Disease burden and clinical severity of the first pandemic wave of COVID-19 in Wuhan, China

Exposure to Concurrent Heatwaves and Ozone Pollution and Associations with Mortality Risk: A Nationwide Study in China

BACKGROUND

Concurrent extreme events are projected to occur more frequently under a changing climate. Understanding the mortality risk and burden of the concurrent heatwaves and ozone (O 3 ) pollution may support the formulation of adaptation strategies and early warning systems for concurrent events in the context of climate change.

OBJECTIVES

We aimed to estimate the mortality risk and excess deaths of concurrent heatwaves and O 3 pollution across 250 counties in China.

METHODS

We collected daily mortality, meteorological, and air pollution data for the summer (1 June to 30 September) during 2013-2018. We defined heatwaves and high O 3 pollution days, then we divided the identified days into three categories: a) days with only heatwaves (heatwave-only event), b) days with only high O 3 pollution (high O 3 pollution-only event), and c) days with concurrent heatwaves and high O 3 pollution (concurrent event). A generalized linear model with a quasi-Poisson regression was used to estimate the risk of mortality associated with extreme events for each county. Then we conducted a random-effects meta-analysis to pool the county-specific estimates to derive the overall effect estimates. We used mixed-effects meta-regression to identify the drivers of the heterogeneity. Finally, we estimated the excess death attributable to extreme events (heatwave-only, high O 3 pollution-only, and concurrent events) from 2013 to 2020.

RESULTS

A higher all-cause mortality risk was associated with exposure to the concurrent heatwaves and high O 3 pollution than exposure to a heatwave-only or a high O 3 pollution-only event. The effects of a concurrent event on circulatory and respiratory mortality were higher than all-cause and nonaccidental mortality. Sex and age significantly impacted the association of concurrent events and heatwave-only events with all-cause mortality. We estimated that annual average excess deaths attributed to the concurrent events were 6,249 in China from 2017 to 2020, 5.7 times higher than the annual average excess deaths attributed to the concurrent events from 2013 to 2016. The annual average proportion of excess deaths attributed to the concurrent events in the total excess deaths caused by three types of events (heatwave-only events, high O 3 pollution-only events, and concurrent events) increased significantly in 2017-2020 (31.50%; 95% CI: 26.73%, 35.53%) compared with 2013-2016 (9.65%; 95% CI: 5.67%, 10.81%). Relative excess risk due to interaction revealed positive additive interaction considering the concurrent effect of heatwaves and high O 3 pollution.

DISCUSSION

Our findings may provide scientific basis for establishing a concurrent event early warning system to reduce the adverse health impact of the concurrent heatwaves and high O 3 pollution. https://doi.org/10.1289/EHP13790.

Risk factors for critical COVID-19 illness during Delta- and Omicron-predominant period in Korea; using K-COV-N cohort in the National health insurance service

BACKGROUND

This study evaluated the clinical characteristics of patients with COVID-19 in Korea, and examined the relationship between severe COVID-19 cases and underlying health conditions during the Delta (September 20, 2021 to December 4, 2021) and the Omicron (February 20, 2022 to March 31, 2022) predominant period.

METHODS

This study assessed the association between critical COVID-19 illness and various risk factors, including a variety of underlying health conditions, using multiple logistic regression models based on the K-COV-N cohort, a nationwide data of confirmed COVID-19 cases linked with COVID-19 vaccination status and the National Health Insurance claim information.

RESULTS

We analyzed 137,532 and 8,294,249 cases of COVID-19 infection during the Delta and the Omicron variant dominant periods, respectively. During the Delta as well as the Omicron period, old age (≥80 years) showed the largest effect size among risk factors for critical COVID-19 illness (aOR = 18.08; 95% confidence interval [CI] = 14.71-22.23 for the Delta; aOR = 24.07; 95% CI = 19.03-30.44 for the Omicron period). We found that patients with solid organ transplant (SOT) recipients, unvaccinated, and interstitial lung disease had more than a two-fold increased risk of critical COVID-19 outcomes between the Delta and Omicron periods. However, risk factors such as urban residence, underweight, and underlying medical conditions, including chronic cardiac diseases, immunodeficiency, and mental disorders, had different effects on the development of critical COVID-19 illness between the Delta and Omicron periods.

CONCLUSION

We found that the severity of COVID-19 infection was much higher for the Delta variant than for the Omicron. Although the Delta and the Omicron variant shared many risk factors for critical illness, several risk factors were found to have different effects on the development of critical COVID-19 illness between those two variants. Close monitoring of a wide range of risk factors for critical illness is warranted as new variants continue to emerge during the pandemic.

Reversible rearrangement of the cellular cytoskeleton: A key to the broad-spectrum antiviral activity of novel amphiphilic polymers

The battle against emerging viral infections has been uneven, as there is currently no broad-spectrum drug available to contain the spread of novel pathogens throughout the population. Consequently, the pandemic outbreak that occurred in early 2020 laid bare the almost empty state of the pandemic box. Therefore, the development of novel treatments with broad specificity has become a paramount concern in this post-pandemic era. Here, we propose copolymers of poly (sodium 2-(acrylamido)-2-methyl-1-propanesulfonate) (PAMPS) and poly (sodium 11-(acrylamido)undecanoate (AaU), both block (PAMPS75-b-PAaUn) and random (P(AMPSm-co-AaUn)) that show efficacy against a broad range of alpha and betacoronaviruses. Owing to their intricate architecture, these polymers exhibit a highly distinctive mode of action, modulating nano-mechanical properties of cells and thereby influencing viral replication. Through the employment of confocal and atomic force microscopy techniques, we discerned perturbations in actin and vimentin filaments, which correlated with modification of cellular elasticity and reduction of glycocalyx layer. Intriguingly, this process was reversible upon polymer removal from the cells. To ascertain the applicability of our findings, we assessed the efficacy and underlying mechanism of the inhibitors using fully differentiated human airway epithelial cultures, wherein near-complete abrogation of viral replication was documented. Given their mode of action, these polymers can be classified as biologically active nanomaterials that exploit a highly conserved molecular target-cellular plasticity-proffering the potential for truly broad-spectrum activity while concurrently for drug resistance development is minimal.

Bayesian sequential approach to monitor COVID-19 variants through test positivity rate from wastewater

Deployment of clinical testing on a massive scale was an essential control measure for curtailing the burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and the magnitude of the COVID-19 (coronavirus disease 2019) pandemic during its waves. As the pandemic progressed, new preventive and surveillance mechanisms emerged. Implementation of vaccine programs, wastewater (WW) surveillance, and at-home COVID-19 antigen tests reduced the demand for mass SARS-CoV-2 testing. Unfortunately, reductions in testing and test reporting rates also reduced the availability of public health data to support decision-making. This paper proposes a sequential Bayesian approach to estimate the COVID-19 test positivity rate (TPR) using SARS-CoV-2 RNA concentrations measured in WW through an adaptive scheme incorporating changes in virus dynamics. The proposed modeling framework was applied to WW surveillance data from two WW treatment plants in California; the City of Davis and the University of California, Davis campus. TPR estimates are used to compute thresholds for WW data using the Centers for Disease Control and Prevention thresholds for low (<5% TPR), moderate (5%-8% TPR), substantial (8%-10% TPR), and high (>10% TPR) transmission. The effective reproductive number estimates are calculated using TPR estimates from the WW data. This approach provides insights into the dynamics of the virus evolution and an analytical framework that combines different data sources to continue monitoring COVID-19 trends. These results can provide public health guidance to reduce the burden of future outbreaks as new variants continue to emerge. IMPORTANCE We propose a statistical model to correlate WW with TPR to monitor COVID-19 trends and to help overcome the limitations of relying only on clinical case detection. We pose an adaptive scheme to model the nonautonomous nature of the prolonged COVID-19 pandemic. The TPR is modeled through a Bayesian sequential approach with a beta regression model using SARS-CoV-2 RNA concentrations measured in WW as a covariable. The resulting model allows us to compute TPR based on WW measurements and incorporates changes in viral transmission dynamics through an adaptive scheme.

Development and Validation of a Prediction Model of the Risk of Pneumonia in Patients with SARS-CoV-2 Infection

Objective

To establish a prediction model of pneumonia risk in SARS-CoV-2-infected patients to reduce unnecessary chest CT scans.

Materials and Methods

The model was constructed based on a retrospective cohort study. We selected SARS-CoV-2 test-positive patients and collected their clinical data and chest CT images from the outpatient and emergency departments of Hunan Provincial People's Hospital, China. Univariate and multivariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression were utilized to identify predictors of pneumonia risk for patients infected with SARS-CoV-2. These predictors were then incorporated into a nomogram to establish the model. To ensure its performance, the model was evaluated from the aspects of discrimination, calibration, and clinical validity. In addition, a smoothed curve was fitted using a generalized additive model (GAM) to explore the association between the pneumonia grade and the model's predicted probability of pneumonia.

