A systematic review and meta-analysis of published research data on COVID-19 infection fatality rates

Methylene blue for COVID-19 ARDS: insights from a randomized Clinical Trial

BACKGROUND

Around the world, the COVID-19 pandemic has presented many difficulties, and acute respiratory distress syndrome (ARDS) has become a major worry. The antiviral and anti-inflammatory characteristics of methylene blue (MB) have garnered interest for potential medicinal applications. The object of the current study is to assess the effect of orally administered MB on the treatment of ARDS associated with COVID-19.

METHOD

A randomized clinical study was carried out on 122 hospitalized patients who had ARDS related to COVID-19. Patients who met the eligibility requirements were randomized at random to either the control group (CG) (n = 60) or the intervention group (IG) (n = 62). Standard treatments were administered to both groups, with the addition of oral MB to the IG. Clinical outcomes, including SpO2 levels, CRP levels were assessed on the third and fifth days. Additionally, at the time of discharge, patients' assessments were made in terms of APACHE II scores, SOFA scores, LDH and CRP levels, SpO2, and respiratory rate in comparison to the day prior to the intervention. Patients were followed for mortality outcomes at one month and three months after the intervention.

RESULTS

Significant changes were observed in SpO2 levels over time (P < 0.001) and between groups (P = 0.022), with higher levels in the MB-treated group. The interaction between time and group (P = 0.019) indicated a stronger increase in SpO2 in the IG, with the IG's SpO2 level increasing by 6.42%. Furthermore, CRP levels showed significant changes over time (P < 0.001), but not between groups (P = 0.092). However, the interaction between group and CRP change over time (P = 0.019) suggested a distinct pattern of CRP decrease in the IG. Significant improvement in RR, SpO2, CRP, and APACHE II score were found according to discharge results. However, in terms of SpO2 and the APACHE II score, this improvement was noteworthy for IG. The length of hospitalization and mortality rates at one- and three-month follow-ups did not differ significantly.

CONCLUSION

Oral administration of MB demonstrated positive effects on improving SpO2 levels and reducing inflammatory markers in COVID-19-related ARDS patients. Despite no significant impact on survival rates or hospitalization length, the study supports the potential efficacy of MB as an alternative treatment for COVID-19 ARDS.

TRIAL REGISTRATION

This study was registered with the Iranian Registry of Clinical Trials ( http://www.irct.ir ) under the registration code IRCT20200409047007N2 on 11/29/2021.

A comparative analysis of COVID-19 seroprevalence rates, observed infection rates, and infection-related mortality

Objectives

The COVID-19 pandemic highlighted the need for data-driven decision making in managing public health crises. This study aims to extend previous research by incorporating infection-related mortality (IRM) to evaluate the discrepancies between seroprevalence data and infection rates reported to the Centers for Disease Control and Prevention (CDC), and to assess the implications for public health policy.

Study design

We conducted a comparative analysis of seroprevalence data collected as part of an NIH study and CDC-reported infection rates across ten U.S. regions, focusing on their correlation with IRM calculations.

Methods

The analysis includes a revision of prior estimates of IRM using updated seroprevalence rates. Correlations were calculated and their statistical relevance assessed.

Results

Findings indicate that COVID-19 is approximately 2.7 times more prevalent than what CDC infection data suggest. Utilizing the lower CDC-reported rates to calculate IRM leads to a significant overestimation by a factor of 2.7. When both seroprevalence and CDC infection data are combined, the overestimation of IRM increases to a factor of 3.79.

Conclusion

The study highlights the importance of integrating multiple data dimensions to accurately understand and manage public health emergencies. The results suggest that public health agencies should enhance their capacity for collecting and analyzing seroprevalence data regularly, given its stronger correlation with IRM than other estimates. This approach will better inform policy decisions and direct effective interventions.

The impact of COVID-19 public health and social measures on years of potential life lost

BACKGROUND

To determine the impacts of statewide coronavirus disease 2019 (COVID-19)-related public health and social measures (PHSMs) and attempted pandemic mitigation measures on years of potential life lost (YPLL).

METHODS

The "openness score" of each state during the COVID-19 pandemic was obtained using two open-source sites, the Multistate openness score and the Wallethub openness score. These scores combined various PHSMs, such as restrictions on gatherings and closing various types of businesses. Using data from the Centers for Disease Control and Prevention (CDC) Wonder database, the differences in prepandemic (2017-2019) and pandemic excess mortality were calculated in terms of YPLL and then compared to the openness scores using univariate regression modeling.

RESULTS

States that instituted more restrictive PHSMs as measured by openness scores failed to experience reductions in YPLL. On the contrary, there were trends toward statistical significance associating greater YPLL with the institution of more stringent PHSMs (p = 0.109 and p = 0.080 for Multistate and Wallethub, respectively).

DISCUSSION

This study suggests restrictive PHSMs were ineffective for improving mortality in this pandemic and trended toward increasing mortality in the younger population, presumably from other, non-COVID-19 causes.

Fatality Rates After Infection With the Omicron Variant (B.1.1.529): How Deadly has it been? A Systematic Review and Meta-Analysis

Background

Since late 2019, the global community has been gripped by the uncertainty surrounding the SARS-CoV-2 pandemic. In November 2021, the emergence of the Omicron variant in South Africa added a new dimension. This study aims to assess the disease's severity and determine the extent to which vaccinations contribute to reducing mortality rates.

Methods

A systematic review and meta-analysis of the epidemiological implications of the omicron variant of SARS-CoV-2 were performed, incorporating an analysis of articles from November 2021that address mortality rates.

Results

The analysis incorporated data from 3,214,869 patients infected with omicron, as presented in 270 articles. A total of 6,782 deaths from the virus were recorded (0.21%). In the analysed articles, the pooled mortality rate was 0.003 and the pooled in-house mortality rate was 0.036. Vaccination is an effective step in preventing death (odds ratio: 0.391, p < 0.01).

Conclusion

The mortality rates for the omicron variant are lower than for the preceding delta variant. mRNA vaccination affords secure and effective protection against severe disease and death from omicron.

Systematic review and meta-analysis of Tuberculosis and COVID-19 Co-infection: Prevalence, fatality, and treatment considerations

BACKGROUND

Tuberculosis (TB) and COVID-19 co-infection poses a significant global health challenge with increased fatality rates and adverse outcomes. However, the existing evidence on the epidemiology and treatment of TB-COVID co-infection remains limited.

METHODS

This updated systematic review aimed to investigate the prevalence, fatality rates, and treatment outcomes of TB-COVID co-infection. A comprehensive search across six electronic databases spanning November 1, 2019, to January 24, 2023, was conducted. The Joanna Briggs Institute Critical Appraisal Checklist assessed risk of bias of included studies, and meta-analysis estimated co-infection fatality rates and relative risk.

RESULTS

From 5,095 studies screened, 17 were included. TB-COVID co-infection prevalence was reported in 38 countries or regions, spanning both high and low TB prevalence areas. Prevalence estimates were approximately 0.06% in West Cape Province, South Africa, and 0.02% in California, USA. Treatment approaches for TB-COVID co-infection displayed minimal evolution since 2021. Converging findings from diverse studies underscored increased hospitalization risks, extended recovery periods, and accelerated mortality compared to single COVID-19 cases. The pooled fatality rate among co-infected patients was 7.1% (95%CI: 4.0% ~ 10.8%), slightly lower than previous estimates. In-hospital co-infected patients faced a mean fatality rate of 11.4% (95%CI: 5.6% ~ 18.8%). The pooled relative risk of in-hospital fatality was 0.8 (95% CI, 0.18-3.68) for TB-COVID patients versus single COVID patients.

CONCLUSION

TB-COVID co-infection is increasingly prevalent worldwide, with fatality rates gradually declining but remaining higher than COVID-19 alone. This underscores the urgency of continued research to understand and address the challenges posed by TB-COVID co-infection.

Workplace interventions to reduce the risk of SARS-CoV-2 infection outside of healthcare settings

BACKGROUND

Although many people infected with SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) experience no or mild symptoms, some individuals can develop severe illness and may die, particularly older people and those with underlying medical problems. Providing evidence-based interventions to prevent SARS-CoV-2 infection has become more urgent with the potential psychological toll imposed by the coronavirus disease 2019 (COVID-19) pandemic. Controlling exposures to occupational hazards is the fundamental method of protecting workers. When it comes to the transmission of viruses, workplaces should first consider control measures that can potentially have the most significant impact. According to the hierarchy of controls, one should first consider elimination (and substitution), then engineering controls, administrative controls, and lastly, personal protective equipment. This is the first update of a Cochrane review published 6 May 2022, with one new study added.

OBJECTIVES

To assess the benefits and harms of interventions in non-healthcare-related workplaces aimed at reducing the risk of SARS-CoV-2 infection compared to other interventions or no intervention.

