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New therapies and vaccines changed the management of COVID-19. The aim of this retrospective study was to describe characteristics, in-hospital mortality and its predictors in patients with moderate/severe COVID-19, considering the 4 different pandemic waves and viral variants' prevalence from February 2020 to January 2022. Among 1135 patients included, 873 (77%) had at least one comorbidity, 177 (16%) were immunocompromised. From waves 1 to 4, patients with severe respiratory failure and ICU admission decreased over time (p < 0.001), like the length of in-hospital stay (p < 0.001). Despite a reduction of in-hospital mortality from 19% to 11%, increased risk of death was related to older age and immunocompromising conditions, especially during the 4th wave (HR = 5.07 and HR = 10.86, p < 0.001 respectively) while remdesivir treatment in the 3rd wave (HR = 0.41, p = 0.010) and positive serology (aHR = 0.66, p = 0.027) were protective for survival. These data support the need for tailoring vaccine campaign for future COVID-19 waves.

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Advancing climate change increases the risk of future infectious disease outbreaks, particularly of zoonotic diseases, by affecting the abundance and spread of viral vectors. Concerningly, there are currently no approved drugs for some relevant diseases, such as the arboviral diseases chikungunya, dengue or zika. The development of novel inhibitors takes 10-15 years to reach the market and faces critical challenges in preclinical and clinical trials, with approximately 30% of trials failing due to side effects. As an early response to emerging infectious diseases, CavitOmiX allows for a rapid computational screening of databases containing 3D point-clouds representing binding sites of approved drugs to identify candidates for off-label use. This process, known as drug repurposing, reduces the time and cost of regulatory approval. Here, we present potential approved drug candidates for off-label use, targeting the ADP-ribose binding site of Alphavirus chikungunya non-structural protein 3. Additionally, we demonstrate a novel in silico drug design approach, considering potential side effects at the earliest stages of drug development. We use a genetic algorithm to iteratively refine potential inhibitors for (i) reduced off-target activity and (ii) improved binding to different viral variants or across related viral species, to provide broad-spectrum and safe antivirals for the future.

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BACKGROUND: Understanding the dynamics of SARS-CoV-2 reinfections is crucial for public health policy, vaccine development, and long-term disease management. However, data on reinfections in the general population remains scarce. OBJECTIVES: This study aimed to investigate SARS-CoV-2 antibody dynamics among Austrian blood donors, representing healthy adults, over two years following primary infection and to evaluate the reinfection risk. METHODS: 117,895 blood donations were analysed for SARS-CoV-2 total anti-N levels from June 2020 to December 2023. We examined anti-N and anti-S antibody dynamics and in vitro functionality in 230 study participants at five defined times during 24 months, assessing associations with demographics, vaccination status, and reinfection awareness. RESULTS: The seroprevalence of SARS-CoV-2 infection-derived anti-N antibodies increased over time, reaching 90% by February 2023 and remaining at that level since then. According to serological screenings, we found an 88% reinfection rate, which is in contrast to participants' reports indicating a reinfection rate of 59%. Our data further reveal that about 26% of reinfections went completely unnoticed. Antibody dynamics were independent of age, sex, and ABO blood group. Interestingly, individuals with multiple reinfections reported symptoms more frequently during their primary infection. Our results further show that vaccination modestly affected reinfection risk and disease course. CONCLUSION: SARS-CoV-2 reinfections were uncommon until the end of 2021 but became common with the advent of Omicron. This study highlights the underestimation of reinfection rates in healthy adults and underscores the need for continued surveillance, which is an important support for public health policies and intervention strategies.

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COVID-19 induces re-circulating long-lived memory B cells (MBC) that, upon re-encounter with the pathogen, are induced to mount immunoglobulin responses. During convalescence, antibodies are subjected to affinity maturation, which enhances the antibody binding strength and generates new specificities that neutralize virus variants. Here, we performed a single-cell RNA sequencing analysis of spike-specific B cells from a SARS-CoV-2 convalescent subject. After COVID-19 vaccination, matured infection-induced MBC underwent recall and differentiated into plasmablasts. Furthermore, the transcriptomic profiles of newly activated B cells transiently shifted toward the ones of atypical and CXCR3+ B cells and several B-cell clonotypes massively expanded. We expressed monoclonal antibodies (mAbs) from all B-cell clones from the largest clonotype that used the VH3-53 gene segment. The in vitro analysis revealed that some somatic hypermutations enhanced the neutralization breadth of mAbs in a putatively stochastic manner. Thus, somatic hypermutation of B-cell clonotypes generates an anticipatory memory that can neutralize new virus variants.

