1st wave than in the following periods (24.2% vs. 11%; p < 0.001). Time from symptoms onset to hospital admission was longer during first wave (8 ± 6 vs. 6 ± 4 days; p < 0.001), while in-hospital staying was significantly shorter (10 ± 14 vs. 15 ± 11 days; p < 0.001). Other significant differences were a larger use of corticosteroids and low-molecular weight heparin as well less antibiotic prescription during the second wave. Respiratory, bio-humoral and X-ray scores were significantly poorer at the time of admission in first-wave patients. After a multivariate regression analysis, C-reactive protein and procalcitonin values, % fraction of inspired oxygen on admission to the Internal Medicine ward and length of hospital stay and duration of symptoms were the strongest predictors of outcome. Concomitant anti-hypertensive treatment (including ACE-inhibitors and angiotensin-receptor blockers) did not affect the outcome. In conclusion, our data suggest that earlier diagnosis, timely hospital admission and rational use of the therapeutic options reduced the systemic inflammatory response and were associated to a better outcome during the 2nd/3rd wave.

Preventive control strategy on second wave of Covid-19 pandemic model incorporating lock-down effect

This study presents an optimal control strategy through a mathematical model of the Covid-19 outbreak without lock-down. The pandemic model analyses the lock-down effect without control strategy based on the current scenario of second wave data to control the rapid spread of the virus. The pandemic model has been discussed with respect to the basic reproduction number and stability analysis of disease-free and endemic equilibrium. A new optimal control problem with treatment is framed to minimize the vulnerable situation of the second wave. This system is applied to study the effects of vaccines and treatment controls. Numerical solutions and the graphical presentation of the results predict the fate of India’s second wave situation on account of the control strategy. Lastly, a comparative study with control and without control has been analysed for the exposed phase, infective phase, and recovery phase to understand the effectiveness of the controls. This model is used to estimate the total number of infected and active cases, deaths, and recoveries in order to control the disease using this system and studying the effects of vaccines and treatment controls.

Reduction in risk of death among patients admitted with COVID-19 between first and second epidemic waves in New York City

Background

Many regions have experienced successive epidemic waves of COVID-19 since the emergence of SARS-CoV-2 with heterogeneous differences in mortality. Elucidating factors differentially associated with mortality between epidemic waves may inform clinical and public health strategies.

Methods

We examined clinical and demographic data among patients admitted with COVID-19 during the first (March-August 2020) and second (August 2020-March 2021) epidemic waves at an academic medical center in New York City.

Results

Hospitalized patients (N = 4631) had lower overall and 30-day in-hospital mortality, defined as death or discharge to hospice, during the second wave (14% and 11%) than the first (22% and 21%). The wave 2 in-hospital mortality decrease persisted after adjusting for several potential confounders. Adjusting for the volume of COVID-19 admissions, a measure of health system strain, accounted for the mortality difference between waves. Several demographic and clinical patient factors were associated with an increased risk of mortality independent of wave; SARS-CoV-2 cycle threshold, Do-Not-Intubate status, oxygen requirement, and intensive care unit admission.

Conclusions

This work suggests that increased in-hospital mortality rates observed during the first epidemic wave were partly due to strain on hospital resources. Preparations for future epidemics should prioritize evidence-based patient risks, treatment paradigms, and approaches to augment hospital capacity.

COVID-19 in Iran: clinical presentations and outcomes in three different surges of COVID-19 infection

Background

A few studies compared the characteristics and outcomes of COVID-19 patients during the first and second surges of the disease. We aimed to describe the clinical features and outcomes of COVID-19 patients across the first, second, and third surges of the disease in Tehran, Iran.

Method

We conducted a retrospective cohort study of patients with COVID-19 admitted to Sina hospital in Tehran, Iran, during three surges of COVID-19 from February 16 to October 28, 2020.

Result

Surge 1 patients were younger with more prevalence of hypertension. They also presented with significantly higher oxygen saturation, systolic blood pressure, and respiratory rate on admission. Patients had higher levels of neutrophil to lymphocyte ratio, Urea, CRP, and ESR, in surge 2. The incidence of dyspnea, chest pain, and neurological manifestations followed a significant increasing trend from surge 1 to surge 3. There was no difference in severity and in-hospital mortality between the surges. However, the length of hospital stays and acute cardiac injury (ACI) was less in surge 1 and acute respiratory distress syndrome (ARDS) in surge 2 than in other surges.

Conclusion

Patients did not significantly differ in disease severity, ICU admission, and mortality between surges; however, length of hospital stay and ACI increased during surges, and the number of patients developing ARDS was significantly less in surge 2 compared to other peaks.