SEARCH METHODS

We searched the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, Web of Science Core Collections, Cochrane COVID-19 Study Register, World Health Organization (WHO) COVID-19 Global literature on coronavirus disease, ClinicalTrials.gov, the WHO International Clinical Trials Registry Platform, and medRxiv to 13 April 2023.

SELECTION CRITERIA

We included randomised controlled trials (RCTs) and non-randomised studies of interventions. We included adult workers, both those who come into close contact with clients or customers (e.g. public-facing employees, such as cashiers or taxi drivers), and those who do not, but who could be infected by coworkers. We excluded studies involving healthcare workers. We included any intervention to prevent or reduce workers' exposure to SARS-CoV-2 in the workplace, defining categories of intervention according to the hierarchy of hazard controls (i.e. elimination; engineering controls; administrative controls; personal protective equipment).

DATA COLLECTION AND ANALYSIS

We used standard Cochrane methods. Our primary outcomes were incidence rate of SARS-CoV-2 infection (or other respiratory viruses), SARS-CoV-2-related mortality, adverse events, and absenteeism from work. Our secondary outcomes were all-cause mortality, quality of life, hospitalisation, and uptake, acceptability, or adherence to strategies. We used the Cochrane RoB 2 tool to assess risk of bias, and GRADE methods to evaluate the certainty of evidence for each outcome.

MAIN RESULTS

We identified 2 studies including a total of 16,014 participants. Elimination-of-exposure interventions We included one study examining an intervention that focused on elimination of hazards, which was an open-label, cluster-randomised, non-inferiority trial, conducted in England in 2021. The study compared standard 10-day self-isolation after contact with an infected person to a new strategy of daily rapid antigen testing and staying at work if the test is negative (test-based attendance). The trialists hypothesised that this would lead to a similar rate of infections, but lower COVID-related absence. Staff (N = 11,798) working at 76 schools were assigned to standard isolation, and staff (N = 12,229) working at 86 schools were assigned to the test-based attendance strategy. The results between test-based attendance and standard 10-day self-isolation were inconclusive for the rate of symptomatic polymerase chain reaction (PCR)-positive SARS-CoV-2 infection (rate ratio (RR) 1.28, 95% confidence interval (CI) 0.74 to 2.21; 1 study; very low-certainty evidence). The results between test-based attendance and standard 10-day self-isolation were inconclusive for the rate of any PCR-positive SARS-CoV-2 infection (RR 1.35, 95% CI 0.82 to 2.21; 1 study; very low-certainty evidence). COVID-related absenteeism rates were 3704 absence days in 566,502 days-at-risk (6.5 per 1000 working days) in the control group and 2932 per 539,805 days-at-risk (5.4 per 1000 working days) in the intervention group (RR 0.83, 95% CI 0.55 to 1.25). We downgraded the certainty of the evidence to low due to imprecision. Uptake of the intervention was 71% in the intervention group, but not reported for the control intervention. The trial did not measure our other outcomes of SARS-CoV-2-related mortality, adverse events, all-cause mortality, quality of life, or hospitalisation. We found seven ongoing studies using elimination-of-hazard strategies, six RCTs and one non-randomised trial. Administrative control interventions We found one ongoing RCT that aims to evaluate the efficacy of the Bacillus Calmette-Guérin (BCG) vaccine in preventing COVID-19 infection and reducing disease severity. Combinations of eligible interventions We included one non-randomised study examining a combination of elimination of hazards, administrative controls, and personal protective equipment. The study was conducted in two large retail companies in Italy in 2020. The study compared a safety operating protocol, measurement of body temperature and oxygen saturation upon entry, and a SARS-CoV-2 test strategy with a minimum activity protocol. Both groups received protective equipment. All employees working at the companies during the study period were included: 1987 in the intervention company and 1798 in the control company. The study did not report an outcome of interest for this systematic review. Other intervention categories We did not find any studies in this category.

AUTHORS' CONCLUSIONS

We are uncertain whether a test-based attendance policy affects rates of PCR-positive SARS-CoV-2 infection (any infection; symptomatic infection) compared to standard 10-day self-isolation amongst school and college staff. A test-based attendance policy may result in little to no difference in absenteeism rates compared to standard 10-day self-isolation. The non-randomised study included in our updated search did not report any outcome of interest for this Cochrane review. As a large part of the population is exposed in the case of a pandemic, an apparently small relative effect that would not be worthwhile from the individual perspective may still affect many people, and thus become an important absolute effect from the enterprise or societal perspective. The included RCT did not report on any of our other primary outcomes (i.e. SARS-CoV-2-related mortality and adverse events). We identified no completed studies on any other interventions specified in this review; however, eight eligible studies are ongoing. More controlled studies are needed on testing and isolation strategies, and working from home, as these have important implications for work organisations.

Flatten the curve. On a new covid-19 (hit) severity

BACKGROUND

During the health crisis of the COVID-19 pandemic, the adagium was to 'flatten the curve'. We investigate how well countries succeeded in this aim by constructing an appropriate severity measure. It is able to distinguish between countries that, e.g., experienced identical overall (excess) mortality rates or attained equal case load peaks over a certain period of time. Concretely, this implies that an identical total number of infections or deaths over a certain period is considered relatively worse if there is a higher and/or more peaks. More classical measures (like the total number or the maximum of cases/deaths) neglect this and are therefore inappropriate to assess the resilience of a health care system nor pandemic policy ex post performance.

METHODS & RESULTS

We applied our new (hit) severity to a set of 32 countries, and found that the flattening didn't go equally well. The difference in severity is large, with Norway being consistently the least severely hit by the pandemic (using deaths as indicator) during the whole observation period, while Hungary comes out as eventually being hit the hardest in our sample.

CONCLUSIONS

Having constructed a (hit) severity measure that enables to differentiate between countries' performances in a sound way, further research should now relate these observed differences to the pre-pandemic health care status and the sanitary measures or restrictions imposed during the pandemic; in order to reveal what measures help the most in what type of health care system and society.

Artificial Neural Network Prediction of COVID-19 Daily Infection Count

This study addresses COVID-19 testing as a nonlinear sampling problem, aiming to uncover the dependence of the true infection count in the population on COVID-19 testing metrics such as testing volume and positivity rates. Employing an artificial neural network, we explore the relationship among daily confirmed case counts, testing data, population statistics, and the actual daily case count. The trained artificial neural network undergoes testing in in-sample, out-of-sample, and several hypothetical scenarios. A substantial focus of this paper lies in the estimation of the daily true case count, which serves as the output set of our training process. To achieve this, we implement a regularized backcasting technique that utilize death counts and the infection fatality ratio (IFR), as the death statistics and serological surveys (providing the IFR) as more reliable COVID-19 data sources. Addressing the impact of factors such as age distribution, vaccination, and emerging variants on the IFR time series is a pivotal aspect of our analysis. We expect our study to enhance our understanding of the genuine implications of the COVID-19 pandemic, subsequently benefiting mitigation strategies.

The Influence of HLA Polymorphisms on the Severity of COVID-19 in the Romanian Population

In this study, we aimed to investigate whether specific HLA alleles found in patients from Romania and the Republic of Moldova were associated with the severity of COVID-19 infection and its associated mortality. We analyzed the HLA alleles at the -A, -B, -C, -DRB1, and -DQB1 loci in a cohort of 130 individuals with severe and extremely severe forms of COVID-19, including 44 individuals who died. We compared these findings to a control group consisting of individuals who had either not been diagnosed with COVID-19 or had experienced mild forms of the disease. Using multivariate logistic regression models, we discovered that the B*27 and B*50 alleles were associated with an increased susceptibility to developing a severe form of COVID-19. The A*33 and C*15 alleles showed potential for offering protection against the disease. Furthermore, we identified two protective alleles (A*03 and DQB1*02) against the development of extremely severe forms of COVID-19. By utilizing score statistics, we established a statistically significant association between haplotypes and disease severity (p = 0.021). In summary, this study provides evidence that HLA genotype plays a role in influencing the clinical outcome of COVID-19 infection.

Evaluation of Genetic or Cellular Impairments in Type I IFN Immunity in a Cohort of Young Adults with Critical COVID-19

Several genetic and immunological risk factors for severe COVID-19 have been identified, with monogenic conditions relating to 13 genes of type I interferon (IFN) immunity proposed to explain 4.8% of critical cases. However, previous cohorts have been clinically heterogeneous and were not subjected to thorough genetic and immunological analyses. We therefore aimed to systematically investigate the prevalence of rare genetic variants causing inborn errors of immunity (IEI) and functionally interrogate the type I IFN pathway in young adults that suffered from critical COVID-19 yet lacked comorbidities. We selected and clinically characterized a cohort of 38 previously healthy individuals under 50 years of age who were treated in intensive care units due to critical COVID-19. Blood samples were collected after convalescence. Two patients had IFN-α autoantibodies. Genome sequencing revealed very rare variants in the type I IFN pathway in 31.6% of the patients, which was similar to controls. Analyses of cryopreserved leukocytes did not indicate any defect in plasmacytoid dendritic cell sensing of TLR7 and TLR9 agonists in patients carrying variants in these pathways. However, lymphocyte STAT phosphorylation and protein upregulation upon IFN-α stimulation revealed three possible cases of impaired type I IFN signaling in carriers of rare variants. Together, our results suggest a strategy of functional screening followed by genome analyses and biochemical validation to uncover undiagnosed causes of critical COVID-19.

