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The haemagglutination test (HAT)-field protocol described here is an optimization of the recently published HAT, for the detection of antibodies directed against the receptor binding domain (RBD) of the SARS-Cov-2 virus. HAT and HAT-field are both based on haemagglutination triggered by a single reagent, the IH4-RBD recombinant protein. A sample of IH4-RBD sufficient for several thousand tests or a plasmid encoding IH4-RBD can be obtained from the authors of our first paper. Using titration of IH4-RBD, HAT-field now allows a quantitative assessment of antibody levels in a single step, using a few microliters of whole blood, such as can be obtained by finger prick, and requires only very simple disposable equipment. Because it is based on a single soluble reagent, the test can be adapted very simply and rapidly to detect antibodies against variants of the SARS-CoV-2, or conceivably against different pathogens. HAT-field appears well suited to provide quantitative assessments of the serological protection of populations as well as individuals, and given its very low cost, the stability of the IH4-RBD reagent in the adapted buffer and the simplicity of the procedure, could be deployed pretty much anywhere, including in the poorest countries and the most remote corners of the globe. [ABSTRACT FROM AUTHOR]

Mucosal vaccine for sublingual route was prepared with recombinant SARS-CoV-2 spike protein receptor binding domain (RBD) antigen and poly(I:C) adjuvant components. The efficacy of this sublingual vaccine was examined using Cynomolgus macaques. Nine of the macaque monkeys were divided into three groups of three animals: control [just 400 µg poly(I:C) per head], low dose [30 µg RBD and 400 µg poly(I:C) per head], and high dose [150 µg RBD and 400 µg poly(I:C) per head], respectively. N-acetylcysteine (NAC), a mild reducing agent losing mucin barrier, was used to enhance vaccine delivery to mucosal immune cells. RBD-specific IgA antibody secreted in pituita was detected in two of three monkeys of the high dose group and one of three animals of the low dose group. RBD-specific IgG and/or IgA antibodies in plasma were also detected in these monkeys. These indicated that the sublingual vaccine stimulated mucosal immune response to produce antigen-specific secretory IgA antibodies in pituita and/or saliva. This sublingual vaccine also affected systemic immune response to produce IgG (IgA) in plasma. Little RBD-specific IgE was detected in plasma, suggesting no allergic antigenicity of this sublingual vaccine. Thus, SARS-CoV-2 sublingual vaccine consisting of poly(I:C) adjuvant showed reasonable efficacy in a non-human primate model. [ABSTRACT FROM AUTHOR]

This article deals with the spread of infectious diseases from a physics perspective. It considers a population as a network of nodes representing the population members, linked by network edges representing the (social) contacts of the individual population members. Infections spread along these edges from one node (member) to another. This article presents a novel, modified version of the SIR compartmental model, able to account for typical network effects and percolation phenomena. The model is successfully tested against the results of simulations based on Monte-Carlo methods. Expressions for the (basic) reproduction numbers in terms of the model parameters are presented, and justify some mild criticisms on the widely spread interpretation of reproduction numbers as being the number of secondary infections due to a single active infection. Throughout the article, special emphasis is laid on understanding, and on the interpretation of phenomena in terms of concepts borrowed from condensed-matter and statistical physics, which reveals some interesting analogies. Percolation effects are of particular interest in this respect and they are the subject of a detailed investigation. The concept of herd immunity (its definition and nature) is intensively dealt with as well, also in the context of large-scale vaccination campaigns and waning immunity. This article elucidates how the onset of herd-immunity can be considered as a second-order phase transition in which percolation effects play a crucial role, thus corroborating, in a more pictorial/intuitive way, earlier viewpoints on this matter. An exact criterium for the most relevant form of herd-immunity to occur can be derived in terms of the model parameters. The analyses presented in this article provide insight in how various measures to prevent an epidemic spread of an infection work, how they can be optimized and what potentially deceptive issues have to be considered when such measures are either implemented or scaled down. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) as a suite of technologies can complement systematic review and meta-analysis studies and answer questions that cannot be typically answered using traditional review protocols and reporting methods. The purpose of this protocol is to introduce a new protocol to complete systematic review and meta-analysis studies.   In this work, systematic review, meta-analysis, and meta-analysis network based on selected AI technique, and for P < 0.05 are followed, with a view to responding to questions and challenges that the global population is facing in light of the COVID-19 pandemic.   Finally, it is expected that conducting reviews by following the proposed protocol can provide suitable answers to some of the research questions raised due to COVID-19. [ABSTRACT FROM AUTHOR]

With the rapid spread of COVID-19, there is an urgent need for a framework to accurately predict COVID-19 transmission. Recent epidemiological studies have found that a prominent feature of COVID-19 is its ability to be transmitted before symptoms occur, which is generally not the case for seasonal influenza and severe acute respiratory syndrome. Several COVID-19 predictive epidemiological models have been proposed; however, they share a common drawback – they are unable to capture the unique asymptomatic nature of COVID-19 transmission. Here, we propose vector autoregression (VAR) as an epidemiological county-level prediction model that captures this unique aspect of COVID-19 transmission by introducing newly infected cases in other counties as lagged explanatory variables. Using the number of new COVID-19 cases in seven New York State counties, we predicted new COVID-19 cases in the counties over the next 4 weeks. We then compared our prediction results with those of 11 other state-of-the-art prediction models proposed by leading research institutes and academic groups. The results showed that VAR prediction is superior to other epidemiological prediction models in terms of the root mean square error of prediction. Thus, we strongly recommend the simple VAR model as a framework to accurately predict COVID-19 transmission. [ABSTRACT FROM AUTHOR]

Background With the results of the largest randomized controlled trial (RECOVERY) and the most extensive retrospective cohort study on coronavirus disease 2019 (COVID-19) recently published, we performed a meta-analysis on the association of aspirin with mortality of COVID-19. We aimed to investigate the role of aspirin in COVID-19 hospitalizations. Materials and Methods We searched PubMed, EMBASE and Cochrane databases for studies from 1 January 2020 until 20 July 2022, that compared aspirin versus non-aspirin use in hospitalized COVID-19 patients. We excluded case reports, review articles and studies on non-hospitalized COVID-19 infections. We used the inverse variance method and random effects model to pool the individual studies. Results Ten observational studies and one randomized controlled trial met the criteria for inclusion. There were 136 695 total patients, of which 27 168 were in the aspirin group and 109 527 were in the non-aspirin group. Aspirin use was associated with a 14% decrease in all-cause mortality compared with non-aspirin use in patients hospitalized with COVID-19 [relative risk (RR) 0.86, confidence interval (95% CI) 0.76–0.97; P  = 0.002; I 2 =64%]. Among subgroups of studies reporting in-hospital mortality in COVID-19 hospitalizations, aspirin use was associated with a 16% decrease in in-hospital mortality compared with non-aspirin use (RR 0.84, 95% CI 0.71–0.99; P  = 0.007; I 2 =64%). Conclusion Our study shows that aspirin decreases in-hospital mortality in patients hospitalized with COVID-19. Further studies are needed to assess which COVID-19 patient populations benefit most, such as patients on aspirin for primary versus secondary prevention of atherosclerotic disease. In addition, significant bleeding also needs to be considered when assessing the risk–benefit of aspirin use. [ABSTRACT FROM AUTHOR]