Results

We selected 299 SARS-CoV-2 test-positive patients, of whom 205 cases were in the training cohort and 94 cases were in the validation cohort. Age, CRP natural log-transformed value (InCRP), and monocyte percentage (%Mon) were found to be valid predictors of pneumonia risk. This predictive model achieved good discrimination of AUC in the training and validation cohorts which was 0.7820 (95% CI: 0.7254-0.8439) and 0.8432 (95% CI: 0.7588-0.9151), respectively. At the cut-off value of 0.5, it had a sensitivity and specificity of 70.75% and 66.33% in the training cohort and 76.09% and 73.91% in the validation cohort, respectively. With suitable calibration accuracy shown in calibration curves, decision curve analysis indicated high clinical value in predicting pneumonia probability in SARS-CoV-2-infected patients. The probability of pneumonia predicted by the model was positively correlated with the actual pneumonia classification.

Conclusion

This study has developed a pneumonia risk prediction model that can be utilized for diagnostic purposes in predicting the probability of pneumonia in patients infected with SARS-CoV-2.

Effectiveness of Chinese medicine formula Huashibaidu granule on mild COVID-19 patients: A prospective, non-randomized, controlled trial

Background

The effectiveness and safety of Huashibaidu granule (HSBD) in treating mild Corona Virus Disease 2019 (COVID-19) patients infected with SARS-CoV-2 remain to be identified. We aimed to evaluate the effectiveness of HSBD in mild COVID-19 patients.

Methods

A prospective, non-randomized, controlled study in mild COVID-19 patients was conducted in Shanghai, from April 8 to May 6, 2022. The enrolled patients were diagnosed as mild COVID-19. Finally, 360 patients received HSBD, and 368 patients received TCM placebo (administered orally 20 g twice daily for 7 days). The primary endpoints were the negative conversion rate of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and the negative conversion time. Secondary endpoints included the hospitalized days and the improvement in the clinical condition.

Results

The negative conversion rate of SARS-CoV-2 at 7 days posttreatment in the HSBD group was higher than that in the control group (95.28% vs. 82.61%, P < 0.001). The median negative conversion time in the HSBD group was markedly decreased by 2 days compared with the control group (3 [3-6] vs. 5 [4-7], P < 0.001). In addition, the median hospitalized day was shortened in the HSBD group by 1 day compared with the control group (6 [4-7] vs. 7 [5-9], P < 0.001). The clinical improvement rate (275/360 [76.39%]) in the HSBD group within 7 days was significantly higher than that (203/368 [55.16%]) in the control group (P < 0.001). The improvement of symptom scores in the HSBD group was higher than that in the control group (2 [1-4] vs. 1 [1-2], P < 0.001). No severe adverse events occurred.

Conclusions

Our study suggested that HSBD effectively increased the negative conversion rate of SARS-CoV-2 and shortened the negative conversion time and hospitalized days in mild COVID-19 patients.

Clinical trial registration

Chinese Clinical Trial Registry, ChiCTR2200058668.

Immunological response after SARS-CoV-2 infection and mRNA vaccines in patients with myasthenia gravis treated with Rituximab

In this study we employed a comprehensive immune profiling approach to determine innate and adaptive immune response to SARS-CoV-2 infection and mRNA vaccines in patients with myasthenia gravis receiving rituximab. By multicolour cytometry, dendritic and natural killer cells, B- and T-cell subsets, including T-cells producing IFN-γ stimulated with SARS-CoV-2 peptides, were analysed after infection and mRNA vaccination. In the same conditions, anti-spike antibodies and cytokines' levels were measured in sera. Despite the impaired B cell and humoral response, rituximab patients showed an intact innate, CD8 T-cell and IFN-γ specific CD4+ and CD8+ T-cell response after infection and vaccination, comparable to controls. No signs of cytokine mediated inflammatory cascade was observed. Our study provides evidence of protective immune response after SARS-CoV-2 infection and mRNA vaccines in patients with myasthenia gravis on B cell depleting therapy and highlights the need for prospective studies with larger cohorts to clarify the role of B cells in SARS-CoV-2 immune response.

Disease burden due to COVID-19 in Taiwan: disability-adjusted life years (DALYs) with implication of Monte Carlo simulations

BACKGROUND

The novel coronavirus disease 2019 (COVID-19) has affected a large number of countries. Informing the public and decision makers of the COVID-19's economic burdens is essential for understanding the real pandemic impact.

METHODS

COVID-19 premature mortality and disability impact in Taiwan was analyzed using the Taiwan National Infectious Disease Statistics System (TNIDSS) by estimating the sex/age-specific years of life lost through death (YLLs), the number of years lived with disability (YLDs), and the disability-adjusted life years (DALYs) from January 2020 to November 2021.

RESULTS

Taiwan recorded 1004.13 DALYs (95% CI: 1002.75-1005.61) per 100,000 population for COVID-19, with YLLs accounting for 99.5% (95% CI: 99.3%99.6%) of all DALYs, with males suffering more from the disease than females. For population aged ≥ 70 years, the disease burdens of YLDs and YLLs were 0.1% and 99.9%, respectively. Furthermore, we found that duration of disease in critical state contributed 63.9% of the variance in DALY estimations.

CONCLUSIONS

The nationwide estimation of DALYs in Taiwan provides insights into the demographic distributions and key epidemiological parameter for DALYs. The essentiality of enforcing protective precautions when needed is also implicated. The higher YLLs percentage in DALYs also revealed the fact of high confirmed death rates in Taiwan. To reduce infection risks and disease, it is crucial to maintain moderate social distancing, border control, hygiene measures, and increase vaccine coverage levels.

Clinical features and viral etiology of acute respiratory infection in an outpatient fever clinic during COVID-19 pandemic in a tertiary hospital in Nanjing, China

BACKGROUND

Clinical feature and viral etiology for acute respiratory infection (ARI) in the community was unknown during coronavirus disease 2019 (COVID-19) pandemic.

OBJECTIVE

In a retrospective study, we aimed to characterize the clinical feature and etiology for the ARI patients admitted to the outpatient fever clinic in Nanjing Drum Tower Hospital between November 2020 and March 2021.

METHODS

Fifteen common respiratory pathogens were tested using pharyngeal swabs by multiplex reverse transcriptase-polymerase chain reaction assays.

RESULTS

Of the 242 patients, 56 (23%) were tested positive for at least one viral agent. The predominant viruses included human rhinovirus (HRV) (5.4%), parainfluenza virus type III (PIV-III) (5.0%), and human coronavirus-NL63 (HCoV-NL63) (3.7%). Cough, sputum, nasal obstruction, and rhinorrhea were the most prevalent symptoms in patients with viral infection. Elderly and the patients with underlying diseases were susceptible to pneumonia accompanied with sputum and chest oppression. Three (5.4%) patients in virus infection group, whereas 31 (16.7%) in non-viral infection group (p = 0.033), were empirically prescribed with antiviral agents. Among 149 patients who received antibiotic therapy, 30 (20.1%) patients were later identified with viral infection.

CONCLUSION

Our study indicated the importance of accurate diagnosis of ARI, especially during the COVID-19 pandemic, which might facilitate appropriate clinical treatment.

Assessing the transition of COVID-19 burden towards the young population while vaccines are rolled out in China

SARS-CoV-2 infection causes most cases of severe illness and fatality in older age groups. Over 92% of the Chinese population aged ≥12 years has been fully vaccinated against COVID-19 (albeit with vaccines developed against historical lineages). At the end of October 2021, the vaccination programme has been extended to children aged 3–11 years. Here, we aim to assess whether, in this vaccination landscape, the importation of Delta variant infections could shift COVID-19 burden from adults to children. We developed an age-structured susceptible-infectious-removed model of SARS-CoV-2 transmission to simulate epidemics triggered by the importation of Delta variant infections and project the age-specific incidence of SARS-CoV-2 infections, cases, hospitalizations, intensive care unit admissions, and deaths. In the context of the vaccination programme targeting individuals aged ≥12 years, and in the absence of non-pharmaceutical interventions, the importation of Delta variant infections could have led to widespread transmission and substantial disease burden in mainland China, even with vaccination coverage as high as 89% across the eligible age groups. Extending the vaccination roll-out to include children aged 3–11 years (as it was the case since the end of October 2021) is estimated to dramatically decrease the burden of symptomatic infections and hospitalizations within this age group (39% and 68%, respectively, when considering a vaccination coverage of 87%), but would have a low impact on protecting infants. Our findings highlight the importance of including children among the target population and the need to strengthen vaccination efforts by increasing vaccine effectiveness.