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Interactions between SARS-CoV-2 and the immune system during infection are complex. However, understanding the within-host SARS-CoV-2 dynamics is of enormous importance for clinical and public health outcomes. Current mathematical models focus on describing the within-host SARS-CoV-2 dynamics during the acute infection phase. Thereby they ignore important long-term post-acute infection effects. We present a mathematical model, which not only describes the SARS-CoV-2 infection dynamics during the acute infection phase, but extends current approaches by also recapitulating clinically observed long-term post-acute infection effects, such as the recovery of the number of susceptible epithelial cells to an initial pre-infection homeostatic level, a permanent and full clearance of the infection within the individual, immune waning, and the formation of long-term immune capacity levels after infection. Finally, we used our model and its description of the long-term post-acute infection dynamics to explore reinfection scenarios differentiating between distinct variant-specific properties of the reinfecting virus. Together, the model's ability to describe not only the acute but also the long-term post-acute infection dynamics provides a more realistic description of key outcomes and allows for its application in clinical and public health scenarios.

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Virus mutations give rise to new variants that cause multiple waves of pandemics and escalate the infected number of individuals. In this paper, we develop both a simple random network that we define as a synthesized human interaction network and an epidemiological model based on the microscopic process of disease spreading to describe the epidemic process with three variants in a population with some features of social structure. The features of social structure we take into account in the model are the average number of degrees and the frequency of contacts. This paper shows many computational results from several scenarios both in varying network structures and epidemiological parameters that cannot be obtained numerically by using the compartmental model.

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BACKGROUND: Few studies highlight the stratification of COVID-19 vaccine effectiveness on MIS-C according to vaccine status, types and SARS-COV-2 variants. METHODS: A web-based analysis was conducted through searches of PubMed, Web of Science and Medline databases from January 1, 2020, to May 16, 2023. The search terms used were (multisystem inflammatory syndrome in children OR MIS-C OR PIMS OR PIMS-TS) AND (COVID-19 OR SARS-CoV-2) AND (vaccine OR vaccination) AND (children OR adolescents OR pediatric). RESULTS: 6701 children from 13 studies met the MIS-C definition. 92.1 % (1332/1446) of MIS-C cases were unvaccinated, whereas partial vaccination and full vaccination were 3.7 % (54/1446) and 4.2 % (60/1446)respectively. In the two studies encompassing 41 vaccinated MIS-C cases, 34 (82.9 %) received BNT162b2, 2 (4.9 %) received mRNA-1273, 4 (9.8 %) received Sinovac vaccine, and only one received a heterologous primary-boost regimen. Among 838 vaccinated MIS-C cases with different SARS-COV-2 variants, 23（2.8 %） were infected by the Wild-type, 80（9.5 %） by the Alpha variant, 521（62.2 %） by the Delta variant, and 214（25.5 %） by the Omicron variant. A significant difference was observed in vaccination rates among MIS-C cases across different variant pandemics (χ2 = 37.79, P < 0.001). The highest vaccination rate (26.3 %) occurred in the Alpha predominant period, thereafter dropped to 5.0 % in the Delta predominant period, and then increased to 12.6 % in the Omicron predominant period. CONCLUSIONS: Heterologous vaccination might provide a slightly more protective effect than homologous manner for MIS-C. As the virus mutates over time, its pathogenicity to MIS-C degrades among vaccinated individuals.

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Rabies is a fatal zoonotic disease caused by the rabies virus. Despite existing vaccines, failures still persist. Complete protection relies on improving vaccination for delayed antibody response and weak cellular immunity. A more effective and secure vaccine is necessary for rabies prevention. For this purpose, we employed the use of PIKA adjuvant, a stabilized double-stranded RNA that interacts with TLR3, as an enhancer for the rabies immunization. Testing on mice infected with seven rabies strains prevalent in China showed over 80% protective efficacy without immunoglobulin. In contrast, the PIKA rabies vaccine exhibited a more significant enhancement in neutralizing antibody levels just 5 days post-vaccination, surpassing the immune response induced by licensed rabies vaccines. Furthermore, the administration of the PIKA rabies vaccine resulted in a significant augmentation in the population of T cells that produce IFN-γ in response to the antigen. Additionally, elevated levels of IL-1ß, IL-6, CCL-2, and TNF-α were observed at the injection site. Furthermore, an increase in the levels of chemotactic proteins and pro-inflammatory molecules in the serum was observed following administration of the PIKA rabies vaccine. Confirmation of the mechanism of action of PIKA was further established by testing it on TLR3-knockout mice, proving that its adjuvant function is dependent on the TLR3 pathway. Taken together, these results indicate that the PIKA vaccine for rabies shows potential as a highly efficacious approach, resulting in a significant enhancement of the efficacy of rabies vaccines.