Multifractal Cross-Correlations of Bitcoin and Ether Trading Characteristics in the Post-COVID-19 Time

Unlike price fluctuations, the temporal structure of cryptocurrency trading has seldom been a subject of systematic study. In order to fill this gap, we analyse detrended correlations of the price returns, the average number of trades in time unit, and the traded volume based on high-frequency data representing two major cryptocurrencies: bitcoin and ether. We apply the multifractal detrended cross-correlation analysis, which is considered the most reliable method for identifying nonlinear correlations in time series. We find that all the quantities considered in our study show an unambiguous multifractal structure from both the univariate (auto-correlation) and bivariate (cross-correlation) perspectives. We looked at the bitcoin–ether cross-correlations in simultaneously recorded signals, as well as in time-lagged signals, in which a time series for one of the cryptocurrencies is shifted with respect to the other. Such a shift suppresses the cross-correlations partially for short time scales, but does not remove them completely. We did not observe any qualitative asymmetry in the results for the two choices of a leading asset. The cross-correlations for the simultaneous and lagged time series became the same in magnitude for the sufficiently long scales.

Quantifying the hidden impact of COVID-19 pandemic: The cytology perspective

Background

The burden of the COVID‐19 pandemic is often enumerated in lives lost, but the strain on health care resources and mobility limitations contributed to the burden of non–COVID related disease. In this study, we evaluated the impact of the pandemic through a time series review of cytology samples.

Methods

Pathology reports for all cytology specimens received from January 2019 through April 2021 at our institution were reviewed. Time series analysis was performed using moving averages, time trend analysis, cross‐correlation, and tests of homogeneity.

Results

During the first peak of the pandemic (March–June 2020), breakpoint analysis showed a downward shift in the number of gynecologic (−89.4%) and non‐gynecologic (−70.4%) cytology specimens within a week of declaration of an emergency. Cross‐correlation analysis showed a relationship between sample numbers and COVID‐19 cases during the initial phase of the pandemic (April–June 2020). During the second surge (October 2020–April 2021), despite the higher incidence of COVID‐19, there was a smaller impact on cytology samples (−20.1% and − 24.8% for gynecologic and non‐gynecologic samples, respectively). During the first 3 months of the pandemic, 154 fewer malignant cases were identified compared with the prior year. Although specimen numbers slowly returned to baseline following the first wave of the pandemic, the earlier decline in malignant diagnoses was not offset during the study period.

Conclusions

The deleterious effects of COVID‐19 extend beyond direct mortality attributed to the disease. The significant decrease in diagnostic cytology specimens during this period has profound implications including delayed care and missed disease.

The COVID‐19 pandemic has led to dramatic reductions in cytology specimen volumes. Although prior studies have noted an increased rate of malignant diagnoses, our data demonstrate that this did not offset the dramatic reduction in specimen numbers. The COVID‐19 pandemic has contributed to delayed and missed diagnoses of malignancy.

COVID-19 waves: variant dynamics and control

The waves of COVID-19 infections driven by its variants continue to nullify the success we achieved through efficacious vaccines, social restrictions, testing and quarantine policies. This paper models the two major variants-driven waves by two sets of susceptible-infected-quarantined-recovered-vaccinated-deceased coupled dynamics that are modulated by the three main interventions: vaccination, quarantine and restrictions. This \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$SI^2Q^2R^2VD$$\end{document}SI2Q2R2VD system is used to demonstrate that the second major novel coronavirus wave in the US is caused by the delta variant and the corresponding rapid surge in infectious cases is driven by the unvaccinated pool of the populace. Next, a feedback control based planned vaccination strategy is derived and is shown to be able to suppress the surge in infections effectively.

Predicting COVID-19 outcomes from clinical and laboratory parameters in an intensive care facility during the second wave of the pandemic in South Africa

Background

The second wave of coronavirus disease 2019 (COVID-19) in South Africa was caused by the Beta variant of severe acute respiratory syndrome coronavirurus-2. This study aimed to explore clinical and biochemical parameters that could predict outcome in patients with COVID-19.

Methods

A prospective study was conducted between 5 November 2020 and 30 April 2021 among patients with confirmed COVID-19 admitted to the intensive care unit (ICU) of a tertiary hospital. The Cox proportional hazards model in Stata 16 was used to assess risk factors associated with survival or death. Factors with P<0.05 were considered significant.

Results

Patients who died were found to have significantly lower median pH (P<0.001), higher median arterial partial pressure of carbon dioxide (P<0.001), higher D-dimer levels (P=0.001), higher troponin T levels (P=0.001), higher N-terminal-prohormone B-type natriuretic peptide levels (P=0.007) and higher C-reactive protein levels (P=0.010) compared with patients who survived. Increased standard bicarbonate (HCO3std) was associated with lower risk of death (hazard ratio 0.96, 95% confidence interval 0.93-0.99).

Conclusions

The mortality of patients with COVID-19 admitted to the ICU was associated with elevated D-dimer and a low HCO3std level. Large studies are warranted to increase the identification of patients at risk of poor prognosis, and to improve the clinical approach.