Two Difficult Pandemics: Tuberculosis and COVID-19

BACKGROUND

The coinfection of Mycobacterium tuberculosis and SARS-CoV-2 is called tuberculosis and COVID-19 coinfection (TB-COVID-19). We aimed to share the clinical, radiological, and laboratory findings and treatment processes of our patients with TB-COVID-19 coinfection in our tertiary reference hospital.

METHODS

Patients aged 18 years and over and hospitalized in the tuberculosis service between March 2020 and September 2022 were included. All coinfected patients whose COVID-19 polymerase chain reaction results were positive while receiving tuberculosis treatment or who were diagnosed with tuberculosis while receiving treatment for COVID-19 were included.

RESULTS

The number of patients was 39; 61.6% of males; the mean age was 52 ± 17.1 years; 20% were foreign nationals; 92.5% were Asian; 69.5% had a bacteriological diagnosis; 84.6% had pulmonary tuberculosis; 10% had received antituberculosis treatment before; and 87.5% were sensitive to the first-line antituberculosis drugs. The most common comorbidities were diabetes and hypertension. 87.5% of the patients were diagnosed with tuberculosis and were superinfected with COVID-19 while receiving tuberculosis treatment. 49.5% of patients had received at least one dose of COVID-19 vaccine. The most common presenting symptom was cough and sputum; the prominent laboratory parameter was C-reactive protein increase, and thorax computed tomography finding was consolidation, tree-in-bud, and cavitation. While 45.9% of the patients were still under treatment, 1 (2.5%) patient also resulted in mortality.

CONCLUSION

In this study, attention was drawn to two infectious diseases seen with respiratory tract symptoms. The mortality rate was found to be low. Neither disease was found to be a factor aggravating the course of each other.

Effects of a Cognitive Behavioral Digital Therapeutic on Anxiety and Depression Symptoms in Patients With Cancer: A Randomized Controlled Trial

PURPOSE

Patients with cancer often experience elevated levels of distress. This double-blind, randomized controlled trial compared the impact of an app-based version of cognitive behavioral stress management (CBSM) versus a health education sham app on anxiety and depression symptoms.

METHODS

Patients with nonmetastatic (stage I-III) cancer who were receiving or recently completed (≤6 months) systemic treatment were recruited nationwide. The primary outcome of change in anxiety symptoms (PROMIS-Anxiety) over 12 weeks and the top secondary outcome of change in depression symptoms (PROMIS-Depression) over 12 weeks were analyzed using mixed-effects modeling with repeated measures (weeks 0, 4, 8, 12). Patient global impressions of change in anxiety and depression were reported at weeks 4, 8, and 12. In addition, self-reported adverse events were collected throughout the study and adjudicated by the site principal investigator.

RESULTS

Four hundred forty-nine patients were enrolled in the trial (age M [standard deviation] = 52.44 [11.46]; 81% female; 76% White; 53% breast cancer). Patients randomly assigned to digitized CBSM showed significantly greater reductions in anxiety (B = -0.03; P = .019) and depression (B = -0.02; P = .042) symptoms over 12 weeks. Patients who received digitized CBSM were also significantly more likely to perceive much or very much improvement (v no/minimal change or much/very much worse) in their symptoms of anxiety (χ2 = 31.76; P < .001) and depression (χ2 = 19.70; P < .001) compared with the control.

CONCLUSION

The use of digitized CBSM led to significant improvements in anxiety and depression outcomes compared with the sham app.

Review of pediatric emergency care and the COVID-19 pandemic

The coronavirus disease 2019 (COVID-19) pandemic posed new challenges in health care delivery for patients of all ages. These included inadequate personal protective equipment, workforce shortages, and unknowns related to a novel virus. Children have been uniquely impacted by COVID-19, both from the system of care and socially. In the initial surges of COVID-19, a decrease in pediatric emergency department (ED) volume and a concomitant increase in critically ill adult patients resulted in re-deployment of pediatric workforce to care for adult patients. Later in the pandemic, a surge in the number of critically ill children was attributed to multisystem inflammatory syndrome in children. This was an unexpected complication of COVID-19 and further challenged the health care system. This article reviews the impact of COVID-19 on the entire pediatric emergency care continuum, factors affecting ED care of children with COVID-19 infection, including availability of vaccines and therapeutics approved for children, and pediatric emergency medicine workforce innovations and/or strategies. Furthermore, it provides guidance to emergency preparedness for optimal delivery of care in future health-related crises.

An early warning precision public health approach for assessing COVID-19 vulnerability in the UK: the Moore-Hill Vulnerability Index (MHVI)

BACKGROUND

Most COVID-19 vulnerability indices rely on measures that are biased by rates of exposure or are retrospective like mortality rates that offer little opportunity for intervention. The Moore-Hill Vulnerability Index (MHVI) is a precision public health early warning alternative to traditional infection fatality rates that presents avenues for mortality prevention.

METHODS

We produced an infection-severity vulnerability index by calculating the proportion of all recorded positive cases that were severe and attended by ambulances at small area scale for the East Midlands of the UK between May 2020 and April 2022. We produced maps identifying regions with high and low vulnerability, investigated the accuracy of the index over shorter and longer time periods, and explored the utility of the MHVI compared to other common proxy measures and indices. Analysis included exploring the correlation between our novel index and the Index of Multiple Deprivation (IMD).

RESULTS

The MHVI captures geospatial dynamics that single metrics alone often overlook, including the compound health challenges associated with disadvantaged and declining coastal towns inhabited by communities with post-industrial health legacies. A moderate negative correlation between MHVI and IMD reflects spatial analysis which suggests that high vulnerability occurs in affluent rural as well as deprived coastal and urban communities. Further, the MHVI estimates of severity rates are comparable to infection fatality rates for COVID-19.

CONCLUSIONS

The MHVI identifies regions with known high rates of poor health outcomes prior to the pandemic that case rates or mortality rates alone fail to identify. Pre-hospital early warning measures could be utilised to prevent mortality during a novel pandemic.

Prevalence of anti-SARS-CoV-2 antibodies in people attending the two main Goma markets in the eastern Democratic Republic of the Congo

The Democratic Republic of the Congo (DRC) officially reports low coronavirus disease 19 (COVID-19) prevalence. This cross-sectional study, conducted between September and November 2021, assessed the COVID-19 seroprevalence in people attending Goma's two largest markets, Kituku and Virunga. A similar study in a slum of Bukavu overlapped for 1 month using identical methods. COVID-19-unvaccinated participants (n = 796 including 454 vendors and 342 customers, 60% of whom were women) were surveyed. The median age of vendors and customers was 34.2 and 30.1 years, respectively. The crude and adjusted anti-SARS-CoV-2 antibody seroprevalence rates were 70.2% (95% CI 66.9-73.4%) and 98.8% (95% CI 94.1-100%), respectively, with no difference between vendors and customers. COVID-19 symptoms reported by survey participants in the previous 6 months were mild or absent in 58.9% and 41.1% of participants with anti-SARS-CoV-2 antibodies, respectively. No COVID-19-seropositive participants reported hospitalisation in the last 6 months. These findings are consistent with those reported in Bukavu. They confirm that SARS-CoV-2 spread without causing severe symptoms in densely populated settlements and markets and suggest that many COVID-19 cases went unreported. Based on these results, the relevance of an untargeted hypothetical vaccination programme in these communities should be questioned.

Rationale for combined therapies in severe-to-critical COVID-19 patients

An unprecedented global social and economic impact as well as a significant number of fatalities have been brought on by the coronavirus disease 2019 (COVID-19), produced by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Acute SARS-CoV-2 infection can, in certain situations, cause immunological abnormalities, leading to an anomalous innate and adaptive immune response. While most patients only experience mild symptoms and recover without the need for mechanical ventilation, a substantial percentage of those who are affected develop severe respiratory illness, which can be fatal. The absence of effective therapies when disease progresses to a very severe condition coupled with the incomplete understanding of COVID-19's pathogenesis triggers the need to develop innovative therapeutic approaches for patients at high risk of mortality. As a result, we investigate the potential contribution of promising combinatorial cell therapy to prevent death in critical patients.

[LDH-neutrophil-lymphocyte index as a predictor of 28-day mortality in patients with COVID-19]

Background

Coronavirus disease 2019 (COVID-19) represents the greatest health crisis of our times; it was declared by WHO a pandemic in March 2020. The risk of presenting a severe disease is inter-individual, since it varies according to age, comorbidities, and immunological status, in addition to the type of SARS-CoV-2 variant. The neutrophil/lymphocyte ratio (NLR) and lactic dehydrogenase (LDH) are widely used markers to assess the severity and predict the course of the disease in patients with COVID-19, with a direct relationship of higher value-worse prognosis.