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Earlier global detection of novel SARS-CoV-2 variants gives governments more time to respond. However, few countries can implement timely national surveillance, resulting in gaps in monitoring. The United Kingdom implemented large-scale community and hospital surveillance, but experience suggests it might be faster to detect new variants through testing England arrivals for surveillance. We developed simulations of emergence and importation of novel variants with a range of infection hospitalization rates to the United Kingdom. We compared time taken to detect the variant though testing arrivals at England borders, hospital admissions, and the general community. We found that sampling 10%-50% of arrivals at England borders could confer a speed advantage of 3.5-6 weeks over existing community surveillance and 1.5-5 weeks (depending on infection hospitalization rates) over hospital testing. Directing limited global capacity for surveillance to highly connected ports could speed up global detection of novel SARS-CoV-2 variants.

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Coronavirus Disease 2019 (COVID-19) has been a global public health concern for almost three years, and the transmission characteristics vary among different virus variants. Previous studies have investigated the relationship between air pollutants and COVID-19 infection caused by the original strain of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, it is unclear whether individuals might be more susceptible to COVID-19 due to exposure to air pollutants, with the SARS-CoV-2 mutating faster and faster. This study aimed to explore the relationship between air pollutants and COVID-19 infection caused by three major SARS-CoV-2 strains (the original strain, Delta variant, and Omicron variant) in China. A generalized additive model was applied to investigate the associations of COVID-19 infection with six air pollutants (PM2.5, PM10, SO2, CO, NO2, and O3). A positive correlation might be indicated between air pollutants (PM2.5, PM10, and NO2) and confirmed cases of COVID-19 caused by different SARS-CoV-2 strains. It also suggested that the mutant variants appear to be more closely associated with air pollutants than the original strain. This study could provide valuable insight into control strategies that limit the concentration of air pollutants at lower levels and would better control the spread of COVID-19 even as the virus continues to mutate.

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Background: Life sciences research often turns out to be ineffective. Our aim was to develop a method for mapping repetitive research processes, detecting practice variations, and exploring inefficiencies. Methods: Three samples of R&I projects were used: companion diagnostics of cancer treatments, identification of COVID-19 variants, and COVID-19 vaccine development. Major steps involved: defined starting points, desired end points; measurement of transition times and success rates; exploration of variations, and recommendations for improved efficiency. Results: Over 50% of CDX developments failed to reach market simultaneously with new drugs. There were significant variations among phases of co-development (Bartlett test P<0.001). Length of time in vaccine development also shows variations (P<0.0001). Similarly, subject participation indicates unexplained variations in trials (Phase I: 489.7 (±461.8); Phase II: 857.3 (±450.1); Phase III: 35402 (±18079). Conclusion: Analysis of repetitive research processes can highlight inefficiencies and show ways to improve quality and productivity in life sciences.

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INTRODUCTION: Multiple COVID-19 vaccines have been approved and employed in the fight against the pandemic. However, these vaccines have limited long-term effectiveness against severe cases and a decreased ability to prevent mild disease. AREAS COVERED: This review discusses the relevant factors influencing the efficacy of the vaccines against mild and severe infection, analyzes the possible underlying mechanisms contributing to the different outcomes in terms of vaccine function and disease progression, and proposes improvements for the next generation of vaccines. EXPERT OPINION: The reduced efficacy of the COVID-19 vaccine in the prevention of viral infection is closely related to the emergence of novel SARS-CoV-2 variants and their rapid transmission ability. Fundamentally, the immune responses induced by COVID-19 vaccines cannot effectively halt virus replication in the upper respiratory tract because only a limited number of specific antibodies reach these areas and decrease in concentration over time. However, the established immune response can provide sufficient protection against severe diseases by blocking viral infection of the lower respiratory tract or lung owing to sufficient antibody repertoires and memory responses. Considering this situation, future COVID-19 vaccines should have the potential to replenish the mucosal immune response in the respiratory tract to prevent viral infection.