The impact of COVID-19 pandemic on hand surgery: a FESSH perspective

COVID-19 has affected us all. The following collection of short essays highlights various aspects of the pandemic and how it has impacted hand surgery and lessons learned, from the perspective of the Federation of European Societies for Surgery of the Hand (FESSH) Executive Committee members. A range of topics were individually chosen by each of the five committee members and presented.

Omicron-included mutation-induced changes in epitopes of SARS-CoV-2 spike protein and effectiveness assessments of current antibodies

The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is spreading globally and continues to rage, posing a serious threat to human health and life quality. Antibody therapy and vaccines both have shown great efficacy in the prevention and treatment of COVID-19, whose development progress and adaptation range have attracted wide attention. However, with the emergence of variant strains of SARS-CoV-2, the neutralization activity of therapeutic or vaccine-induced antibodies may be reduced, requiring long-term virus monitoring and drug upgrade in response to its evolution. In this paper, conformational changes including continuous epitopes (CPs), discontinuous epitopes (DPs) and recognition interfaces of the three representative SARS-CoV-2 spike protein (SP) mutants (i.e., the Delta (B.1.617.2), Mu (B.1.621) and Omicron (B.1.1.529) strains), were analyzed to evaluate the effectiveness of current mainstream antibodies. The results showed that the conformation of SP wild type (WT) and mutants both remained stable, while the local antigenic epitopes underwent significant changes. Sufficient flexibility of SP CPs is critical for effective antibody recognition. The DPs of Delta, Mu and Omicron variants have showed stronger binding to human angiotensin converting enzyme-2 (hACE2) than WT; the possible drug resistance mechanisms of antibodies against three different epitopes (i.e., NTD_DP, RBD1_DP and RBD2_DP) were also proposed, respectively; the RBD2 of Delta, NTD of Mu, NTD and RBD2 of Omicron are deserve more attention in the subsequent design of next-generation vaccines. The simulation results not only revealed structural characteristics of SP antigenic epitopes, but also provided guidance for antibody modification, vaccine design and effectiveness evaluation.

Mortality rates among COVID-19 patients hospitalised during the first three waves of the epidemic in Milan, Italy: A prospective observational study

Introduction

This paper describes how mortality among hospitalised COVID-19 patients changed during the first three waves of the epidemic in Italy.

Methods

This prospective cohort study used the Kaplan-Meier method to analyse the time-dependent probability of death of all of the patients admitted to a COVID-19 referral centre in Milan, Italy, during the three consecutive periods of: 21 February-31 July 2020 (first wave, W1), 1 August 2020–31 January 2021 (second wave, W2), and 1 February-30 April 2021 (third wave, W3). Cox models were used to examine the association between death and the period of admission after adjusting for age, biological sex, the time from symptom onset to admission, disease severity upon admission, obesity, and the comorbidity burden.

Results

Of the 2,023 COVID-19 patients admitted to our hospital during the study period, 553 (27.3%) were admitted during W1, 838 (41.5%) during W2, and 632 (31.2%) during W3. The crude mortality rate during W1, W2 and W3 was respectively 21.3%, 23.7% and 15.8%. After adjusting for potential confounders, hospitalisation during W2 or W3 was independently associated with a significantly lower risk of death than hospitalisation during W1 (adjusted hazard ratios [AHRs]: 0.75, 95% confidence interval [CI] 0.59–0.95, and 0.58, 95% CI 0.44–0.77). Among the patients aged >75 years, there was no significant difference in the probability of death during the three waves (AHRs during W2 and W3 vs W1: 0.93, 95% CI 0.65–1.33, and 0.88, 95% CI 0.59–1.32), whereas those presenting with critical disease during W2 and W3 were at significantly lower risk of dying than those admitted during W1 (AHRs 0.61, 95% CI 0.43–0.88, and 0.44, 95% CI 0.28–0.70).

Conclusions

Hospitalisation during W2 and W3 was associated with a reduced risk of COVID-19 death in comparison with W1, but there was no difference in survival probability in patients aged >75 years.

Comparing the dynamics of COVID-19 infection and mortality in the United States, India, and Brazil

This paper compares and contrasts the spread and impact of COVID-19 in the three countries most heavily impacted by the pandemic: the United States (US), India and Brazil. All three of these countries have a federal structure, in which the individual states have largely determined the response to the pandemic. Thus, we perform an extensive analysis of the individual states of these three countries to determine patterns of similarity within each. First, we analyse structural similarity and anomalies in the trajectories of cases and deaths as multivariate time series. Next, we study the lengths of the different waves of the virus outbreaks across the three countries and their states. Finally, we investigate suitable time offsets between cases and deaths as a function of the distinct outbreak waves. In all these analyses, we consistently reveal more characteristically distinct behaviour between US and Indian states, while Brazilian states exhibit less structure in their wave behaviour and changing progression between cases and deaths.