Objective

To verify if the LDH-neutrophil-lymphocyte index calculated from laboratory tests taken within the first 24 hours of admission is useful as a predictor of 28-day mortality in adult patients diagnosed with COVID-19.

Material and methods

Retrospective and analytical cohort study. All consecutive patients over 16 years of any gender, admitted to a tertiary care center from March 2020 to March 2021, who had a diagnosis of COVID-19 with a positive PCR for SARS-CoV-2, were included.

Results

Higher levels of the LDHNL index were associated with higher mortality in patients hospitalized for COVID-19 (Q2 vs. Q1: RR 1.52 [1.24-1.87], p < 0.05; Q3 vs. Q1: RR 1.87 [1.55-2.25], p < 0.05; and Q4 vs. Q1: RR 2.74 [2.22-3-39], p < 0.05).

Conclusions

The serum LDHNL index taken in the first 24 hours of admission can help to predict early the risk of mortality in hospitalized patients with COVID-19.

Validating model output in the absence of ground truth data: A COVID-19 case study using the Simulator of Infectious Disease Dynamics in North Carolina (SIDD-NC) model

As the COVID-19 pandemic has progressed, various models have been developed to forecast changes in the outbreak and assess intervention strategies. In this study we validate the Simulator of Infectious Disease Dynamics in North Carolina (SIDD-NC) model against an ensemble of proxy-ground truth infections datasets. We assess the performance of SIDD-NC using Spearman Rank Correlation, RMSE, and percent RMSE at a state and county level. We conduct the analysis for the period of March 2020 through November 2020 as well as in shorter time increments to assess both the recreation of the pandemic curve as well as day-to-day transmission of SARS-CoV-2 within the population. We find that SIDD-NC performs well against the datasets in the ensemble, generating an estimate of infections that is robust both spatially and temporally.

A Comparative Study on Deep Learning Models for COVID-19 Forecast

The COVID-19 pandemic has led to a global health crisis with significant morbidity, mortality, and socioeconomic disruptions. Understanding and predicting the dynamics of COVID-19 are crucial for public health interventions, resource allocation, and policy decisions. By developing accurate models, informed public health strategies can be devised, resource allocation can be optimized, and virus transmission can be reduced. Various mathematical and computational models have been developed to estimate transmission dynamics and forecast the pandemic's trajectories. However, the evolving nature of COVID-19 demands innovative approaches to enhance prediction accuracy. The machine learning technique, particularly the deep neural networks (DNNs), offers promising solutions by leveraging diverse data sources to improve prevalence predictions. In this study, three typical DNNs, including the Long Short-Term Memory (LSTM) network, Physics-informed Neural Network (PINN), and Deep Operator Network (DeepONet), are employed to model and forecast COVID-19 spread. The training and testing data used in this work are the global COVID-19 cases in the year of 2021 from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. A seven-day moving average as well as the normalization techniques are employed to stabilize the training of deep learning models. We systematically investigate the effect of the number of training data on the predicted accuracy as well as the capability of long-term forecast in each model. Based on the relative L2 errors between the predictions from deep learning models and the reference solutions, the DeepONet, which is capable of learning hidden physics given the training data, outperforms the other two approaches in all test cases, making it a reliable tool for accurate forecasting the dynamics of COVID-19.

Understanding the host-pathogen evolutionary balance through Gaussian process modeling of SARS-CoV-2

We have developed a machine learning (ML) approach using Gaussian process (GP)-based spatial covariance (SCV) to track the impact of spatial-temporal mutational events driving host-pathogen balance in biology. We show how SCV can be applied to understanding the response of evolving covariant relationships linking the variant pattern of virus spread to pathology for the entire SARS-CoV-2 genome on a daily basis. We show that GP-based SCV relationships in conjunction with genome-wide co-occurrence analysis provides an early warning anomaly detection (EWAD) system for the emergence of variants of concern (VOCs). EWAD can anticipate changes in the pattern of performance of spread and pathology weeks in advance, identifying signatures destined to become VOCs. GP-based analyses of variation across entire viral genomes can be used to monitor micro and macro features responsible for host-pathogen balance. The versatility of GP-based SCV defines starting point for understanding nature's evolutionary path to complexity through natural selection.

Mass spectrometry-based phosphoproteomics in clinical applications

Protein phosphorylation is an essential post-translational modification that regulates many aspects of cellular physiology, and dysregulation of pivotal phosphorylation events is often responsible for disease onset and progression. Clinical analysis on disease-relevant phosphoproteins, while quite challenging, provides unique information for precision medicine and targeted therapy. Among various approaches, mass spectrometry (MS)-centered characterization features discovery-driven, high-throughput and in-depth identification of phosphorylation events. This review highlights advances in sample preparation and instrument in MS-based phosphoproteomics and recent clinical applications. We emphasize the preeminent data-independent acquisition method in MS as one of the most promising future directions and biofluid-derived extracellular vesicles as an intriguing source of the phosphoproteome for liquid biopsy.

Physical distancing versus testing with self-isolation for controlling an emerging epidemic

Two distinct strategies for controlling an emerging epidemic are physical distancing and regular testing with self-isolation. These strategies are especially important before effective vaccines or treatments become widely available. The testing strategy has been promoted frequently but used less often than physical distancing to mitigate COVID-19. We compared the performance of these strategies in an integrated epidemiological and economic model that includes a simple representation of transmission by "superspreading," wherein a relatively small fraction of infected individuals cause a large share of infections. We examined the economic benefits of distancing and testing over a wide range of conditions, including variations in the transmissibility and lethality of the disease meant to encompass the most prominent variants of COVID-19 encountered so far. In a head-to-head comparison using our primary parameter values, both with and without superspreading and a declining marginal value of mortality risk reductions, an optimized testing strategy outperformed an optimized distancing strategy. In a Monte Carlo uncertainty analysis, an optimized policy that combined the two strategies performed better than either one alone in more than 25% of random parameter draws. Insofar as diagnostic tests are sensitive to viral loads, and individuals with high viral loads are more likely to contribute to superspreading events, superspreading enhances the relative performance of testing over distancing in our model. Both strategies performed best at moderate levels of transmissibility, somewhat lower than the transmissibility of the ancestral strain of SARS-CoV-2.

CoVSense: Ultrasensitive Nucleocapsid Antigen Immunosensor for Rapid Clinical Detection of Wildtype and Variant SARS-CoV-2

The widespread accessibility of commercial/clinically-viable electrochemical diagnostic systems for rapid quantification of viral proteins demands translational/preclinical investigations. Here, Covid-Sense (CoVSense) antigen testing platform; an all-in-one electrochemical nano-immunosensor for sample-to-result, self-validated, and accurate quantification of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid (N)-proteins in clinical examinations is developed. The platform's sensing strips benefit from a highly-sensitive, nanostructured surface, created through the incorporation of carboxyl-functionalized graphene nanosheets, and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) conductive polymers, enhancing the overall conductivity of the system. The nanoengineered surface chemistry allows for compatible direct assembly of bioreceptor molecules. CoVSense offers an inexpensive (<$2 kit) and fast/digital response (<10 min), measured using a customized hand-held reader (<$25), enabling data-driven outbreak management. The sensor shows 95% clinical sensitivity and 100% specificity (Ct<25), and overall sensitivity of 91% for combined symptomatic/asymptomatic cohort with wildtype SARS-CoV-2 or B.1.1.7 variant (N = 105, nasal/throat samples). The sensor correlates the N-protein levels to viral load, detecting high Ct values of ≈35, with no sample preparation steps, while outperforming the commercial rapid antigen tests. The current translational technology fills the gap in the workflow of rapid, point-of-care, and accurate diagnosis of COVID-19.

Characteristics and outcomes of an international cohort of 600 000 hospitalized patients with COVID-19

Background

We describe demographic features, treatments and clinical outcomes in the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) COVID-19 cohort, one of the world's largest international, standardized data sets concerning hospitalized patients.

Methods

The data set analysed includes COVID-19 patients hospitalized between January 2020 and January 2022 in 52 countries. We investigated how symptoms on admission, co-morbidities, risk factors and treatments varied by age, sex and other characteristics. We used Cox regression models to investigate associations between demographics, symptoms, co-morbidities and other factors with risk of death, admission to an intensive care unit (ICU) and invasive mechanical ventilation (IMV).

Results

Data were available for 689 572 patients with laboratory-confirmed (91.1%) or clinically diagnosed (8.9%) SARS-CoV-2 infection from 52 countries. Age [adjusted hazard ratio per 10 years 1.49 (95% CI 1.48, 1.49)] and male sex [1.23 (1.21, 1.24)] were associated with a higher risk of death. Rates of admission to an ICU and use of IMV increased with age up to age 60 years then dropped. Symptoms, co-morbidities and treatments varied by age and had varied associations with clinical outcomes. The case-fatality ratio varied by country partly due to differences in the clinical characteristics of recruited patients and was on average 21.5%.