Innate and adaptive immune response in SARS-CoV-2 infection-Current perspectives

Coronavirus disease 2019 (COVID-19) has been a global pandemic, caused by a novel coronavirus strain with strong infectivity, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). With the in-depth research, the close relationship between COVID-19 and immune system has been dug out. During the infection, macrophages, dendritic cells, natural killer cells, CD8⁺ T cells, Th1, Th17, Tfh cells and effector B cells are all involved in the anti-SARS-CoV-2 responses, however, the dysfunctional immune responses will ultimately lead to the excessive inflammation, acute lung injury, even other organ failure. Thus, a detailed understanding of pertinent immune response during COVID-19 will provide insights in predicting disease outcomes and developing appropriate therapeutic approaches. In this review, we mainly clarify the role of immune cells in COVID-19 and the target-vaccine development and treatment.

Confronting SARS-CoV-2 Infection: Patients' Experience in the First Pandemic Wave-Cross-Sectional Study

The aim of the study was to investigate the real-life experience of persons infected with SARS-CoV-2 in Slovenia in the first pandemic wave and how the buffering effect of social and informational support affected negative feelings. We used a self-administrated questionnaire. There were 1182 eligible notified cases with the response rate 64.9%. At least 62% of responders were able to follow the isolation rules, while 21.1% did not or could not organize their living separately from other household members. The main providers during the isolation period were close family members. The most prevalent emotion in our study was worry (70.3%) and fear (37.6%). Worry and fear during the illness were less probable for men than women, but more probable for older patients. Participants with strong emotional support had lower odds of being sad. Those who were exposed to a larger number of sources of information had higher odds of being worried. Those patients who used a higher number of more credible sources of information had higher odds of being afraid during illness. Pets did not play a special role in psychological well-being. The role of the media and public health communications should be explored further to achieve an improved response.

The molecular epidemiology of multiple zoonotic origins of SARS-CoV-2

Understanding the circumstances that lead to pandemics is important for their prevention. We analyzed the genomic diversity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) early in the coronavirus disease 2019 (COVID-19) pandemic. We show that SARS-CoV-2 genomic diversity before February 2020 likely comprised only two distinct viral lineages, denoted "A" and "B." Phylodynamic rooting methods, coupled with epidemic simulations, reveal that these lineages were the result of at least two separate cross-species transmission events into humans. The first zoonotic transmission likely involved lineage B viruses around 18 November 2019 (23 October to 8 December), and the separate introduction of lineage A likely occurred within weeks of this event. These findings indicate that it is unlikely that SARS-CoV-2 circulated widely in humans before November 2019 and define the narrow window between when SARS-CoV-2 first jumped into humans and when the first cases of COVID-19 were reported. As with other coronaviruses, SARS-CoV-2 emergence likely resulted from multiple zoonotic events.

Fast and noninvasive electronic nose for sniffing out COVID-19 based on exhaled breath-print recognition

The reverse transcription-quantitative polymerase chain reaction (RT-qPCR) approach has been widely used to detect the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, instead of using it alone, clinicians often prefer to diagnose the coronavirus disease 2019 (COVID-19) by utilizing a combination of clinical signs and symptoms, laboratory test, imaging measurement (e.g., chest computed tomography scan), and multivariable clinical prediction models, including the electronic nose. Here, we report on the development and use of a low cost, noninvasive method to rapidly sniff out COVID-19 based on a portable electronic nose (GeNose C19) integrating an array of metal oxide semiconductor gas sensors, optimized feature extraction, and machine learning models. This approach was evaluated in profiling tests involving a total of 615 breath samples composed of 333 positive and 282 negative samples. The samples were obtained from 43 positive and 40 negative COVID-19 patients, respectively, and confirmed with RT-qPCR at two hospitals located in the Special Region of Yogyakarta, Indonesia. Four different machine learning algorithms (i.e., linear discriminant analysis, support vector machine, stacked multilayer perceptron, and deep neural network) were utilized to identify the top-performing pattern recognition methods and to obtain a high system detection accuracy (88–95%), sensitivity (86–94%), and specificity (88–95%) levels from the testing datasets. Our results suggest that GeNose C19 can be considered a highly potential breathalyzer for fast COVID-19 screening.

Evaluation of portable near-infrared spectroscopy for authentication of mRNA based COVID-19 vaccines

Since its identification in 2019, Covid-19 has spread to become a global pandemic. Until now, vaccination in its different forms proves to be the most effective measure to control the outbreak and lower the burden of the disease on healthcare systems. This arena has become a prime target to criminal networks that spread counterfeit Covid-19 vaccines across the supply chain mainly for profit. Counterfeit vaccines provide false sense of security to individuals, heightens the risk of exposure and outbreak of the virus, and increase the risk of harm linked to Covid-19 infection. Moreover, the increase in counterfeit vaccines feeds hesitancy towards vaccination and erodes the trust in mass immunisation programmes. It is therefore of paramount importance to work on rapid and reliable methods for vaccine authentication. Subsequently this work utilised a portable and non-destructive near infrared (NIR) spectroscopic method for authentication of Covid-19 vaccines. A total of 405 Covid-19 vaccines samples, alongside their main constituents, were measured as received through glass vials. Spectral quality and bands were inspected by considering the raw spectra of the vaccines. Authentication was explored by applying principal component analysis (PCA) to the multiplicative scatter correction-first derivative spectra. The results showed that NIR spectra of the vaccine featured mainly bands corresponding to the mRNA active ingredient. Fewer bands corresponded to the excipients and protein spectra. The vaccines NIR spectra were strongly absorbing with maximum absorbances up to 2.7 absorbance units and that differentiated them from samples containing normal saline only (constituent reported for counterfeit Covid-19 vaccines). Clustering based on PCA offered optimal authentication of Covid-19 vaccines when applied over the range of 9000–4000 cm^-1These findings shed light on the potential of using NIR for analysing Covid-19 vaccines and presents a rapid and effective initial technique for Covid-19 vaccine authentication.

Development and Validation of a Predictive Nomogram with Age and Laboratory Findings for Severe COVID-19 in Hunan Province, China

Purpose

To identify more objectively predictive factors of severe outcome among patients hospitalized for coronavirus disease 2019 (COVID-19).

Patients and Methods

A retrospective cohort of 479 hospitalized patients diagnosed with COVID-19 in Hunan Province was selected. The prognostic effects of factors such as age and laboratory indicators were analyzed using the Kaplan–Meier method and Cox proportional hazards model. A prognostic nomogram model was established to predict the progression of patients with COVID-19.

Results

A total of 524 patients in Hunan province with COVID-19 from December 2019 to October 2020 were retrospectively recruited. Among them, 479 eligible patients were randomly assigned into the training cohort (n = 383) and validation cohort (n = 96), at a ratio of 8:2. Sixty-eight (17.8%) and 15 (15.6%) patients developed severe COVID-19 after admission in the training cohort and validation cohort, respectively. The differences in baseline characteristics were not statistically significant between the two cohorts with regard to age, sex, and comorbidities (P > 0.05). Multivariable analyses included age, C-reactive protein, fibrinogen, lactic dehydrogenase, neutrophil-to-lymphocyte ratio, urea, albumin-to-globulin ratio, and eosinophil count as predictive factors for patients with progression to severe COVID-19. A nomogram was constructed with sufficient discriminatory power (C index = 0.81), and proper consistency between the prediction and observation, with an area under the ROC curve of 0.81 and 0.86 in the training and validation cohort, respectively.

Conclusion

We proposed a simple nomogram for early detection of patients with non-severe COVID-19 but at high risk of progression to severe COVID-19, which could help optimize clinical care and personalized decision-making therapies.

Linear and non-linear dynamics of the epidemics: System identification based parametric prediction models for the pandemic outbreaks

Coronavirus disease 2019 (COVID-19) has endured constituting formidable economic, social, educational, and phycological challenges for the societies. Moreover, during pandemic outbreaks, the hospitals are overwhelmed with patients requiring more intensive care units and intubation equipment. Therein, to cope with these urgent healthcare demands, the state authorities seek ways to develop policies based on the estimated future casualties. These policies are mainly non-pharmacological policies including the restrictions, curfews, closures, and lockdowns. In this paper, we construct three model structures of the S^pIⁿI^tI^bD-N (suspicious S^p, infected Iⁿ, intensive care I^t, intubated I^b, and dead D together with the non-pharmacological policies N) holding two key targets. The first one is to predict the future COVID-19 casualties including the intensive care and intubated ones, which directly determine the need for urgent healthcare facilities, and the second one is to analyse the linear and non-linear dynamics of the COVID-19 pandemic under the non-pharmacological policies. In this respect, we have modified the non-pharmacological policies and incorporated them within the models whose parameters are learned from the available data. The trained models with the data released by the Turkish Health Ministry confirmed that the linear S^pIⁿI^tI^bD-N model yields more accurate results under the imposed non-pharmacological policies. It is important to note that the non-pharmacological policies have a damping effect on the pandemic casualties and this can dominate the non-linear dynamics. Herein, a model without pharmacological or non-pharmacological policies might have more dominant non-linear dynamics. In addition, the paper considers two machine learning approaches to optimize the unknown parameters of the constructed models. The results show that the recursive neural network has superior performance for learning nonlinear dynamics. However, the batch least squares outperforms in the presence of linear dynamics and stochastic data. The estimated future pandemic casualties with the linear S^pIⁿI^tI^bD-N model confirm that the suspicious, infected, and dead casualties converge to zero from 200000, 1400, 200 casualties, respectively. The convergences occur in 120 days under the current conditions.