Comparison of characteristics and outcomes of patients admitted to hospital with COVID-19 during wave 1 and wave 2 of the current pandemic

In this study of patients admitted with COVID-19, we examined differences between the two waves in patient characteristics and outcomes. Data were collected from the first COVID-19 admission to the end of study (01/03/2020-31/03/2021). Data were adjusted for age and sex and presented as odds ratios (OR) with 95% confidence intervals (CI). Among 12,471 admissions, 1452 (11.6%) patients were diagnosed with COVID-19. On admission, the mean (± SD) age of patients with other causes was 68.3 years (± 19.8) and those with COVID-19 in wave 1 was 69.4 years (± 18.0) and wave 2 was 66.2 years (± 18.4). Corresponding ages at discharge were 67.5 years (± 19.7), 63.9 years (± 18.0) and 62.4 years (± 18.0). The highest proportion of total admissions was among the oldest group (≥ 80 years) in wave 1 (35.0%). When compared with patients admitted with other causes, those admitted with COVID-19 in wave 1 and in wave 2 were more frequent in the 40-59 year band: 20.8, 24.6 and 30.0%; consisted of more male patients: 47.5, 57.6 and 58.8%; and a high LACE (Length of stay, Acuity of admission, Comorbidity and Emergency department visits) index (score ≥ 10): 39.4, 61.3 and 50.3%. Compared to wave-2 patients, those admitted in wave 1 had greater risk of death in hospital: OR = 1.58 (1.18-2.12) and within 30 days of discharge: OR = 2.91 (1.40-6.04). Survivors of COVID-19 in wave 1 stayed longer in hospital (median = 6.5 days; interquartile range = 2.9-12.0) as compared to survivors from wave 2 (4.5 days; interquartile range = 1.9-8.7). Patient characteristics differed significantly between the two waves of COVID-19 pandemic. There was an improvement in outcomes in wave 2, including shorter length of stay in hospital and reduction of mortality.

Reduction in risk of death among patients admitted with COVID-19 between first and second epidemic waves in New York City

Many regions have experienced successive epidemic waves of COVID-19 since the emergence of SARS-CoV-2 with heterogeneous differences in mortality. Elucidating factors differentially associated with mortality between epidemic waves may inform clinical and public health strategies. We examined clinical and demographic data among patients admitted with COVID-19 during the first (March-June 2020) and second (December 2020-March 2021) epidemic waves at an academic medical center in New York City. Hospitalized patients (N=4631) had lower mortality during the second wave (14%) than the first (23%). Patients in the second wave had a lower 30-day mortality (Hazard Ratio (HR) 0.52, 95% CI 0.44, 0.61) than those in the first wave. The mortality decrease persisted after adjusting for confounders except for the volume of COVID-19 admissions (HR 0.88, 95% CI 0.70, 1.11), a measure of health system strain. Several demographic and clinical patient factors were associated with an increased risk of mortality independent of wave.

Article summary

Using clinical and demographic data from COVID-19 hospitalizations at a tertiary New York City medical center, we show that a reduction in mortality during the second epidemic wave was associated with decreased strain on healthcare resources.

Clinical Frailty Scale (CFS) indicated frailty is associated with increased in-hospital and 30-day mortality in COVID-19 patients: a systematic review and meta-analysis

BACKGROUND

The concept of frailty provides an age-independent, easy-to-use tool for risk stratification. We aimed to summarize the evidence on the efficacy of frailty tools in risk assessment in COVID-19 patients.

METHODS

The protocol was registered (CRD42021241544). Studies reporting on frailty in COVID-19 patients were eligible. The main outcomes were mortality, length of hospital stay (LOH) and intensive care unit (ICU) admission in frail and non-frail COVID-19 patients. Frailty was also compared in survivors and non-survivors. Five databases were searched up to 24th September 2021. The QUIPS tool was used for the risk of bias assessment. Odds ratios (OR) and weighted mean differences (WMD) were calculated with 95% confidence intervals (CI) using a random effect model. Heterogeneity was assessed using the I² and χ² tests.

RESULTS

From 3640 records identified, 54 were included in the qualitative and 42 in the quantitative synthesis. Clinical Frailty Scale (CFS) was used in 46 studies, the Hospital Frailty Risk Score (HFRS) by 4, the Multidimensional Prognostic Index (MPI) by 3 and three studies used other scores. We found that patients with frailty (CFS 4-9 or HFRS ≥ 5) have a higher risk of mortality (CFS: OR: 3.12; CI 2.56-3.81; HFRS OR: 1.98; CI 1.89-2.07). Patients with frailty (CFS 4-9) were less likely to be admitted to ICU (OR 0.28, CI 0.12-0.64). Quantitative synthesis for LOH was not feasible. Most studies carried a high risk of bias.

CONCLUSIONS

As determined by CFS, frailty is strongly associated with mortality; hence, frailty-based patient management should be included in international COVID-19 treatment guidelines. Future studies investigating the role of frailty assessment on deciding ICU admission are strongly warranted.