Conclusions

Age was the strongest determinant of risk of death, with a ∼30-fold difference between the oldest and youngest groups; each of the co-morbidities included was associated with up to an almost 2-fold increase in risk. Smoking and obesity were also associated with a higher risk of death. The size of our international database and the standardized data collection method make this study a comprehensive international description of COVID-19 clinical features. Our findings may inform strategies that involve prioritization of patients hospitalized with COVID-19 who have a higher risk of death.

Characterisation of infection-induced SARS-CoV-2 seroprevalence amongst children and adolescents in North Carolina

Few prospective studies have documented the seropositivity among those children infected with severe acute respiratory syndrome coronavirus 2. From 2 April 2021 to 24 June 2021, we prospectively enrolled children between the ages of 2 and 17 years at three North Carolina healthcare systems. Participants received at least four at-home serological tests detecting the presence of antibodies against, but not differentiating between, the nucleocapsid or spike antigen. A total of 1,058 participants were enrolled in the study, completing 2,709 tests between 1 May 2021 and 31 October 2021. Using multilevel regression with poststratification techniques and considering our assay sensitivity and sensitivity, we estimated that the seroprevalence of infection-induced antibodies among unvaccinated children and adolescents aged 2-17 years in North Carolina increased from 15.2% (95% credible interval, CrI 9.0-22.0) in May 2021 to 54.1% (95% CrI 46.7-61.1) by October 2021, indicating an average infection-to-reported-case ratio of 5. A rapid rise in seropositivity was most pronounced in those unvaccinated children aged 12-17 years, based on our estimates. This study underlines the utility of serial, serological testing to inform a broader understanding of the regional immune landscape and spread of infection.

The challenge of estimating the direct and indirect effects of COVID-19 interventions - Toward an integrated economic and epidemiological approach

Decisions on public health measures to contain a pandemic are often based on parameters such as expected disease burden and additional mortality due to the pandemic. Both pandemics and non-pharmaceutical interventions to fight pandemics, however, produce economic, social, and medical costs. The costs are, for example, caused by changes in access to healthcare, social distancing, and restrictions on economic activity. These factors indirectly influence health outcomes in the short- and long-term perspective. In a narrative review based on targeted literature searches, we develop a comprehensive perspective on the concepts available as well as the challenges of estimating the overall disease burden and the direct and indirect effects of COVID-19 interventions from both epidemiological and economic perspectives, particularly during the early part of a pandemic. We review the literature and discuss relevant components that need to be included when estimating the direct and indirect effects of the COVID-19 pandemic. The review presents data sources and different forms of death counts, and discusses empirical findings on direct and indirect effects of the pandemic and interventions on disease burden as well as the distribution of health risks.

Unintended Consequences of COVID-19 Non-Pharmaceutical Interventions (NPIs) for Population Health and Health Inequalities

Since the start of the COVID-19 pandemic in early 2020, governments around the world have adopted an array of measures intended to control the transmission of the SARS-CoV-2 virus, using both pharmaceutical and non-pharmaceutical interventions (NPIs). NPIs are public health interventions that do not rely on vaccines or medicines and include policies such as lockdowns, stay-at-home orders, school closures, and travel restrictions. Although the intention was to slow viral transmission, emerging research indicates that these NPIs have also had unintended consequences for other aspects of public health. Hence, we conducted a narrative review of studies investigating these unintended consequences of NPIs, with a particular emphasis on mental health and on lifestyle risk factors for non-communicable diseases (NCD): physical activity (PA), overweight and obesity, alcohol consumption, and tobacco smoking. We reviewed the scientific literature using combinations of search terms such as 'COVID-19', 'pandemic', 'lockdowns', 'mental health', 'physical activity', and 'obesity'. NPIs were found to have considerable adverse consequences for mental health, physical activity, and overweight and obesity. The impacts on alcohol and tobacco consumption varied greatly within and between studies. The variability in consequences for different groups implies increased health inequalities by age, sex/gender, socioeconomic status, pre-existing lifestyle, and place of residence. In conclusion, a proper assessment of the use of NPIs in attempts to control the spread of the pandemic should be weighed against the potential adverse impacts on other aspects of public health. Our findings should also be of relevance for future pandemic preparedness and pandemic response teams.

Relative role of border restrictions, case finding and contact tracing in controlling SARS-CoV-2 in the presence of undetected transmission: a mathematical modelling study

BACKGROUND

Understanding the overall effectiveness of non-pharmaceutical interventions to control the COVID-19 pandemic and reduce the burden of disease is crucial for future pandemic planning. However, quantifying the effectiveness of specific control measures and the extent of missed infections, in the absence of early large-scale serological surveys or random community testing, has remained challenging.

METHODS

Combining data on notified local COVID-19 cases with known and unknown sources of infections in Singapore with a branching process model, we reconstructed the incidence of missed infections during the early phase of the wild-type SARS-CoV-2 and Delta variant transmission. We then estimated the relative effectiveness of border control measures, case finding and contact tracing when there was no or low vaccine coverage in the population. We compared the risk of ICU admission and death between the wild-type SARS-CoV-2 and the Delta variant in notified cases and all infections.

RESULTS

We estimated strict border control measures were associated with 0.2 (95% credible intervals, CrI 0.04-0.8) missed imported infections per notified case between July and December 2020, a decline from around 1 missed imported infection per notified case in the early phases of the pandemic. Contact tracing was estimated to identify 78% (95% CrI 62-93%) of the secondary infections generated by notified cases before the partial lockdown in Apr 2020, but this declined to 63% (95% CrI 56-71%) during the lockdown and rebounded to 78% (95% CrI 58-94%) during reopening in Jul 2020. The contribution of contact tracing towards overall outbreak control also hinges on ability to find cases with unknown sources of infection: 42% (95% CrI 12-84%) of such cases were found prior to the lockdown; 10% (95% CrI 7-15%) during the lockdown; 47% (95% CrI 17-85%) during reopening, due to increased testing capacity and health-seeking behaviour. We estimated around 63% (95% CrI 49-78%) of the wild-type SARS-CoV-2 infections were undetected during 2020 and around 70% (95% CrI 49-91%) for the Delta variant in 2021.

CONCLUSIONS

Combining models with case linkage data enables evaluation of the effectiveness of different components of outbreak control measures, and provides more reliable situational awareness when some cases are missed. Using such approaches for early identification of the weakest link in containment efforts could help policy makers to better redirect limited resources to strengthen outbreak control.

Direct and indirect mortality impacts of the COVID-19 pandemic in the United States, March 1, 2020 to January 1, 2022

Excess mortality studies provide crucial information regarding the health burden of pandemics and other large-scale events. Here, we use time series approaches to separate the direct contribution of SARS-CoV-2 infection on mortality from the indirect consequences of the pandemic in the United States. We estimate excess deaths occurring above a seasonal baseline from March 1, 2020 to January 1, 2022, stratified by week, state, age, and underlying mortality condition (including COVID-19 and respiratory diseases; Alzheimer's disease; cancer; cerebrovascular diseases; diabetes; heart diseases; and external causes, which include suicides, opioid overdoses, and accidents). Over the study period, we estimate an excess of 1,065,200 (95% Confidence Interval (CI) 909,800-1,218,000) all-cause deaths, of which 80% are reflected in official COVID-19 statistics. State-specific excess death estimates are highly correlated with SARS-CoV-2 serology, lending support to our approach. Mortality from 7 of the 8 studied conditions rose during the pandemic, with the exception of cancer. To separate the direct mortality consequences of SARS-CoV-2 infection from the indirect effects of the pandemic, we fit generalized additive models (GAM) to age- state- and cause-specific weekly excess mortality, using covariates representing direct (COVID-19 intensity) and indirect pandemic effects (hospital intensive care unit (ICU) occupancy and measures of interventions stringency). We find that 84% (95% CI 65-94%) of all-cause excess mortality can be statistically attributed to the direct impact of SARS-CoV-2 infection. We also estimate a large direct contribution of SARS-CoV-2 infection (≥67%) on mortality from diabetes, Alzheimer's, heart diseases, and in all-cause mortality among individuals over 65 years. In contrast, indirect effects predominate in mortality from external causes and all-cause mortality among individuals under 44 years, with periods of stricter interventions associated with greater rises in mortality. Overall, on a national scale, the largest consequences of the COVID-19 pandemic are attributable to the direct impact of SARS-CoV-2 infections; yet, the secondary impacts dominate among younger age groups and in mortality from external causes. Further research on the drivers of indirect mortality is warranted as more detailed mortality data from this pandemic becomes available.