Trends in outcomes following COVID-19 symptom onset in Milan: a cohort study

BACKGROUND

For people with symptomatic COVID-19, the relative risks of hospital admission, death without hospital admission and recovery without admission, and the times to those events, are not well understood. We describe how these quantities varied with individual characteristics, and through the first wave of the pandemic, in Milan, Italy.

METHODS

A cohort study of 27 598 people with known COVID-19 symptom onset date in Milan, Italy, testing positive between February and June 2020 and followed up until 17 July 2020. The probabilities of different events, and the times to events, were estimated using a mixture multistate model.

RESULTS

The risk of death without hospital admission was higher in March and April (for non-care home residents, 6%-8% compared with 2%-3% in other months) and substantially higher for care home residents (22%-29% in March). For all groups, the probabilities of hospitalisation decreased from February to June. The probabilities of hospitalisation also increased with age, and were higher for men, substantially lower for healthcare workers and care home residents, and higher for people with comorbidities. Times to hospitalisation and confirmed recovery also decreased throughout the first wave. Combining these results with our previously developed model for events following hospitalisation, the overall symptomatic case fatality risk was 15.8% (15.4%-16.2%).

CONCLUSIONS

The highest risks of death before hospital admission coincided with periods of severe burden on the healthcare system in Lombardy. Outcomes for care home residents were particularly poor. Outcomes improved as the first wave waned, community healthcare resources were reinforced and testing became more widely available.

COVID-19 epidemic in New York City: development of an age group-specific mathematical model to predict the outcome of various vaccination strategies

Background

Since December 14, 2020, New York City (NYC) has started the first batch of COVID-19 vaccines. However, the shortage of vaccines is currently an inevitable problem. Therefore, optimizing the age-specific COVID-19 vaccination is an important issue that needs to be addressed as a priority.

Objective

Combined with the reported COVID-19 data in NYC, this study aimed to construct a mathematical model with five age groups to estimate the impact of age-specific vaccination on reducing the prevalence of COVID-19.

Methods

We proposed an age-structured mathematical model and estimated the unknown parameters based on the method of Markov Chain Monte Carlo (MCMC). We also calibrated our model by using three different types of reported COVID-19 data in NYC. Moreover, we evaluated the reduced cumulative number of deaths and new infections with different vaccine allocation strategies.

Results

Compared with the current vaccination strategy in NYC, if we gradually increased the vaccination coverage rate for only one age groups from March 1, 2021 such that the vaccination coverage rate would reach to 40% by June 1, 2021, then as of June 1, 2021, the cumulative deaths in the 75–100 age group would be reduced the most, about 72 fewer deaths per increased 100,000 vaccinated individuals, and the cumulative new infections in the 0–17 age group would be reduced the most, about 21,591 fewer new infections per increased 100,000 vaccinated individuals. If we gradually increased the vaccination coverage rate for two age groups from March 1, 2021 such that the vaccination coverage rate would reach to 40% by June 1, 2021, then as of June 1, 2021, the cumulative deaths in the 65–100 age group would be reduced the most, about 36 fewer deaths per increased 100,000 vaccinated individuals, and the cumulative new infections in the 0–44 age group would be reduced the most, about 17,515 fewer new infections per increased 100,000 vaccinated individuals. In addition, if we had an additional 100,000 doses of vaccine for 0–17 and 75–100 age groups as of June 1, 2021, then the allocation of 80% to the 0–17 age group and 20% to the 75–100 age group would reduce the maximum numbers of new infections and deaths simultaneously in NYC.

Conclusions

The COVID-19 burden including deaths and new infections would decrease with increasing vaccination coverage rate. Priority vaccination to the elderly and adolescents would minimize both deaths and new infections.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12985-022-01771-9.

Signaling Through FcγRIIA and the C5a-C5aR Pathway Mediate Platelet Hyperactivation in COVID-19

Patients with COVID-19 present with a wide variety of clinical manifestations. Thromboembolic events constitute a significant cause of morbidity and mortality in patients infected with SARS-CoV-2. Severe COVID-19 has been associated with hyperinflammation and pre-existing cardiovascular disease. Platelets are important mediators and sensors of inflammation and are directly affected by cardiovascular stressors. In this report, we found that platelets from severely ill, hospitalized COVID-19 patients exhibited higher basal levels of activation measured by P-selectin surface expression and had poor functional reserve upon in vitro stimulation. To investigate this question in more detail, we developed an assay to assess the capacity of plasma from COVID-19 patients to activate platelets from healthy donors. Platelet activation was a common feature of plasma from COVID-19 patients and correlated with key measures of clinical outcome including kidney and liver injury, and APACHEIII scores. Further, we identified ferritin as a pivotal clinical marker associated with platelet hyperactivation. The COVID-19 plasma-mediated effect on control platelets was highest for patients that subsequently developed inpatient thrombotic events. Proteomic analysis of plasma from COVID-19 patients identified key mediators of inflammation and cardiovascular disease that positively correlated with in vitro platelet activation. Mechanistically, blocking the signaling of the FcγRIIa-Syk and C5a-C5aR pathways on platelets, using antibody-mediated neutralization, IgG depletion or the Syk inhibitor fostamatinib, reversed this hyperactivity driven by COVID-19 plasma and prevented platelet aggregation in endothelial microfluidic chamber conditions. These data identified these potentially actionable pathways as central for platelet activation and/or vascular complications and clinical outcomes in COVID-19 patients. In conclusion, we reveal a key role of platelet-mediated immunothrombosis in COVID-19 and identify distinct, clinically relevant, targetable signaling pathways that mediate this effect.

Response Efficacy and Self-Efficacy Mediated the Relationship between Perceived Threat and Psychic Anxiety among College Students in the Early Stage of the COVID-19 Pandemic

Applying Fear Appeals Theory and Social Learning Theory, this study aims to explore the impact of perceived threat on psychic anxiety among college students in the early stage of the COVID-19 pandemic and the mediating roles of response efficacy and self-efficacy. An empirical study was conducted using an online cross-sectional survey in the early stage of the COVID-19 pandemic in February 2020. A random sampling method was applied to administer questionnaires to 646 Chinese college students. The results showed that: (1) the perceived threat of the COVID-19 pandemic, including perceived susceptibility and severity, was positively correlated with psychic anxiety; (2) self-efficacy mediated the effect of both perceived susceptibility and severity on psychic anxiety, while the response efficacy only mediated the effect of perceived susceptibility on psychic anxiety; and (3) response efficacy and self-efficacy played a serial mediating role on the relationship between perceived susceptibility and psychic anxiety. This study elucidates the relationship between perceived threat and psychic anxiety from the perspective of cognitive appraisal of threat, showing the role positive efficacy appraisal played in reducing psychic anxiety, which could be induced by the perceived threat of major public health emergencies such as COVID-19 pandemic.

Hospital workforce mental reaction to the pandemic in a low COVID-19 burden setting: a cross-sectional clinical study

Τhe COVID-19 pandemic has mental health implications for both healthcare workforces and general population, particularly in regions heavily hit by the crisis. Τhe study aimed (i) to investigate anxiety- and depression severity differences between staff of a COVID-19 treatment unit (N = 84) and a hospital without such a unit (N = 55) in comparison to participants of a convenience general population online survey (N = 240) and (ii) to explore relations between such symptoms and hospital staff reaction to COVID-19 in a low COVID-19 burden setting. Anxiety was studied with the Generalized Anxiety Disorder 7-Item in hospital workforces and with the Hospital Anxiety Depression Scale (HADS) in online survey participants. Depression symptoms were assessed with the Patient Health Questionnaire-9 in hospital employees and the HADS in the online survey sample. Symptoms were classified as absent/minimal, borderline abnormal or indicating clinical caseness. Staff reaction to COVID-19 was tapped with a 9-item-questionnaire and the 22-item Impact of Event Scale-revised (IES-R). Proper tests for differences and stepwise ordered logistic regression models were employed. Anxiety- and depression severity was higher in hospital workforces than in online survey participants (P < 0.05). Anxiety was more severe in frontline- compared to backstage employees (P < 0.001) was inversely correlated with age (P = 0.011) and positively with avoidance (P = 0.028). Both anxiety and depression symptoms related to intrusion symptoms (P < 0.001). Regarding the relatively long data collection period, an inverse association between crisis duration and depression symptoms was detected (P = 0.025). These observations point to the urgent need for distress-mitigating interventions for hospital workforces even in low COVID-19 burden settings.