COVID-19: average time from infection to death in Poland, USA, India and Germany

There are many discussions in the media about an interval (delay) from the time of the infections to deaths. Apart from the curiosity of the researchers, defining this time interval may, under certain circumstances, be of great organizational and economic importance. The study considers an attempt to determine this difference through the correlations of shifted time series and a specific bootstrapping that allows finding the distance between local maxima on the series under consideration. We consider data from Poland, the USA, India and Germany. The median of the difference's distribution is quite consistent for such diverse countries. The main conclusion of our research is that the searched interval has rather a multimodal form than unambiguously determined.

In search of peak human athletic potential: A mathematical investigation

This paper applies existing and new approaches to study trends in the performance of elite athletes over time. We study both track and field scores of men and women athletes on a yearly basis from 2001 to 2019, revealing several trends and findings. First, we perform a detailed regression study to reveal the existence of an "Olympic effect," where average performance improves during Olympic years. Next, we study the rate of change in athlete performance and fail to reject the notion that athlete scores are leveling off, at least among the top 100 annual scores. Third, we examine the relationship in performance trends among men and women's categories of the same event, revealing striking similarity, together with some anomalous events. Finally, we analyze the geographic composition of the world's top athletes, attempting to understand how the diversity by country and continent varies over time across events. We challenge a widely held conception of athletics that certain events are more geographically dominated than others. Our methods and findings could be applied more generally to identify evolutionary dynamics in group performance and highlight spatiotemporal trends in group composition.

Evaluating the Association Between Comorbidities and COVID-19 Severity Scoring on Chest CT Examinations Between the Two Waves of COVID-19: An Imaging Study Using Artificial Intelligence

Background

Coronavirus disease 2019 (COVID-19) has accounted for over 352 million cases and five million deaths globally. Although it affects populations across all nations, developing or transitional, of all genders and ages, the extent of the specific involvement is not very well known. This study aimed to analyze and determine how different were the first and second waves of the COVID-19 pandemic by assessing computed tomography severity scores (CT-SS).

Methodology

This was a retrospective, cross-sectional, observational study performed at a tertiary care Institution. We included 301 patients who underwent CT of the chest between June and October 2020 and 1,001 patients who underwent CT of the chest between February and April 2021. All included patients were symptomatic and were confirmed to be COVID-19 positive. We compared the CT-SS between the two datasets. In addition, we analyzed the distribution of CT-SS concerning age, comorbidities, and gender, as well as their differences between the two waves of COVID-19. Analysis was performed using the SPSS version 22 (IBM Corp., Armonk, NY, USA). The artificial intelligence platform U-net architecture with Xception encoder was used in the analysis.

Results

The study data revealed that while the mean CT-SS did not differ statistically between the two waves of COVID-19, the age group most affected in the second wave was almost a decade younger. While overall the disease had a predilection toward affecting males, our findings showed that females were more afflicted in the second wave of COVID-19 compared to the first wave. In particular, the disease had an increased severity in cases with comorbidities such as hypertension, diabetes mellitus, bronchial asthma, and tuberculosis.

Conclusions

This assessment demonstrated no significant difference in radiological severity score between the two waves of COVID-19. The secondary objective revealed that the two waves showed demographical differences. Hence, we iterate that no demographical subset of the population should be considered low risk as the disease manifestation was heterogeneous.

Harnessing Artificial Intelligence to assess the impact of nonpharmaceutical interventions on the second wave of the Coronavirus Disease 2019 pandemic across the world

In the present paper, we aimed to determine the influence of various non-pharmaceutical interventions (NPIs) enforced during the first wave of COVID-19 across countries on the spreading rate of COVID-19 during the second wave. For this purpose, we took into account national-level climatic, environmental, clinical, health, economic, pollution, social, and demographic factors. We estimated the growth of the first and second wave across countries by fitting a logistic model to daily-reported case numbers, up to the first and second epidemic peaks. We estimated the basic and effective (second wave) reproduction numbers across countries. Next, we used a random forest algorithm to study the association between the growth rate of the second wave and NPIs as well as pre-existing country-specific characteristics. Lastly, we compared the growth rate of the first and second waves of COVID-19. The top three factors associated with the growth of the second wave were body mass index, the number of days that the government sets restrictions on requiring facial coverings outside the home at all times, and restrictions on gatherings of 10 people or less. Artificial intelligence techniques can help scholars as well as decision and policy-makers estimate the effectiveness of public health policies, and implement "smart" interventions, which are as efficacious as stringent ones.

Estimating a continuously varying offset between multivariate time series with application to COVID-19 in the United States

This paper introduces new methods to track the offset between two multivariate time series on a continuous basis. We then apply this framework to COVID-19 counts on a state-by-state basis in the United States to determine the progression from cases to deaths as a function of time. Across multiple approaches, we reveal an "up-down-up" pattern in the estimated offset between reported cases and deaths as the pandemic progresses. This analysis could be used to predict imminent increased load on a healthcare system and aid the allocation of additional resources in advance.