Ascertainment rate of SARS-CoV-2 infections from healthcare and community testing in the UK

The proportion of SARS-CoV-2 infections ascertained through healthcare and community testing is generally unknown and expected to vary depending on natural factors and changes in test-seeking behaviour. Here we use population surveillance data and reported daily case numbers in the United Kingdom to estimate the rate of case ascertainment. We mathematically describe the relationship between the ascertainment rate, the daily number of reported cases, population prevalence, and the sensitivity of PCR and Lateral Flow tests as a function time since exposure. Applying this model to the data, we estimate that 20%-40% of SARS-CoV-2 infections in the UK were ascertained with a positive test with results varying by time and region. Cases of the Alpha variant were ascertained at a higher rate than the wild type variants circulating in the early pandemic, and higher again for the Delta variant and Omicron BA.1 sub-lineage, but lower for the BA.2 sub-lineage. Case ascertainment was higher in adults than in children. We further estimate the daily number of infections and compare this to mortality data to estimate that the infection fatality rate increased by a factor of 3 during the period dominated by the Alpha variant, and declined in line with the distribution of vaccines. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

The Search of Association of HLA Class I and Class II Alleles with COVID-19 Mortality in the Russian Cohort

HLA genes play a pivotal role in the immune response via presenting the pathogen peptides on the cell surface in a host organism. Here, we studied the association of HLA allele variants of class I (loci A, B, C) and class II (loci DRB1, DQB1, DPB1) genes with the outcome of COVID-19 infection. We performed high-resolution sequencing of class HLA I and class II genes based on the sample population of 157 patients who died from COVID-19 and 76 patients who survived despite severe symptoms. The results were further compared with HLA genotype frequencies in the control population represented by 475 people from the Russian population. Although the obtained data revealed no significant differences between the samples at a locus level, they allowed one to uncover a set of notable alleles potentially contributing to the COVID-19 outcome. Our results did not only confirm the previously discovered fatal role of age or association of DRB1*01:01:01G and DRB1*01:02:01G alleles with severe symptoms and survival, but also allowed us to single out the DQB1*05:03:01G allele and B*14:02:01G~C*08:02:01G haplotype, which were associated with survival. Our findings showed that not only separate allele, but also their haplotype, could serve as potential markers of COVID-19 outcome and be used during triage for hospital admission.

A Prospective Study of Longitudinal Risks of Cognitive Deficit for People Undergoing Glioblastoma Surgery Using a Tablet Computer Cognition Testing Battery: Towards Personalized Understanding of Risks to Cognitive Function

Glioblastoma and the surgery to remove it pose high risks to the cognitive function of patients. Little reliable data exist about these risks, especially postoperatively before radiotherapy. We hypothesized that cognitive deficit risks detected before surgery will be exacerbated by surgery in patients with glioblastoma undergoing maximal treatment regimens. We used longitudinal electronic cognitive testing perioperatively to perform a prospective, longitudinal, observational study of 49 participants with glioblastoma undergoing surgery. Before surgery (A1), the participant risk of deficit in 5/6 cognitive domains was increased compared to normative data. Of these, the risks to Attention (OR = 31.19), Memory (OR = 97.38), and Perception (OR = 213.75) were markedly increased. These risks significantly increased in the early period after surgery (A2) when patients were discharged home or seen in the clinic to discuss histology results. For participants tested at 4-6 weeks after surgery (A3) before starting radiotherapy, there was evidence of risk reduction towards A1. The observed risks of cognitive deficit were independent of patient-specific, tumour-specific, and surgery-specific co-variates. These results reveal a timeframe of natural recovery in the first 4-6 weeks after surgery based on personalized deficit profiles for each participant. Future research in this period could investigate personalized rehabilitation tools to aid the recovery process found.

Supervised Machine Learning Models to Identify Early-Stage Symptoms of SARS-CoV-2

The coronavirus disease (COVID-19) pandemic was caused by the SARS-CoV-2 virus and began in December 2019. The virus was first reported in the Wuhan region of China. It is a new strain of coronavirus that until then had not been isolated in humans. In severe cases, pneumonia, acute respiratory distress syndrome, multiple organ failure or even death may occur. Now, the existence of vaccines, antiviral drugs and the appropriate treatment are allies in the confrontation of the disease. In the present research work, we utilized supervised Machine Learning (ML) models to determine early-stage symptoms of SARS-CoV-2 occurrence. For this purpose, we experimented with several ML models, and the results showed that the ensemble model, namely Stacking, outperformed the others, achieving an Accuracy, Precision, Recall and F-Measure equal to 90.9% and an Area Under Curve (AUC) of 96.4%.

A review of COVID vaccines: success against a moving target

BACKGROUND

Multiple vaccine platforms against COVID-19 have been developed and found safe and efficacious at a record speed. Although most are effective, they vary in their ease of production and distribution, their potential speed of modification against new variants, and their durability of protection and safety in certain target groups.

SOURCES OF DATA

Our discussion is based on published reports of clinical trials and analyses from national and global health agencies.

AREAS OF AGREEMENT

The production of neutralizing antibodies against the viral spike protein is protective, and all vaccines for which published data exist have been found to be effective against severe disease caused by the viral strain they target.

AREAS OF CONTROVERSY

The degree to which vaccines protect against emerging variants, moderate disease and asymptomatic infection remains somewhat unclear.

GROWING POINTS

Knowledge of the duration of protection and its decay is increasing, and discussions of booster frequency and target strains are ongoing.

AREAS TIMELY FOR DEVELOPING RESEARCH

The global effort to combat transmission and disease continues to rely upon intense epidemiological surveillance, whilst real-world data and clinical trials shape vaccination schedules and formulae.

Time-dependent risk of COVID-19 death with overwhelmed health-care capacity in Japan, 2020-2022

BACKGROUND

It has been descriptively argued that the case fatality risk (CFR) of coronavirus disease (COVID-19) is elevated when medical services are overwhelmed. The relationship between CFR and pressure on health-care services should thus be epidemiologically explored to account for potential epidemiological biases. The purpose of the present study was to estimate the age-dependent CFR in Tokyo and Osaka over time, investigating the impact of caseload demand on the risk of death.

METHODS

We estimated the time-dependent CFR, accounting for time delay from diagnosis to death. To this end, we first determined the time distribution from diagnosis to death, allowing variations in the delay over time. We then assessed the age-dependent CFR in Tokyo and Osaka. In Osaka, the risk of intensive care unit (ICU) admission was also estimated.

RESULTS

The CFR was highest among individuals aged 80 years and older and during the first epidemic wave from February to June 2020, estimated as 25.4% (95% confidence interval [CI] 21.1 to 29.6) and 27.9% (95% CI 20.6 to 36.1) in Tokyo and Osaka, respectively. During the fourth wave of infection (caused by the Alpha variant) in Osaka the CFR among the 70s and ≥ 80s age groups was, respectively, 2.3 and 1.5 times greater than in Tokyo. Conversely, despite the surge in hospitalizations, the risk of ICU admission among those aged 80 and older in Osaka decreased. Such time-dependent variation in the CFR was not seen among younger patients < 70 years old. With the Omicron variant, the CFR among the 80s and older in Tokyo and Osaka was 3.2% (95% CI 3.0 to 3.5) and 2.9% (95% CI 2.7 to 3.1), respectively.

CONCLUSION

We found that without substantial control, the CFR can increase when a surge in cases occurs with an identifiable elevation in risk-especially among older people. Because active treatment options including admission to ICU cannot be offered to the elderly with an overwhelmed medical service, the CFR value can potentially double compared with that in other areas of health care under less pressure.

Pulmonary Embolism Severity Index Predicts Adverse Events in Hospitalized COVID-19 Patients: A Retrospective Observational Study

OBJECTIVES

Pulmonary embolism is one of the leading causes of death in patients with COVID-19. Autopsy findings showed that the incidence of thromboembolic events was higher than clinically suspected. In this study, the authors investigated the relationship between pulmonary embolism severity index (PESI) and simplified PESI (sPESI) on admission to the hospital, as well as adverse events in hospitalized COVID-19 patients without clinically documented venous and/or pulmonary embolism. The adverse events investigated were the development of acute respiratory distress syndrome, the need for intensive care unit admission, invasive or noninvasive mechanical ventilation, and in-hospital mortality.

DESIGN

A retrospective and observational study.

SETTING

Two large-volume tertiary hospitals in the same city.

PARTICIPANTS

A total of 720 hospitalized COVID-19 patients with a positive polymerase chain reaction were evaluated.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Of the study population, 48.6% (350) were women, and the median age was 66 years (19-96). The overall in-hospital mortality rate was 20.5%. In the multivariate logistic regression analysis, a significant relationship was found between the whole adverse events considered and PESI, as well as sPESI (p < 0.001). According to the results, sPESI ≥2 predicts in-hospital mortality with a sensitivity of 61.4% and specificity of 83.3% (area under the curve = 0.817, 95% confidence interval 0.787-0.845, p < 0.001). Similarly, PESI classes IV and V also were found as independent risk factors for in-hospital mortality (for PESI class IV, odds ratio = 2.81, p < 0.017; for PESI class V, odds ratio = 3.94, p < 0.001).