Investigating vaccine-induced immunity and its effect in mitigating SARS-CoV-2 epidemics in China

BACKGROUND

To allow a return to a pre-COVID-19 lifestyle, virtually every country has initiated a vaccination program to mitigate severe disease burden and control transmission. However, it remains to be seen whether herd immunity will be within reach of these programs.

METHODS

We developed a compartmental model of SARS-CoV-2 transmission for China, a population with low prior immunity from natural infection. Two vaccination programs were tested and model-based estimates of the immunity level in the population were provided.

RESULTS

We found that it is unlikely to reach herd immunity for the Delta variant given the relatively low efficacy of the vaccines used in China throughout 2021 and the lack of prior natural immunity. We estimated that, assuming a vaccine efficacy of 90% against the infection, vaccine-induced herd immunity would require a coverage of 93% or higher of the Chinese population. However, even when vaccine-induced herd immunity is not reached, we estimated that vaccination programs can reduce SARS-CoV-2 infections by 50-62% in case of an all-or-nothing vaccine model and an epidemic starts to unfold on December 1, 2021.

CONCLUSIONS

Efforts should be taken to increase population's confidence and willingness to be vaccinated and to develop highly efficacious vaccines for a wide age range.

Epidemiology of SARS-CoV-2 Infection in Italy Using Real-World Data: Methodology and Cohort Description of the Second Phase of Web-Based EPICOVID19 Study

Digital technologies have been extensively employed in response to the SARS-CoV-2 pandemic worldwide. This study describes the methodology of the two-phase internet-based EPICOVID19 survey, and the characteristics of the adult volunteer respondents who lived in Italy during the first (April-May 2020) and the second wave (January-February 2021) of the epidemic. Validated scales and ad hoc questionnaires were used to collect socio-demographic, medical and behavioural characteristics, as well as information on COVID-19. Among those who provided email addresses during phase I (105,355), 41,473 participated in phase II (mean age 50.7 years ± 13.5 SD, 60.6% females). After a median follow-up of ten months, 52.8% had undergone nasopharyngeal swab (NPS) testing and 13.2% had a positive result. More than 40% had undergone serological test (ST) and 11.9% were positive. Out of the 2073 participants with at least one positive ST, 72.8% had only negative results from NPS or never performed it. These results indicate that a large fraction of individuals remained undiagnosed, possibly contributing to the spread of the virus in the community. Participatory online surveys offer a unique opportunity to collect relevant data at individual level from large samples during confinement.

Psychological differences in adults with and without a COVID-19 diagnosis

BACKGROUND

Substantial evidence is emerging regarding the broad societal and psychological impacts of the COVID-19 pandemic, but little is known about whether infected individuals are differently affected.

AIM

We evaluated psychological differences between individuals who do vs. do not report testing positive for COVID-19.

METHODS

An online survey was offered to adults (≥18 years) who were diagnosed with COVID-19 by a provider within a large integrated-delivery healthcare system, enrolled in COVID-19-related clinical trials at the healthcare system, or responded to targeted local distribution. Measures assessed included the 8-item Patient Health Questionnaire depression scale, Generalized Anxiety Disorder 7-item Scale, and Posttraumatic Diagnostic Scale for DSM-5.

RESULTS

Of 487 respondents, 43% reported testing positive for COVID-19, including 11% requiring hospitalization. Overall rates of general anxiety disorder and posttraumatic stress were 34% and 16%, respectively, with no significant differences between groups. Prevalence of depression was higher among respondents reporting a positive COVID-19 test (52% vs. 31%). This difference persisted after controlling for respondent characteristics (odds ratio = 3.7, p < 0.01).

CONCLUSIONS

People who report testing positive for COVID-19, even those not requiring hospitalization, have increased risk for depression. Mental health care screening and services should be offered to individuals testing positive, facilitating early intervention.

Clinical profile of coronavirus disease 2019 comparing the first and second waves: A single-center study from North India

Background and Objectives:

Severe acute respiratory syndrome coronavirus 2, caused by the novel coronavirus disease 2019 (COVID-19), led to a devastating pandemic that hit majority of the countries globally in a wave-like pattern. The characteristics of the disease varied in different geographical areas and different populations. This study highlights the epidemiological and clinical characteristics of COVID-19 during two major waves in North India.

Materials and Methods:

Clinical characteristics and outcomes of all COVID-19-reverse transcription-polymerase chain reaction-positive patients, admitted from March 2020 to June 2021, to a tertiary care center in North India, were studied retrospectively.

Results:

During this period, total of 5652 patients were diagnosed having COVID. Patients who were incidentally diagnosed as COVID-positive (n=667) with other unrelated comorbid conditions and patients admitted under level 1 facility (n=1655; 1219 from first and 436 from second wave) were excluded from final analysis. Males were most commonly affected in both waves, with male to female ratio 4:1 in first and 3:1 in second wave. First wave had significantly more people with co-morbidities like diabetes mellitus and hypertension (P=0.001), whereas younger age group (age <40 years) were significantly more affected in second wave (P= 0.000). Fever was the most common presenting complaint in both waves, followed by cough and breathlessness. Patients during first wave had more severe disease at presentation and high mortality compared to the second wave.

Conclusion:

Majority of the patients with COVID-19 infection presenting to our hospital were young during the second wave. Fever was noted as presenting manifestation. Mortality was low during the second wave as compared to the first wave, likely to be due to proper protocol-based treatment resulting in better outcomes.

Effects of college students' mindfulness on depression symptoms during the epidemic prevention and control period: The mediating effect of psychological resilience

Depression symptoms significantly impact college students' mental health, particularly during the "closed management" period during the spread of COVID-19. Exploring the mechanism that affects college students' depression symptoms can help alleviate the impact of closed management policies on individual mental health and improve their mental health level. The onset of the COVID-19 pandemic resulted in the normalization of epidemic prevention and control in China and the implementation of the dynamic zero-COVID policy. This study used the Five-Factor Mindfulness Questionnaire-Short Form, Psychological Resilience Scale, and Beck Depression Scale to investigate the mindfulness, psychological resilience, and depression symptoms of 1,062 students under closed management conditions at Northwest Normal University. The mindfulness, psychological resilience, and depression status of students in closed management were investigated using an online questionnaire survey. Eight hundred and ten college students (M _age = 20.43, SD = 1.67, range = 17-30) were selected to test the model using the structural equation model and bootstrap method. The results showed that the gender differences in mindfulness and psychological resilience were not significant. Gender differences in depression symptoms were significant, and depression symptoms in men were significantly higher than in women. Grade differences in resilience, mindfulness, and depression levels were not significant. Thus, psychological resilience is negatively associated with depressive symptoms. Psychological resilience plays a mediating role between mindfulness and depressive symptoms. This study provides reference and inspiration for improving college students' mental health under epidemic prevention and control circumstances.

Innovations and development of Covid-19 vaccines: A patent review

More than 125 million confirmed cases of COVID-19 have been reported globally with rising cases in all countries since the first case was reported. A vaccine is the best measure for the effective prevention and control of COVID-19. There are more than 292 COVID-19 candidates' vaccines being developed as of July 2021 of which 184 are in human preclinical trials. A patent provides protection and a marketing monopoly to the inventor of an invention for a specified period. Therefore, vaccine developers, including Moderna, BioNTech, Janssen, Inovio, and Gamaleya also filed patent applications for the protection of their vaccines. This review aims to provide an insight into the patent literature of COVID-19 vaccines. The patent search was done using Patentscope and Espacenet databases. The results have revealed that most of the key players have patented their inventive COVID-19 vaccine. Many patent applications related to COVID-19 vaccines developed via different technologies (DNA, RNA, virus, bacteria, and protein subunit) have also been filed. The publication of a normal patent application takes place after 18 months of its filing. Therefore, many patents/patent applications related to the COVID-19 vaccine developed through different technology may come into the public domain in the coming days.

Doctor-patient relationship improved during COVID-19 pandemic, but weakness remains

OBJECTIVE

To assess the quality of the doctor-patient relationship (DPR) in China and possible influencing factors during the COVID-19 period from the patient's perspective.