Collective correlations, dynamics, and behavioural inconsistencies of the cryptocurrency market over time

This paper introduces new methods to study behaviours among the 52 largest cryptocurrencies between 01-01-2019 and 30-06-2021. First, we explore evolutionary correlation behaviours and apply a recently proposed turning point algorithm to identify regimes in market correlation. Next, we inspect the relationship between collective dynamics and the cryptocurrency market size-revealing an inverse relationship between the size of the market and the strength of collective dynamics. We then explore the time-varying consistency of the relationships between cryptocurrencies' size and their returns and volatility. There, we demonstrate that there is greater consistency between size and volatility than size and returns. Finally, we study the spread of volatility behaviours across the market changing with time by examining the structure of Wasserstein distances between probability density functions of rolling volatility. We demonstrate a new phenomenon of increased uniformity in volatility during market crashes, which we term volatility dispersion.

COVID-19 Propagation Model Based on Economic Development and Interventions

In order to understand the influencing factors affecting the COVID-19 propagation, and analyze the development trend of the epidemic situation in the world, COVID-19 propagation model to simulate the COVID-19 propagation in the population is proposed in this paper. First of all, this paper analyzes the economic factors and interventions affecting the COVID-19 propagation in various different countries. Then, the touch number for COVID-19 High-risk Population Dynamic Network in this paper was redefined, and it predicts and analyzes the development trend of the epidemic situation in different countries. The simulation data and the published confirmed data by the world health organization could fit well, which also verified the reliability of the model. Finally, this paper also analyzes the impact of public awareness of prevention on the control of the epidemic. The analysis shows that increasing the awareness of prevention, timely and early adoption of protective measures such as wearing masks, and reducing travel can greatly reduce the risk of infection and the outbreak scale.

The mechanism of governments' and individuals' influence on protective behaviours during the second wave of COVID-19: a multiple mediation model

Background: The SARS-CoV-2 virus continues to spread and resurge globally with signs of a second wave, despite actions by governments to curb the COVID-19 pandemic. However, evidence-based strategies to combat COVID-19 recurrence are poorly documented. Objective: To reveal how governments and individuals should act to effectively cope with future waves, this study proposed a preventive model of COVID-19 resurgence. Method: A questionnaire survey was conducted among 1,137 residents of Beijing, where the epidemic reoccurred. Structural equation model was used to explore the mechanism among government intervention, perceived efficacy, positive emotions, posttraumatic growth (PTG) and protective behaviours. Results: Data analysis revealed that during COVID-19 resurgence, government intervention could directly and indirectly influence protective behaviours through individual factors (i.e. perceived efficacy, positive emotions), and PTG could mediate the indirect pathway to protective behaviours. Conclusions: These findings implied that government intervention needs to be integrated with individual factors to effectively control repeated COVID-19 outbreaks.

Predicting regional COVID-19 hospital admissions in Sweden using mobility data

The transmission of COVID-19 is dependent on social mixing, the basic rate of which varies with sociodemographic, cultural, and geographic factors. Alterations in social mixing and subsequent changes in transmission dynamics eventually affect hospital admissions. We employ these observations to model and predict regional hospital admissions in Sweden during the COVID-19 pandemic. We use an SEIR-model for each region in Sweden in which the social mixing is assumed to depend on mobility data from public transport utilisation and locations for mobile phone usage. The results show that the model could capture the timing of the first and beginning of the second wave of the pandemic 3 weeks in advance without any additional assumptions about seasonality. Further, we show that for two major regions of Sweden, models with public transport data outperform models using mobile phone usage. We conclude that a model based on routinely collected mobility data makes it possible to predict future hospital admissions for COVID-19 3 weeks in advance.

Extracorporeal Cytokine Removal in Critically Ill COVID-19 Patients: A Case Series

Introduction: Extracorporeal hemoadsorption (HA) is a potential adjunctive therapy in severe cases of COVID-19 associated pneumonia. In this retrospective study we report data from critically ill patients treated with HA during the first and second wave of the pandemic. Patients and Methods: All patients, who received HA therapy with CytoSorb within the first 96 h of intensive care unit (ICU) admission without hospital-acquired bacterial superinfection, were included. Clinical and laboratory data were collected: on admission, before (T_B) and after (T_A) HA therapy. Results: Out of the 367 COVID-19 cases, 13 patients were treated with CytoSorb, also requiring mechanical ventilation and renal replacement therapy. All patients were alive at the end of HA, but only 3 survived hospital stay. From T_B-T_A there was a tendency of decreasing norepinephrine requirement: 193.7 [IQR: 34.8-270.4] to 50.2 [6.5-243.5] ug/kg/day and increasing PaO2/FiO2 ratio 127.8 (95% CI: 96.0-159.6) to 155.0 (115.3-194.6) mmHg but they did not reach statistical significance (p = 0.14 and 0.58, respectively). Treatment related adverse events were not reported. Conclusion: The treatment was well-tolerated, and there was a tendency toward an improvement in vasopressor need and oxygenation during the course of HA. These observations render the need for prospective randomized trials.