CONCLUSIONS

PESI and sPESI scoring systems were both found to be associated with adverse events, and they can be used to predict in-hospital mortality in hospitalized COVID-19 patients without documented venous and/or pulmonary embolism.

Inhibition of coronavirus HCoV-OC43 by targeting the eIF4F complex

The translation initiation complex 4F (eIF4F) is a rate-limiting factor in protein synthesis. Alterations in eIF4F activity are linked to several diseases, including cancer and infectious diseases. To this end, coronaviruses require eIF4F complex activity to produce proteins essential for their life cycle. Efforts to target coronaviruses by abrogating translation have been largely limited to repurposing existing eIF4F complex inhibitors. Here, we report the results of a high throughput screen to identify small molecules that disrupt eIF4F complex formation and inhibit coronavirus RNA and protein levels. Of 338,000 small molecules screened for inhibition of the eIF4F-driven, CAP-dependent translation, we identified SBI-1232 and two structurally related analogs, SBI-5844 and SBI-0498, that inhibit human coronavirus OC43 (HCoV-OC43; OC43) with minimal cell toxicity. Notably, gene expression changes after OC43 infection of Vero E6 or A549 cells were effectively reverted upon treatment with SBI-5844 or SBI-0498. Moreover, SBI-5844 or SBI-0498 treatment effectively impeded the eIF4F complex assembly, with concomitant inhibition of newly synthesized OC43 nucleocapsid protein and OC43 RNA and protein levels. Overall, we identify SBI-5844 and SBI-0498 as small molecules targeting the eIF4F complex that may limit coronavirus transcripts and proteins, thereby representing a basis for developing novel therapeutic modalities against coronaviruses.

Circulating Dickkopf1 Parallels Metabolic Adaptations and Predicts Disease Trajectories in Patients With COVID-19

CONTEXT AND AIMS

Coronavirus disease 19 (COVID-19) trajectories show high interindividual variability, ranging from asymptomatic manifestations to fatal outcomes, the latter of which may be fueled by immunometabolic maladaptation of the host. Reliable identification of patients who are at risk of severe disease remains challenging. We hypothesized that serum concentrations of Dickkopf1 (DKK1) indicate disease outcomes in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected individuals.

METHODS

We recruited hospitalized patients with PCR-confirmed SARS-CoV-2 infection and included 80 individuals for whom blood samples from 2 independent time points were available. DKK1 serum concentrations were measured by ELISA in paired samples. Clinical data were extracted from patient charts and correlated with DKK1 levels. Publicly available datasets were screened for changes in cellular DKK1 expression on SARS-CoV-2 infection. Plasma metabolites were profiled by nuclear magnetic resonance spectroscopy in an unbiased fashion and correlated with DKK1 data. Kaplan-Meier and Cox regression analysis were used to investigate the prognostic value of DKK1 levels in the context of COVID-19.

RESULTS

We report that serum levels of DKK1 predict disease outcomes in patients with COVID-19. Circulating DKK1 concentrations are characterized by high interindividual variability and change as a function of time during SARS-CoV-2 infection, which is linked to platelet counts. We further find that the metabolic signature associated with SARS-CoV-2 infection resembles fasting metabolism and is mirrored by circulating DKK1 abundance. Patients with low DKK1 levels are twice as likely to die from COVID-19 than those with high levels, and DKK1 predicts mortality independent of markers of inflammation, renal function, and platelet numbers.

CONCLUSION

Our study suggests a potential clinical use of circulating DKK1 as a predictor of disease outcomes in patients with COVID-19. These results require validation in additional cohorts.

Prospective, randomised, parallel-group, open-label study to evaluate the effectiveness and safety of IMU-838, in combination with oseltamivir, in adults with COVID-19: the trial protocol

BACKGROUND

Globally, there is a scarcity of effective treatments for SARS-CoV-2 infections (causing COVID-19). Repurposing existing medications may offer the best hope for treating patients with COVID-19 to curb the pandemic. IMU-838 is a dihydroorotate dehydrogenase inhibitor, which is an effective mechanism for antiviral effects against respiratory viruses. When used synergistically with oseltamivir, therapeutic effects have been observed against influenza and SARS-CoV-2 in rodents. The IMU-838 and Oseltamivir in the Treatment of COVID-19 (IONIC) trial is a randomised controlled trial that will investigate whether time to clinical improvement in patients with COVID-19 is improved following a 14-day course of IMU-838+oseltamivir versus oseltamivir alone.

METHODS

IONIC trial is an open-label study in which participants will be randomised 1:1 in two parallel arms: the intervention arm (IMU-838+oseltamivir) and the control arm (oseltamivir only). The primary outcome is time to clinical improvement; defined as the time from randomisation to a two-point improvement on WHO ordinal scale; discharge from hospital, or death (whichever occurs first). The study is sponsored by the University Hospitals Coventry and Warwickshire NHS Trust and funded by LifeArc.

DISCUSSION

The IONIC protocol describes an overarching trial design to provide reliable evidence on the effectiveness of IMU-838 (vidofludimus calcium) when delivered in combination with an antiviral therapy (oseltamivir) (IONIC intervention) for confirmed or suspected COVID-19 infection in adult patients receiving usual standard of care.

ETHICS AND DISSEMINATION

This study has been independently reviewed and approved by Wales Research Ethics Committee. In addition, required regulatory approvals were received from Medicines and Healthcare products Regulatory Agency.

TRIAL REGISTRATION NUMBER

EudraCT 2020-001805-21, ISRCTN53038326, NCT04516915.

The Impact and Progression of the COVID-19 Pandemic in Bulgaria in Its First Two Years

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.

NC-COVID: A Time-Varying Compartmental Model for Estimating SARS-CoV-2 Infection Dynamics in North Carolina, US

Efforts to track and model SARS-CoV-2 infection dynamics in the population have been complicated by certain aspects of the transmission characteristics, which include a pre-symptomatic infectious phase as well as asymptomatic infectious individuals. Another problem is that many models focus on case count, as there has been (and is) limited data regarding infection status of members of the population, which is the most important aspect for constructing transmission models. This paper describes and explains the parameterization, calibration, and revision of the NC-COVID model, a compartmental model to estimate SARS-CoV-2 infection dynamics for the state of North Carolina, US. The model was developed early in the pandemic to provide rapid, up-to-date state-level estimates of the number of people who were currently infected, were immune from a prior infection, and remained susceptible to infection. As a post modeling exercise, we assessed the veracity of the model by comparing its output to SARS-CoV-2 viral particle concentrations detected in wastewater data and to estimates of people infected using COVID-19 deaths. The NC-COVID model was highly correlated with these independently derived estimates, suggesting that it produced accurate estimates of SARS-CoV-2 infection dynamics in North Carolina.

Investigation of turning points in the effectiveness of Covid-19 social distancing

Covid-19 is the first digitally documented pandemic in history, presenting a unique opportunity to learn how to best deal with similar crises in the future. In this study we have carried out a model-based evaluation of the effectiveness of social distancing, using Austria and Slovenia as examples. Whereas the majority of comparable studies have postulated a negative relationship between the stringency of social distancing (reduction in social contacts) and the scale of the epidemic, our model has suggested a varying relationship, with turning points at which the system changes its predominant regime from 'less social distancing-more cumulative deaths and infections' to 'less social distancing-fewer cumulative deaths and infections'. This relationship was found to persist in scenarios with distinct seasonal variation in transmission and limited national intensive care capabilities. In such situations, relaxing social distancing during low transmission seasons (spring and summer) was found to relieve pressure from high transmission seasons (fall and winter) thus reducing the total number of infections and fatalities. Strategies that take into account this relationship could be particularly beneficial in situations where long-term containment is not feasible.

Early insights of the COVID-19 pandemic in the Veterans' Affairs spinal cord injury and disorders population

STUDY DESIGN

Retrospective cohort.

OBJECTIVES

The primary outcome of the study was to identify patient characteristics associated with a positive COVID-19 test. The secondary outcome was to identify patient characteristics associated with mortality from COVID-19.

SETTING

Veterans Health Administration (VHA) National Spinal Cord Injury and Disorders (SCI) Registry, created by the National Spinal Cord Injury and Disorders SCI Program Office in March 2020.

METHODS

Data was analyzed in the form of descriptive statistics and then subsequent regression analysis was performed.

RESULTS

A total of 4,562 persons with SCI were tested for COVID-19 between March and July 2020, and 290 were positive. The study found that African Americans had increased odds of testing positive for COVID-19 (OR 1.53 (1.18-2.00), p &lt; 0.01). Increased age correlated with increased odds of mortality after testing positive for COVID-19 (1.046 (1.003-1.090)). Non-smokers had lower odds of mortality following positive COVID-19 test (0.15 (0.04-0.52)). No association was found between neurologic level of injury (NLI) and positive COVID-19 test or increased mortality. Increased Body Mass Index (BMI) did correlate with positive COVID-19 test but not increased mortality. The case fatality rate for persons with SCI and a positive test for COVID-19 was 12%.