METHODS

An online survey was carried out nationwide from March 12, 2020 to March 30, 2020 in China via a convenience sampling strategy. Patients who met the inclusion criteria were invited to complete a questionnaire regarding the quality of DPR, including sociodemographic information, the Patient-Doctor Relationship Questionnaire (PDRQ-9), and influencing factors for DPR during the pandemic.

RESULTS

A total of 1903 patients were included. Our result showed that participants had a higher PDRQ-9 score during the COVID-19 pandemic (4.18 ± 0.51) than that before the COVID-19 pandemic (3.86 ± 0.67). Importance-performance analysis (IPA) revealed that doctor-patient communication, patient satisfaction, consultation time, doctor's attitude, and medical knowledge were specific aspects that needed to be prioritized to improve the DPR. Multiple linear regression analysis suggested that positive media reports, telemedicine, and national policies had a significantly positive effect on the DPR during the pandemic (P < 0.05).

CONCLUSION

In general, the DPR had been improved during the COVID-19 pandemic. Our research found the key points that needed to be prioritized to improve the DPR during the pandemic, which may provide effective suggestions for building a harmonious DPR in the future.

A quantitative assessment of epidemiological parameters required to investigate COVID-19 burden

Solid estimates describing the clinical course of SARS-CoV-2 infections are still lacking due to under-ascertainment of asymptomatic and mild-disease cases. In this work, we quantify age-specific probabilities of transitions between stages defining the natural history of SARS-CoV-2 infection from 1965 SARS-CoV-2 positive individuals identified in Italy between March and April 2020 among contacts of confirmed cases. Infected contacts of cases were confirmed via RT-PCR tests as part of contact tracing activities or retrospectively via IgG serological tests and followed-up for symptoms and clinical outcomes. In addition, we provide estimates of time intervals between key events defining the clinical progression of cases as obtained from a larger sample, consisting of 95,371 infections ascertained between February and July 2020. We found that being older than 60 years of age was associated with a 39.9% (95%CI: 36.2–43.6%) likelihood of developing respiratory symptoms or fever ≥ 37.5 °C after SARS-CoV-2 infection; the 22.3% (95%CI: 19.3–25.6%) of the infections in this age group required hospital care and the 1% (95%CI: 0.4–2.1%) were admitted to an intensive care unit (ICU). The corresponding proportions in individuals younger than 60 years were estimated at 27.9% (95%CI: 25.4–30.4%), 8.8% (95%CI: 7.3–10.5%) and 0.4% (95%CI: 0.1–0.9%), respectively. The infection fatality ratio (IFR) ranged from 0.2% (95%CI: 0.0–0.6%) in individuals younger than 60 years to 12.3% (95%CI: 6.9–19.7%) for those aged 80 years or more; the case fatality ratio (CFR) in these two age classes was 0.6% (95%CI: 0.1–2%) and 19.2% (95%CI: 10.9–30.1%), respectively. The median length of stay in hospital was 10 (IQR: 3–21) days; the length of stay in ICU was 11 (IQR: 6–19) days. The obtained estimates provide insights into the epidemiology of COVID-19 and could be instrumental to refine mathematical modeling work supporting public health decisions.

The Importance of RNA-Based Vaccines in the Fight against COVID-19: An Overview

In recent years, vaccine development using ribonucleic acid (RNA) has become the most promising and studied approach to produce safe and effective new vaccines, not only for prophylaxis but also as a treatment. The use of messenger RNA (mRNA) as an immunogenic has several advantages to vaccine development compared to other platforms, such as lower coast, the absence of cell cultures, and the possibility to combine different targets. During the COVID-19 pandemic, the use of mRNA as a vaccine became more relevant; two out of the four most widely applied vaccines against COVID-19 in the world are based on this platform. However, even though it presents advantages for vaccine application, mRNA technology faces several pivotal challenges to improve mRNA stability, delivery, and the potential to generate the related protein needed to induce a humoral- and T-cell-mediated immune response. The application of mRNA to vaccine development emerged as a powerful tool to fight against cancer and non-infectious and infectious diseases, for example, and represents a relevant research field for future decades. Based on these advantages, this review emphasizes mRNA and self-amplifying RNA (saRNA) for vaccine development, mainly to fight against COVID-19, together with the challenges related to this approach.

CT Examinations for COVID-19: A Systematic Review of Protocols, Radiation Dose, and Numbers Needed to Diagnose and Predict

Purpose

Although chest CT has been discussed as a first-line test for coronavirus disease 2019 (COVID-19), little research has explored the implications of CT exposure in the population. To review chest CT protocols and radiation doses in COVID-19 publications and explore the number needed to diagnose (NND) and the number needed to predict (NNP) if CT is used as a first-line test.

Materials and Methods

We searched nine highly cited radiology journals to identify studies discussing the CT-based diagnosis of COVID-19 pneumonia. Study-level information on the CT protocol and radiation dose was collected, and the doses were compared with each national diagnostic reference level (DRL). The NND and NNP, which depends on the test positive rate (TPR), were calculated, given a CT sensitivity of 94% (95% confidence interval [CI]: 91%-96%) and specificity of 37% (95% CI: 26%-50%), and applied to the early outbreak in Wuhan, New York, and Italy.

Results

From 86 studies, the CT protocol and radiation dose were reported in 81 (94.2%) and 17 studies (19.8%), respectively. Low-dose chest CT was used more than twice as often as standard-dose chest CT (39.5% vs.18.6%), while the remaining studies (44.2%) did not provide relevant information. The radiation doses were lower than the national DRLs in 15 of the 17 studies (88.2%) that reported doses. The NND was 3.2 scans (95% CI: 2.2-6.0). The NNPs at TPRs of 50%, 25%, 10%, and 5% were 2.2, 3.6, 8.0, 15.5 scans, respectively. In Wuhan, 35418 (TPR, 58%; 95% CI: 27710-56755) to 44840 (TPR, 38%; 95% CI: 35161-68164) individuals were estimated to have undergone CT examinations to diagnose 17365 patients. During the early surge in New York and Italy, daily NNDs changed up to 5.4 and 10.9 times, respectively, within 10 weeks.

Conclusion

Low-dose CT protocols were described in less than half of COVID-19 publications, and radiation doses were frequently lacking. The number of populations involved in a first-line diagnostic CT test could vary dynamically according to daily TPR; therefore, caution is required in future planning.

Cellular and humoral immune responses following SARS-CoV-2 mRNA vaccination in patients with multiple sclerosis on anti-CD20 therapy

SARS-CoV-2 messenger RNA vaccination in healthy individuals generates immune protection against COVID-19. However, little is known about SARS-CoV-2 mRNA vaccine-induced responses in immunosuppressed patients. We investigated induction of antigen-specific antibody, B cell and T cell responses longitudinally in patients with multiple sclerosis (MS) on anti-CD20 antibody monotherapy (n = 20) compared with healthy controls (n = 10) after BNT162b2 or mRNA-1273 mRNA vaccination. Treatment with anti-CD20 monoclonal antibody (aCD20) significantly reduced spike-specific and receptor-binding domain (RBD)-specific antibody and memory B cell responses in most patients, an effect ameliorated with longer duration from last aCD20 treatment and extent of B cell reconstitution. By contrast, all patients with MS treated with aCD20 generated antigen-specific CD4 and CD8 T cell responses after vaccination. Treatment with aCD20 skewed responses, compromising circulating follicular helper T (TFH) cell responses and augmenting CD8 T cell induction, while preserving type 1 helper T (TH1) cell priming. Patients with MS treated with aCD20 lacking anti-RBD IgG had the most severe defect in circulating TFH responses and more robust CD8 T cell responses. These data define the nature of the SARS-CoV-2 vaccine-induced immune landscape in aCD20-treated patients and provide insights into coordinated mRNA vaccine-induced immune responses in humans. Our findings have implications for clinical decision-making and public health policy for immunosuppressed patients including those treated with aCD20.

No Sex Differences in Psychological Burden and Health Behaviors of Healthcare Workers During the COVID-19 Stay-at-Home Orders

Background: Females with novel coronavirus disease 2019 (COVID-19) state-ordered home isolation were associated with higher anxiety and reduced sleep quality than males. Sex differences in psychobehavioral changes during the COVID-19 stay-at home orders among healthcare workers remained unclear. The purpose of this study was to explore the sex differences in psychological burden and health behaviors among these persons.

Methods: This was a cross-sectional study using online data available in the open Interuniversity Consortium for Political and Social Research (OPENICPSR). Healthcare workers including females and males who transitioned to working from home during the COVID-19 stay-at-home orders were included. Sex differences were compared using the chi-square test and Student's t-test. We performed logistic and linear regression analyses to determine the association of females with psychological burden and health behaviors.