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PURPOSE: The goal of this study is to examine the chronological development of hospitalized oncology and COVID-19 patients, and compare effects on oncology sub-disciplines for pre-pandemic (2017-19) and pandemic (2020-21) years in the setting of a German university maximum care provider. METHODS: Data were retrospectively retrieved from the hospital performance controlling system for patient collectives with oncological main (nOnco) and COVID-19 secondary diagnosis (nCOVID-19). Data analysis is based on descriptive statistical assessment. RESULTS: The oncology patient collective (nOnco = 27,919) shows a decrease of hospitalized patients for the whole pandemic (- 4% for 2020 and - 2,5% for 2021 to 2019). The number of hospitalized COVID-19 patients increases from first to second pandemic year by + 106.71% (nCOVID-19 = 868). Maximum decline in monthly hospitalized oncology patients amounts to - 19% (May 2020) during the first and - 21% (December 2020) during the second lockdown. Relative monthly hospitalization levels of oncology patients reverted to pre-pandemic levels from February 2021 onwards. CONCLUSION: The results confirm a decline in hospitalized oncology patients for the entire pandemic in the setting of a maximum care provider. Imposed lockdown and contact restrictions, rising COVID-19 case numbers, as well as discovery of new virus variants have a negative impact on hospitalized treated oncological patients.

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OBJECTIVES: Antigen rapid diagnostic tests (RDTs) for SARS coronavirus 2 (SARS-CoV-2) are quick, widely available, and inexpensive. Consequently, RDTs have been established as an alternative and additional diagnostic strategy to quantitative reverse transcription polymerase chain reaction (RT-qPCR). However, reliable clinical and large-scale performance data specific to a SARS-CoV-2 virus variant of concern (VOC) are limited, especially for the Omicron VOC. The aim of this study was to compare RDT performance among different VOCs. METHODS: This single-centre prospective performance assessment compared RDTs from three manufacturers (NADAL, Panbio, MEDsan) with RT-qPCR including deduced standardized viral load from oropharyngeal swabs for detection of SARS-CoV-2 in a clinical point-of-care setting from November 2020 to January 2022. RESULTS: Among 35 479 RDT/RT-qPCR tandems taken from 26 940 individuals, 164 of the 426 SARS-CoV-2 positive samples tested true positive with an RDT corresponding to an RDT sensitivity of 38.50% (95% CI, 34.00-43.20%), with an overall specificity of 99.67% (95% CI, 99.60-99.72%). RDT sensitivity depended on viral load, with decreasing sensitivity accompanied by descending viral load. VOC-dependent sensitivity assessment showed a sensitivity of 42.86% (95% CI, 32.82-53.52%) for the wild-type SARS-CoV-2, 43.42% (95% CI, 32.86-54.61%) for the Alpha VOC, 37.67% (95% CI, 30.22-45.75%) for the Delta VOC, and 33.67% (95% CI, 25.09-43.49%) for the Omicron VOC. Sensitivity in samples with high viral loads of ≥106 SARS-CoV-2 RNA copies per mL was significantly lower in the Omicron VOC (50.00%; 95% CI, 36.12-63.88%) than in the wild-type SARS-CoV-2 (79.31%; 95% CI, 61.61-90.15%; p 0.015). DISCUSSION: RDT sensitivity for detection of the Omicron VOC is reduced in individuals infected with a high viral load, which curtails the effectiveness of RDTs. This aspect furthert: limits the use of RDTs, although RDTs are still an irreplaceable diagnostic tool for rapid, economic point-of-care and extensive SARS-CoV-2 screening.