CONCLUSIONS

It is important to define the risk factors for patients with SCI to elucidate and mitigate individual and population risks. These risk factors also can play a role in determining the allocation of critical healthcare resources.

Seroprevalence of SARS-CoV-2-specific anti-spike IgM, IgG, and anti-nucleocapsid IgG antibodies during the second wave of the pandemic: A population-based cross-sectional survey across Kashmir, India

Background

Within Kashmir, which is one of the topographically distinct areas in the Himalayan belt of India, a total of 2,236 cumulative deaths occurred by the end of the second wave. We aimed to conduct this population-based study in the age group of 7 years and above to estimate the seropositivity and its attributes in Kashmir valley.

Methods

We conducted a community-based household-level cross-sectional study, with a multistage, population-stratified, probability-proportionate-to-size, cluster sampling method to select 400 participants from each of the 10 districts of Kashmir. We also selected a quota of healthcare workers, police personnel, and antenatal women from each of the districts. Households were selected from each cluster and all family members with age 7 years or more were invited to participate. Information was collected through a standardized questionnaire and entered into Epicollect 5 software. Trained healthcare personnel were assigned for collecting venous blood samples from each of the participants which were transferred and processed for immunological testing. Testing was done for the presence of SARS-CoV-2-specific anti-spike IgM, IgG antibodies, and anti-nucleocapsid IgG antibodies. Weighted seropositivity was estimated along with the adjustment done for the sensitivity and specificity of the test used.

Findings

The data were collected from a total of 4,229 participants from the general population within the 10 districts of Kashmir. Our results showed that 84.84% (95% CI 84.51-85.18%) of the participants were seropositive in the weighted imputed data among the general population. In multiple logistic regression, the variables significantly affecting the seroprevalence were the age group 45-59 years (odds ratio of 0.73; 95% CI 0.67-0.78), self-reported history of comorbidity (odds ratio of 1.47; 95% CI 1.33-1.61), and positive vaccination history (odds ratio of 0.85; 95% CI 0.79-0.90) for anti-nucleocapsid IgG antibodies. The entire assessed variables showed a significant role during multiple logistic regression analysis for affecting IgM anti-spike antibodies with an odds ratio of 1.45 (95% CI 1.32-1.57) for age more than 60 years, 1.21 (95% CI 1.15-1.27) for the female gender, 0.87 (95% CI 0.82-0.92) for urban residents, 0.86 (95% CI 0.76-0.92) for self-reported comorbidity, and an odds ratio of 1.16 (95% CI 1.08-1.24) for a positive history of vaccination. The estimated infection fatality ratio was 0.033% (95% CI: 0.034-0.032%) between 22 May and 31 July 2021 against the seropositivity for IgM antibodies.

Interpretation

During the second wave of the SARS-CoV-2 pandemic, 84.84% (95% CI 84.51-85.18%) of participants from this population-based cross-sectional sample were seropositive against SARS-CoV-2. Despite a comparatively lower number of cases reported and lower vaccination coverage in the region, our study found such high seropositivity across all age groups, which indicates the higher number of subclinical and less severe unnoticed caseload in the community.

High-dimensional multinomial multiclass severity scoring of COVID-19 pneumonia using CT radiomics features and machine learning algorithms

We aimed to construct a prediction model based on computed tomography (CT) radiomics features to classify COVID-19 patients into severe-, moderate-, mild-, and non-pneumonic. A total of 1110 patients were studied from a publicly available dataset with 4-class severity scoring performed by a radiologist (based on CT images and clinical features). The entire lungs were segmented and followed by resizing, bin discretization and radiomic features extraction. We utilized two feature selection algorithms, namely bagging random forest (BRF) and multivariate adaptive regression splines (MARS), each coupled to a classifier, namely multinomial logistic regression (MLR), to construct multiclass classification models. The dataset was divided into 50% (555 samples), 20% (223 samples), and 30% (332 samples) for training, validation, and untouched test datasets, respectively. Subsequently, nested cross-validation was performed on train/validation to select the features and tune the models. All predictive power indices were reported based on the testing set. The performance of multi-class models was assessed using precision, recall, F1-score, and accuracy based on the 4 × 4 confusion matrices. In addition, the areas under the receiver operating characteristic curves (AUCs) for multi-class classifications were calculated and compared for both models. Using BRF, 23 radiomic features were selected, 11 from first-order, 9 from GLCM, 1 GLRLM, 1 from GLDM, and 1 from shape. Ten features were selected using the MARS algorithm, namely 3 from first-order, 1 from GLDM, 1 from GLRLM, 1 from GLSZM, 1 from shape, and 3 from GLCM features. The mean absolute deviation, skewness, and variance from first-order and flatness from shape, and cluster prominence from GLCM features and Gray Level Non Uniformity Normalize from GLRLM were selected by both BRF and MARS algorithms. All selected features by BRF or MARS were significantly associated with four-class outcomes as assessed within MLR (All p values < 0.05). BRF + MLR and MARS + MLR resulted in pseudo-R2 prediction performances of 0.305 and 0.253, respectively. Meanwhile, there was a significant difference between the feature selection models when using a likelihood ratio test (p value = 0.046). Based on confusion matrices for BRF + MLR and MARS + MLR algorithms, the precision was 0.856 and 0.728, the recall was 0.852 and 0.722, whereas the accuracy was 0.921 and 0.861, respectively. AUCs (95% CI) for multi-class classification were 0.846 (0.805-0.887) and 0.807 (0.752-0.861) for BRF + MLR and MARS + MLR algorithms, respectively. Our models based on the utilization of radiomic features, coupled with machine learning were able to accurately classify patients according to the severity of pneumonia, thus highlighting the potential of this emerging paradigm in the prognostication and management of COVID-19 patients.

Host genetic loci LZTFL1 and CCL2 associated with SARS-CoV-2 infection and severity of COVID-19

OBJECTIVES

Host genetic factors contribute to the variable severity of COVID-19. We examined genetic variants from genome-wide association studies and candidate gene association studies in a cohort of patients with COVID-19 and investigated the role of early SARS-CoV-2 strains in COVID-19 severity.

METHODS

This case-control study included 123 COVID-19 cases (hospitalized or ambulatory) and healthy controls from the state of Baden-Wuerttemberg, Germany. We genotyped 30 single nucleotide polymorphisms, using a custom-designed panel. Cases were also compared with the 1000 genomes project. Polygenic risk scores were constructed. SARS-CoV-2 genomes from 26 patients with COVID-19 were sequenced and compared between ambulatory and hospitalized cases, and phylogeny was reconstructed.

RESULTS

Eight variants reached nominal significance and two were significantly associated with at least one of the phenotypes "susceptibility to infection", "hospitalization", or "severity": rs73064425 in LZTFL1 (hospitalization and severity, P <0.001) and rs1024611 near CCL2 (susceptibility, including 1000 genomes project, P = 0.001). The polygenic risk score could predict hospitalization. Most (23/26, 89%) of the SARS-CoV-2 genomes were classified as B.1 lineage. No associations of SARS-CoV-2 mutations or lineages with severity were observed.

CONCLUSION

These host genetic markers provide insights into pathogenesis and enable risk classification. Variants which reached nominal significance should be included in larger studies.

Quantifying the information in noisy epidemic curves

Reliably estimating the dynamics of transmissible diseases from noisy surveillance data is an enduring problem in modern epidemiology. Key parameters are often inferred from incident time series, with the aim of informing policy-makers on the growth rate of outbreaks or testing hypotheses about the effectiveness of public health interventions. However, the reliability of these inferences depends critically on reporting errors and latencies innate to the time series. Here, we develop an analytical framework to quantify the uncertainty induced by under-reporting and delays in reporting infections, as well as a metric for ranking surveillance data informativeness. We apply this metric to two primary data sources for inferring the instantaneous reproduction number: epidemic case and death curves. We find that the assumption of death curves as more reliable, commonly made for acute infectious diseases such as COVID-19 and influenza, is not obvious and possibly untrue in many settings. Our framework clarifies and quantifies how actionable information about pathogen transmissibility is lost due to surveillance limitations.

Playing defense? Health care in the era of Covid

Health workers have to balance their own welfare vs. that of their patients particularly when patients have a readily transmissible disease. These risks become more consequential during an outbreak, and especially so when the chance of severe illness or mortality is non-negligible. One way to reduce risk is by reducing contact with patients. Such changes could be along the intensive or extensive margins. Using data on primary care outpatient encounters during the early months of the Covid-19 pandemic, I document important changes in the intensity of provider-patient interactions. Significantly, I find that adherence to clinical guidelines, the probability that routine procedures such as physical examinations were completed, and even the quality of information given by health providers, all declined sharply. I present evidence that these effects likely reflect risk mitigation behavior by health providers.