Results: A total of 537 respondents (425 females and 112 males) were enrolled in our study. Sex differences in age (42.1 ± 12.3 years vs. 46.6 ± 15.7 years, t = −2.821, p = 0.005), occupation (χ² = 41.037, p < 0.001), mood change (n = 297, 69.9% vs. n = 61, 54.5%, χ² = 9.482, p = 0.002), bedtime schedule (χ² = 6.254, p = 0.044) and news consumption (n = 344, 80.9% vs. n = 76, 67.9%, χ² = 8.905, p = 0.003) were statistically significant. Logistic regression showed that females was negatively associated with better mood status (OR = 0.586, 95% CI 0.153–2.247, p = 0.436). In addition, linear regression showed that females were not correlated with total sleep time after adjusting for sio-demographics, mental health outcomes and health behaviors (B = 0.038, 95% CI −0.313–0.388, p = 0.833).

Conclusion: No sex differences in psychological burden and health behaviors of healthcare workers were found during the COVID-19 stay-at-home orders. The COVID-19 state-ordered home isolation may be a potential way to reduce disproportionate effects of COVID-19 pandemic on females and help to minimize sex differences in psychological burden and health behaviors among healthcare workers.

Food insecurity arises the likelihood of hospitalization in patients with COVID-19

The World Health Organization (WHO) has declared the Corona pandemic as a public health emergency. This pandemic affects the main pillars of food security. This study aimed to investigate the relationship between food insecurity and the probability of hospitalization and the length of the recovery period after getting COVID-19. The cross-sectional study was performed through the census on COVID-19 patients diagnosed in Fasa, Iran. Informed consent, demographic, and food security questionnaire were completed over the phone. Then, all patients were followed up until recovery. Data were analyzed using SPSS26 and Chi-square test, t-test, and logistic regression (P < 0.05). In this study, 219 COVID-19 patients [100 (54.7%) male and 119 (54.3%) female] with a mean age of 40.05 ± 15.54 years old were examined. Possibility of hospitalization and the length of the recovery period of more than one month was significantly longer in the food-insecure group (P = 0.001) and (P = 0.37), respectively, but the mean length of hospital stay in the two groups was not significantly different (P = 0.76). After adjusting for all confounding variables, people with food insecurity were 3.9 times more likely to be hospitalized than those with food security. Overall, we observed that food-insecure people were significantly more likely to be hospitalized than the secure group.

"The possible implication of endothelin in the pathology of COVID-19-induced pulmonary hypertension"

COVID-19 pandemic has changed the world dramatically since was first reported in Wuhan city, China [1]. Not only as a respiratory illness that could lead to fatal respiratory failure, but also some evidences suggest that it can propagate as a chronic disease associated with a variety of persistent post COVID-19 pathologies that affect patients' life [2,3]. Pulmonary hypertension (PH) is one of the challenging diseases that may develop as a consequence of SARS-COV-2 infection in some COVID-19 survivors [4,5]. The vasopressor, proliferative, proinflammatory, and prothrombotic actions of endothelin [6] may be encountered in the COVID-19-induced PH pathology. And so, endothelin blockers may have an important role to restrict the development of serious PH outcomes with special precautions considering patients with significant hypoxemia.

Evaluating the effectiveness of control measures in multiple regions during the early phase of the COVID-19 pandemic in 2020

The number of COVID-19 confirmed cases rapidly grew since the SARS-CoV-2 virus was identified in late 2019. Due to the high transmissibility of this virus, more countries are experiencing the repeated waves of the COVID-19 pandemic. However, with limited manufacturing and distribution of vaccines, control measures might still be the most critical measures to contain outbreaks worldwide. Therefore, evaluating the effectiveness of various control measures is necessary to inform policymakers and improve future preparedness. In addition, there is an ongoing need to enhance our understanding of the epidemiological parameters and the transmission patterns for a better response to the COVID-19 pandemic. This review focuses on how various models were applied to guide the COVID-19 response by estimating key epidemiologic parameters and evaluating the effectiveness of control measures. We also discuss the insights obtained from the prediction of COVID-19 trajectories under different control measures scenarios.

SARS-CoV-2 infection hospitalization, severity, criticality, and fatality rates in Qatar

The SARS-CoV-2 pandemic resulted in considerable morbidity and mortality as well as severe economic and societal disruptions. Despite scientific progress, true infection severity, factoring both diagnosed and undiagnosed infections, remains poorly understood. This study aimed to estimate SARS-CoV-2 age-stratified and overall morbidity and mortality rates based on analysis of extensive epidemiological data for the pervasive epidemic in Qatar, a country where < 9% of the population are ≥ 50 years. We show that SARS-CoV-2 severity and fatality demonstrate a striking age dependence with low values for those aged < 50 years, but rapidly growing rates for those ≥ 50 years. Age dependence was particularly pronounced for infection criticality rate and infection fatality rate. With Qatar's young population, overall SARS-CoV-2 severity and fatality were not high with < 4 infections in every 1000 being severe or critical and < 2 in every 10,000 being fatal. Only 13 infections in every 1000 received any hospitalization in acute-care-unit beds and < 2 in every 1000 were hospitalized in intensive-care-unit beds. However, we show that these rates would have been much higher if Qatar's population had the demographic structure of Europe or the United States. Epidemic expansion in nations with young populations may lead to considerably lower disease burden than currently believed.

Time-varying optimization of COVID-19 vaccine prioritization in the context of limited vaccination capacity

Dynamically adapting the allocation of COVID-19 vaccines to the evolving epidemiological situation could be key to reduce COVID-19 burden. Here we developed a data-driven mechanistic model of SARS-CoV-2 transmission to explore optimal vaccine prioritization strategies in China. We found that a time-varying vaccination program (i.e., allocating vaccines to different target groups as the epidemic evolves) can be highly beneficial as it is capable of simultaneously achieving different objectives (e.g., minimizing the number of deaths and of infections). Our findings suggest that boosting the vaccination capacity up to 2.5 million first doses per day (0.17% rollout speed) or higher could greatly reduce COVID-19 burden, should a new wave start to unfold in China with reproduction number ≤1.5. The highest priority categories are consistent under a broad range of assumptions. Finally, a high vaccination capacity in the early phase of the vaccination campaign is key to achieve large gains of strategic prioritizations.

Time-varying optimization of COVID-19 vaccine prioritization in the context of limited vaccination capacity

Dynamically adapting the allocation of COVID-19 vaccines to the evolving epidemiological situation could be key to reduce COVID-19 burden. Here we developed a data-driven mechanistic model of SARS-CoV-2 transmission to explore optimal vaccine prioritization strategies in China. We found that a time-varying vaccination program (i.e., allocating vaccines to different target groups as the epidemic evolves) can be highly beneficial as it is capable of simultaneously achieving different objectives (e.g., minimizing the number of deaths and of infections). Our findings suggest that boosting the vaccination capacity up to 2.5 million first doses per day (0.17% rollout speed) or higher could greatly reduce COVID-19 burden, should a new wave start to unfold in China with reproduction number ≤1.5. The highest priority categories are consistent under a broad range of assumptions. Finally, a high vaccination capacity in the early phase of the vaccination campaign is key to achieve large gains of strategic prioritizations.

HLA genetic polymorphism in patients with Coronavirus Disease 2019 in Midwestern United States

The experience of individuals with Coronavirus Disease 2019 (COVID‐19) ranges from asymptomatic to life threatening multi‐organ dysfunction. Specific HLA alleles may affect the predisposition to severe COVID‐19 because of their role in presenting viral peptides to launch the adaptive immune response to severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). In this population‐based case–control study in the midwestern United States, we performed high‐resolution HLA typing of 234 cases hospitalized for COVID‐19 in the St. Louis metropolitan area and compared their HLA allele frequencies with those of 22,000 matched controls from the National Marrow Donor Program (NMDP). We identified two predisposing alleles, HLA‐DRB1*08:02 in the Hispanic group (OR = 9.0, 95% confidence interval: 2.2–37.9; adjusted p = 0.03) and HLA‐A*30:02 in younger African Americans with ages below the median (OR = 2.2, 1.4–3.6; adjusted p = 0.01), and several candidate alleles with potential associations with COVID‐19 in African American, White, and Hispanic groups. We also detected risk‐associated amino acid residues in the peptide binding grooves of some of these alleles, suggesting the presence of functional associations. These findings support the notion that specific HLA alleles may be protective or predisposing factors to COVID‐19. Future consortium analysis of pooled cases and controls is warranted to validate and extend these findings, and correlation with viral peptide binding studies will provide additional evidence for the functional association between HLA alleles and COVID‐19.