Cardiac inflammation and microvascular procoagulant changes are decreased in second wave compared to first wave deceased COVID-19 patients

Background

Compelling evidence has shown cardiac involvement in COVID-19 patients. However, the overall majority of these studies use data obtained during the first wave of the pandemic, while recently differences have been reported in disease course and mortality between first- and second wave COVID-19 patients. The aim of this study was to analyze and compare cardiac pathology between first- and second wave COVID-19 patients.

Methods

Autopsied hearts from first- (n = 15) and second wave (n = 10) COVID-19 patients and from 18 non-COVID-19 control patients were (immuno)histochemically analyzed. CD45+ leukocyte, CD68+ macrophage and CD3+ T lymphocyte infiltration, cardiomyocyte necrosis and microvascular thrombosis were quantified. In addition, the procoagulant factors Tissue Factor (TF), Factor VII (FVII), Factor XII (FXII), the anticoagulant protein Dipeptidyl Peptidase 4 (DPP4) and the advanced glycation end-product N(^ε)-Carboxymethyllysine (CML), as markers of microvascular thrombogenicity and dysfunction, were quantified.

Results

Cardiac inflammation was significantly decreased in second wave compared to first wave COVID-19 patients, predominantly related to a decrease in infiltrated lymphocytes and the occurrence of lymphocytic myocarditis. This was accompanied by significant decreases in cardiomyocyte injury and microvascular thrombosis. Moreover, microvascular deposits of FVII and CML were significantly lower in second wave compared to first wave COVID-19 patients.

Conclusions

These results show that in our cohort of fatal COVID-19 cases cardiac inflammation, cardiomyocyte injury and microvascular thrombogenicity were markedly decreased in second wave compared to first wave patients. This may reflect advances in COVID-19 treatment related to an increased use of steroids in the second COVID-19 wave.

Translational suppression of SARS-COV-2 ORF8 protein mRNA as a Viable therapeutic target against COVID-19: Computational studies on potential roles of isolated compounds from Clerodendrum volubile leaves

The open reading frame 8 (ORF8) protein of SARS-CoV-2 has been implicated in the onset of cytokine storms, which are responsible for the pathophysiology of COVID-19 infection. The present study investigated the potential of isolated compounds from Clerodendrum volubile leaves to stall oxidative bursts in vitro and interact with ORF8 mRNA segments of the SARS-CoV-2 whole genome using computational tools. Five compounds, namely, harpagide, 1-(3-methyl-2-butenoxy)-4-(1-propenyl)benzene, ajugoside, iridoid glycoside and erucic acid, were isolated from C. volubile leaves, and their structures were elucidated using conventional spectroscopy tools. Iridoid glycoside is being reported for the first time and is thus regarded as a new compound. The ORF8 mRNA sequences of the translation initiation sites (TIS) and translation termination sites (TTSs) encoding ORF8 amino acids were retrieved from the full genome of SARS-CoV-2. Molecular docking studies revealed strong molecular interactions of the isolated compounds with the TIS and TTS of ORF8 mRNA. Harpagide showed the strongest binding affinity for TIS, while erucic acid was the strongest for TTS. The immunomodulatory potentials of the isolated compounds were investigated on neutrophil phagocytic respiratory bursts using luminol-amplified chemiluminescence technique. The compounds significantly inhibited oxidative burst, with 1-(3-methyl-2-butenoxy)-4-(1-propenyl)benzene having the best activity. Ajugoside and erucic acid showed significant inhibitory activity on T-cell proliferation. These results indicate the potential of C. volubile compounds as immunomodulators and can be utilized to curb cytokine storms implicated in COVID-19 infection. These potentials are further corroborated by the strong interactions of the compounds with the TIS and TTS of ORF8 mRNA from the SARS-CoV-2 whole genome.

Epidemiological comparison of the first and second waves of the COVID-19 pandemic in Nigeria, February 2020-April 2021

Background

With reports of surges in COVID-19 case numbers across over 50 countries, country-level epidemiological analysis is required to inform context-appropriate response strategies for containment and mitigation of the outbreak. We aimed to compare the epidemiological features of the first and second waves of COVID-19 in Nigeria.

Methods

We conducted a retrospective analysis of the Surveillance Outbreak Response Management and Analysis System data of the first and second epidemiological waves, which were between 27 February and 24 October 2020, and 25 October 2020 to 3 April 2021, respectively. Descriptive statistical measures including frequencies and percentages, test positivity rate (TPR), cumulative incidence (CI) and case fatality rates (CFRs) were compared. A p value of <0.05 was considered statistically significant. All statistical analyses were carried out in STATA V.13.