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The COVID-19 pandemic and the high numbers of infected individuals pose major challenges for public health departments. To overcome these challenges, the health department in Cologne has developed a software called DiKoMa. This software offers the possibility to track contact and index persons, but also provides a digital symptom diary. In this work, the question of whether these can also be used for diagnostic purposes will be investigated. Machine learning makes it possible to identify infections based on early symptom profiles and to distinguish between the predominant dominant variants. Focusing on the occurrence of the symptoms in the first week, a decision tree is trained for the differentiation between contact and index persons and the prevailing dominant variants (Wildtype, Alpha, Delta, and Omicron). The model is evaluated, using sex- and age-stratified cross-validation and validated by symptom profiles of the first 6 days. The variants achieve an AUC-ROC from 0.89 for Omicron and 0.6 for Alpha. No significant differences are observed for the results of the validation set (Alpha 0.63 and Omicron 0.87). The evaluation of symptom combinations using artificial intelligence can determine the individual risk for the presence of a COVID-19 infection, allows assignment to virus variants, and can contribute to the management of epidemics and pandemics on a national and international level. It can help to reduce the number of specific tests in times of low labor capacity and could help to early identify new virus variants.

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Objectives: Real-time data analysis during a pandemic is crucial. This paper aims to introduce a novel interactive tool called Covid-Predictor-Tracker using several sources of COVID-19 data, which allows examining developments over time and across countries. Exemplified here by investigating relative effects of vaccination to non-pharmaceutical interventions on COVID-19 spread. Methods: We combine >100 indicators from the Global COVID-19 Trends and Impact Survey, Johns Hopkins University, Our World in Data, European Centre for Disease Prevention and Control, National Centers for Environmental Information, and Eurostat using random forests, hierarchical clustering, and rank correlation to predict COVID-19 cases. Results: Between 2/2020 and 1/2022, we found among the non-pharmaceutical interventions "mask usage" to have strong effects after the percentage of people vaccinated at least once, followed by country-specific measures such as lock-downs. Countries with similar characteristics share ranks of infection predictors. Gender and age distribution, healthcare expenditures and cultural participation interact with restriction measures. Conclusion: Including time-aware machine learning models in COVID-19 infection dashboards allows to disentangle and rank predictors of COVID-19 cases per country to support policy evaluation. Our open-source tool can be updated daily with continuous data streams, and expanded as the pandemic evolves.

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The SARS-CoV-2 epidemic has been extended by the evolution of more transmissible viral variants. In autumn 2020, the B.1.177 lineage became the dominant variant in England, before being replaced by the B.1.1.7 (Alpha) lineage in late 2020, with the sweep occurring at different times in each region. This period coincided with a large number of non-pharmaceutical interventions (e.g. lockdowns) to control the epidemic, making it difficult to estimate the relative transmissibility of variants. In this paper, we model the spatial spread of these variants in England using a meta-population agent-based model which correctly characterizes the regional variation in cases and distribution of variants. As a test of robustness, we additionally estimated the relative transmissibility of multiple variants using a statistical model based on the renewal equation, which simultaneously estimates the effective reproduction number R. Relative to earlier variants, the transmissibility of B.1.177 is estimated to have increased by 1.14 (1.12-1.16) and that of Alpha by 1.71 (1.65-1.77). The vaccination programme starting in December 2020 is also modelled. Counterfactual simulations demonstrate that the vaccination programme was essential for reopening in March 2021, and that if the January lockdown had started one month earlier, up to 30 k (24 k-38 k) deaths could have been prevented. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

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Our previous paper showed that microRNAs (miRNAs) present within human placental or mesenchymal stem cell-derived extracellular vesicles (EVs) directly interacted with the RNA genome of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), inhibiting viral replication. In this paper, we analyzed whether these miRNAs could exert antiviral activity against other variants of SARS-CoV-2. We downloaded compete SARS-CoV-2 genome data submitted to the National Center for Biotechnology Information for each SARS-CoV-2 variant, aligned the data to the reference SARS-CoV-2 genome sequence, and then confirmed the presence of 3' untranslated region (UTR) mutations. We identified one type of 3' UTR mutation in the Alpha variant, four in the Beta variant, four in the Gamma variant, three in the Delta variant, and none in the Omicron variant. Our findings indicate that 3' UTR mutations rarely occur as persistent mutations. Interestingly, we further confirmed that this phenomenon could suppress virus replication in the same manner as the previously discovered interaction of placental-EV-derived miRNA with 3' UTRs of SARS-CoV-2. Because the 3' UTR of the SARS-CoV-2 RNA genome has almost no mutations, it is expected to be an effective therapeutic target regardless of future variants. Thus, a therapeutic strategy targeting the 3' UTR of SARS-CoV-2 is likely to be extremely valuable, and such an approach is also expected to be applied to all RNA-based virus therapeutics.