A Scenario-Based Evaluation of COVID-19-Related Essential Clinical Resource Demands in China

The coronavirus disease 2019 (COVID-19) pandemic is a global crisis, and medical systems in many countries are overwhelmed with supply shortages and increasing demands to treat patients due to the surge in cases and severe illnesses. This study aimed to assess COVID-19-related essential clinical resource demands in China, based on different scenarios involving COVID-19 spreads and interventions. We used a susceptible-exposed-infectious-hospitalized/isolated-removed (SEIHR) transmission dynamics model to estimate the number of COVID-19 infections and hospitalizations with corresponding essential healthcare resources needed. We found that, under strict non-pharmaceutical interventions (NPIs) or mass vaccination of the population, China would be able to contain community transmission and local outbreaks rapidly. However, under scenarios involving a low intensity of implemented NPIs and a small proportion of the population vaccinated, the use of a peacetime-wartime transition model would be needed for medical source stockpiles and preparations to ensure a normal functioning healthcare system. The implementation of COVID-19 vaccines and NPIs in different periods can influence the transmission of COVID-19 and subsequently affect the demand for clinical diagnosis and treatment. An increased proportion of asymptomatic infections in simulations will not reduce the demand for medical resources; however, attention must be paid to the increasing difficulty in containing COVID-19 transmission due to asymptomatic cases. This study provides evidence for emergency preparations and the adjustment of prevention and control strategies during the COVID-19 pandemic. It also provides guidance for essential healthcare investment and resource allocation.

Assessing the Effect of Global Travel and Contact Restrictions on Mitigating the COVID-19 Pandemic

Travel restrictions and physical distancing have been implemented across the world to mitigate the coronavirus disease 2019 (COVID-19) pandemic, but studies are needed to understand their effectiveness across regions and time. Based on the population mobility metrics derived from mobile phone geolocation data across 135 countries or territories during the first wave of the pandemic in 2020, we built a metapopulation epidemiological model to measure the effect of travel and contact restrictions on containing COVID-19 outbreaks across regions. We found that if these interventions had not been deployed, the cumulative number of cases could have shown a 97-fold (interquartile range 79-116) increase, as of May 31, 2020. However, their effectiveness depended upon the timing, duration, and intensity of the interventions, with variations in case severity seen across populations, regions, and seasons. Additionally, before effective vaccines are widely available and herd immunity is achieved, our results emphasize that a certain degree of physical distancing at the relaxation of the intervention stage will likely be needed to avoid rapid resurgences and subsequent lockdowns.

Despite vaccination, China needs non-pharmaceutical interventions to prevent widespread outbreaks of COVID-19 in 2021

COVID-19 vaccination is being conducted in over 200 countries and regions to control SARS-CoV-2 transmission and return to a pre-pandemic lifestyle. However, understanding when non-pharmaceutical interventions (NPIs) can be lifted as immunity builds up remains a key question for policy makers. To address this, we built a data-driven model of SARS-CoV-2 transmission for China. We estimated that, to prevent the escalation of local outbreaks to widespread epidemics, stringent NPIs need to remain in place at least one year after the start of vaccination. Should NPIs alone be capable of keeping the reproduction number (R_t) around 1.3, the synergetic effect of NPIs and vaccination could reduce the COVID-19 burden by up to 99% and bring R_t below the epidemic threshold in about 9 months. Maintaining strict NPIs throughout 2021 is of paramount importance to reduce COVID-19 burden while vaccines are distributed to the population, especially in large populations with little natural immunity.

Using data-driven epidemiological modelling, Yu et al. estimate that, even with increasing vaccine availability, China will have to maintain stringent non-pharmaceutical interventions for at least a year to prevent new widespread outbreaks of COVID-19.

Impact of COVID-19 outbreaks and interventions on influenza in China and the United States

Coronavirus disease 2019 (COVID-19) was detected in China during the 2019-2020 seasonal influenza epidemic. Non-pharmaceutical interventions (NPIs) and behavioral changes to mitigate COVID-19 could have affected transmission dynamics of influenza and other respiratory diseases. By comparing 2019-2020 seasonal influenza activity through March 29, 2020 with the 2011-2019 seasons, we found that COVID-19 outbreaks and related NPIs may have reduced influenza in Southern and Northern China and the United States by 79.2% (lower and upper bounds: 48.8%-87.2%), 79.4% (44.9%-87.4%) and 67.2% (11.5%-80.5%). Decreases in influenza virus infection were also associated with the timing of NPIs. Without COVID-19 NPIs, influenza activity in China and the United States would likely have remained high during the 2019-2020 season. Our findings provide evidence that NPIs can partially mitigate seasonal and, potentially, pandemic influenza.

To what extent do we need to rely on non-pharmaceutical interventions while COVID-19 vaccines roll out in 2021?

COVID-19 vaccination is being conducted in over 190 countries/regions to control SARS-CoV-2 transmission and return to a pre-pandemic lifestyle. However, understanding when non-pharmaceutical interventions (NPIs) can be lifted as immunity builds up remain a key question for policy makers. To address it, we built a data-driven model of SARS-CoV-2 transmission for China. We estimated that to prevent the escalation of local outbreaks to widespread epidemics, stringent NPIs need to remain in place at least one year after the start of vaccination. Should NPIs alone be capable to keep the reproduction number (R _t ) around 1.3, the synergetic effect of NPIs and vaccination could reduce up to 99% of COVID-19 burden and bring R _t below the epidemic threshold in about 9 months. Maintaining strict NPIs throughout 2021 is of paramount importance to reduce COVID-19 burden while vaccines are distributed to the population, especially in large populations with little natural immunity.

Signaling through FcγRIIA and the C5a-C5aR pathway mediates platelet hyperactivation in COVID-19

Patients with COVID-19 present with a wide variety of clinical manifestations. Thromboembolic events constitute a significant cause of morbidity and mortality in patients infected with SARS-CoV-2. Severe COVID-19 has been associated with hyperinflammation and pre-existing cardiovascular disease. Platelets are important mediators and sensors of inflammation and are directly affected by cardiovascular stressors. In this report, we found that platelets from severely ill, hospitalized COVID-19 patients exhibit higher basal levels of activation measured by P-selectin surface expression, and have a poor functional reserve upon in vitro stimulation. Correlating clinical features to the ability of plasma from COVID-19 patients to stimulate control platelets identified ferritin as a pivotal clinical marker associated with platelet hyperactivation. The COVID-19 plasma-mediated effect on control platelets was highest for patients that subsequently developed inpatient thrombotic events. Proteomic analysis of plasma from COVID-19 patients identified key mediators of inflammation and cardiovascular disease that positively correlated with in vitro platelet activation. Mechanistically, blocking the signaling of the FcγRIIa-Syk and C5a-C5aR pathways on platelets, using antibody-mediated neutralization, IgG depletion or the Syk inhibitor fostamatinib, reversed this hyperactivity driven by COVID-19 plasma and prevented platelet aggregation in endothelial microfluidic chamber conditions, thus identifying these potentially actionable pathways as central for platelet activation and/or vascular complications in COVID-19 patients. In conclusion, we reveal a key role of platelet-mediated immunothrombosis in COVID-19 and identify distinct, clinically relevant, targetable signaling pathways that mediate this effect. These studies have implications for the role of platelet hyperactivation in complications associated with SARS-CoV-2 infection.

COVER ILLUSTRATION

ONE-SENTENCE SUMMARY

The FcγRIIA and C5a-C5aR pathways mediate platelet hyperactivation in COVID-19.

Machine Learning Approach to Predicting COVID-19 Disease Severity Based on Clinical Blood Test Data: Statistical Analysis and Model Development

Background

Accurate prediction of the disease severity of patients with COVID-19 would greatly improve care delivery and resource allocation and thereby reduce mortality risks, especially in less developed countries. Many patient-related factors, such as pre-existing comorbidities, affect disease severity and can be used to aid this prediction.

Objective

Because rapid automated profiling of peripheral blood samples is widely available, we aimed to investigate how data from the peripheral blood of patients with COVID-19 can be used to predict clinical outcomes.

Methods

We investigated clinical data sets of patients with COVID-19 with known outcomes by combining statistical comparison and correlation methods with machine learning algorithms; the latter included decision tree, random forest, variants of gradient boosting machine, support vector machine, k-nearest neighbor, and deep learning methods.

Results

Our work revealed that several clinical parameters that are measurable in blood samples are factors that can discriminate between healthy people and COVID-19–positive patients, and we showed the value of these parameters in predicting later severity of COVID-19 symptoms. We developed a number of analytical methods that showed accuracy and precision scores >90% for disease severity prediction.

Conclusions

We developed methodologies to analyze routine patient clinical data that enable more accurate prediction of COVID-19 patient outcomes. With this approach, data from standard hospital laboratory analyses of patient blood could be used to identify patients with COVID-19 who are at high risk of mortality, thus enabling optimization of hospital facilities for COVID-19 treatment.