Results

There were 802 143 tests recorded during the study period (362 550 and 439 593 in the first and second waves, respectively). Of these, 66 121 (18.2%) and 91 644 (20.8%) tested positive in the first and second waves, respectively. There was a 21.3% increase in the number of tests conducted in the second wave with TPR increasing by 14.3%. CI during the first and second waves were 30.3/100 000 and 42.0/100 000 respectively. During the second wave, confirmed COVID-19 cases increased among females and people 30 years old or younger and decreased among urban residents and individuals with travel history within 14 days of sample collection (p value <0.001). Most confirmed cases were asymptomatic at diagnosis during both waves: 74.9% in the first wave; 79.7% in the second wave. CFR decreased during the second wave (0.7%) compared with the first wave (1.8%).

Conclusion

Nigeria experienced a larger but less severe second wave of COVID-19. Continued implementation of public health and social measures is needed to mitigate the resurgence of another wave.

Social stress drives the multi-wave dynamics of COVID-19 outbreaks

The dynamics of epidemics depend on how people's behavior changes during an outbreak. At the beginning of the epidemic, people do not know about the virus, then, after the outbreak of epidemics and alarm, they begin to comply with the restrictions and the spreading of epidemics may decline. Over time, some people get tired/frustrated by the restrictions and stop following them (exhaustion), especially if the number of new cases drops down. After resting for a while, they can follow the restrictions again. But during this pause the second wave can come and become even stronger then the first one. Studies based on SIR models do not predict the observed quick exit from the first wave of epidemics. Social dynamics should be considered. The appearance of the second wave also depends on social factors. Many generalizations of the SIR model have been developed that take into account the weakening of immunity over time, the evolution of the virus, vaccination and other medical and biological details. However, these more sophisticated models do not explain the apparent differences in outbreak profiles between countries with different intrinsic socio-cultural features. In our work, a system of models of the COVID-19 pandemic is proposed, combining the dynamics of social stress with classical epidemic models. Social stress is described by the tools of sociophysics. The combination of a dynamic SIR-type model with the classical triad of stages of the general adaptation syndrome, alarm-resistance-exhaustion, makes it possible to describe with high accuracy the available statistical data for 13 countries. The sets of kinetic constants corresponding to optimal fit of model to data were found. These constants characterize the ability of society to mobilize efforts against epidemics and maintain this concentration over time and can further help in the development of management strategies specific to a particular society.

Trends in COVID-19 prevalence and mortality: A year in review

This paper introduces new methods to study the changing dynamics of COVID-19 cases and deaths among the 50 worst-affected countries throughout 2020. First, we analyse the trajectories and turning points of rolling mortality rates to understand at which times the disease was most lethal. We demonstrate five characteristic classes of mortality rate trajectories and determine structural similarity in mortality trends over time. Next, we introduce a class of virulence matrices to study the evolution of COVID-19 cases and deaths on a global scale. Finally, we introduce three-way inconsistency analysis to determine anomalous countries with respect to three attributes: countries' COVID-19 cases, deaths and human development indices. We demonstrate the most anomalous countries across these three measures are Pakistan, the United States and the United Arab Emirates.

Neurotropism of SARS-CoV-2 and neurological diseases of the central nervous system in COVID-19 patients

The devastating COVID-19 pandemic is caused by the SARS-CoV-2 virus. It primarily affects the lung and induces acute respiratory distress leading to a decrease in oxygen supply to the cells. This lung insufficiency caused by SARS-CoV-2 virus contributes to hypoxia which can affect the brain and other organ systems. The heightened cytokine storm in COVID-19 patients leads to an immune reaction in the vascular endothelial cells that compromise the host defenses against the SARS-CoV-2 virus in various organs. The vascular endothelial cell membrane breach allows access for SARS-CoV-2 to infect multiple tissues and organs. The neurotropism of spike protein in SARS-CoV-2 rendered by furin site insertion may increase neuronal infections. These could result in encephalitis and encephalopathy. The COVID-19 patients suffered severe lung deficiency often showed effects in the brain and neural system. The early symptoms include headache, loss of smell, mental confusion, psychiatric disorders and strokes, and rarely encephalitis, which indicated the vulnerability of the nervous system to SARS-CoV-2. Infection of the brain and peripheral nervous system can lead to the dysfunction of other organs and result in multi-organ failure. This review focuses on discussing the vulnerability of the nervous system based on the pattern of expression of the receptors for the SARS-CoV-2 and the mechanisms of its cell invasion. The SARS-CoV-2 elicited immune response and host immune response evasion are further discussed. Then the effects on the nervous system and its consequences on neuro-sensory functions are discussed. Finally, the emerging information on the overall genetic susceptibility seen in COVID-19 patients and its implications for therapy outlook is discussed.