The role of mild and asymptomatic infections on COVID-19 vaccines performance: A modeling study

Introduction: Different COVID-19 vaccine efficacies are reported, with remarkable effectiveness against severe disease. The so called sterilizing immunity, occurring when vaccinated individuals cannot transmit the virus, is still being evaluated. It is also unclear to what extent people with no symptoms or mild infection transmit the disease, and estimating their contribution to outbreaks is challenging. Objective: With an uneven roll out of vaccination, the purpose of this study is to investigate the role of mild and asymptomatic infections on COVID-19 vaccine performance as vaccine efficacy and vaccine coverage vary. Methods: We use an epidemiological SHAR (Susceptible-Hospitalized-Asymptomatic-Recovered) model framework to evaluate the effects of vaccination in different epidemiological scenarios of coverage and efficacy. Two vaccination models, the vaccine V1 protecting against severe disease, and the vaccine V2, protecting against infection as well as severe disease, are compared to evaluate the reduction of overall infections and hospitalizations. Results: Vaccine performance is driven by the ability of asymptomatic or mild disease cases transmitting the virus. Vaccines protecting against severe disease but failing to block transmission might not be able to reduce significantly the severe disease burden during the initial stage of a vaccination roll out programme, with an eventual increase on the number of overall infections in a population. Conclusion: The different COVID-19 vaccines currently in use have features placing them closer to one or the other of these two extreme cases, V1 and V2, and insights on the importance of asymptomatic infection in a vaccinated population are of a major importance for the future planning of vaccination programmes. Our results give insights on how to best combine the use of the available COVID-19 vaccines, optimizing the reduction of hospitalizations

Critical fluctuations in epidemic models explain COVID‑19 post‑lockdown dynamics

As the COVID-19 pandemic progressed, research on mathematical modeling became imperative and very influential to understand the epidemiological dynamics of disease spreading. The momentary reproduction ratio r(t) of an epidemic is used as a public health guiding tool to evaluate the course of the epidemic, with the evolution of r(t) being the reasoning behind tightening and relaxing control measures over time. Here we investigate critical fluctuations around the epidemiological threshold, resembling new waves, even when the community disease transmission rate β is not signifcantly changing. Without loss of generality, we use simple models that can be treated analytically and results are applied to more complex models describing COVID-19 epidemics. Our analysis shows that, rather than the supercritical regime (infectivity larger than a critical value, β>βc) leading to new exponential growth of infection, the subcritical regime (infectivity smaller than a critical value, β<βc) with small import is able to explain the dynamic behaviour of COVID-19 spreading after a lockdown lifting, with r(t) ≈ 1 hovering around its threshold value.BMTF “Mathematical Modeling Applied to Health” Project European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 79249

Modelling COVID 19 in the Basque Country from introduction to control measure response

In March 2020, a multidisciplinary task force (so‐called Basque Modelling Task Force, BMTF) was created to assist the Basque health managers and Government during the COVID‐19 responses. BMTF is a modelling team, working on different approaches, including stochastic processes, statistical methods and artificial intelligence. Here we describe the efforts and challenges to develop a flexible modeling framework able to describe the dynamics observed for the tested positive cases, including the modelling development steps. The results obtained by a new stochastic SHARUCD model framework are presented. Our models differentiate mild and asymptomatic from severe infections prone to be hospitalized and were able to predict the course of the epidemic, providing important projections on the national health system’s necessities during the increased population demand on hospital admissions. Short and longer‐term predictions were tested with good results adjusted to the available epidemiological data. We have shown that the partial lockdown measures were effective and enough to slow down disease transmission in the Basque Country. The growth rate was calculated from the model and from the data and the implications for the reproduction ratio r are shown. The analysis of the growth rates from the data led to improved model versions describing after the exponential phase also the new information obtained during the phase of response to the control measures. This framework is now being used to monitor disease transmission while the country lockdown was gradually lifted, with insights to specific programs for a general policy of “social distancing” and home quarantining.Marie Skłodowska-Curie Grant Agreement No. 79249

Can consumers learn to ask three questions to improve shared decision making? A feasibility study of the ASK (AskShareKnow) Patient–Clinician Communication Model® intervention in a primary health-care setting

Funded by Informed Medical Decisions Foundation. Grant Number: #0175-1 National Health and Medical Research Council Public Health Training Fellowship. Grant Number: 568962Peer reviewedPublisher PD

Electronic tuning and uniform superconductivity in CeCoIn5

We report a globally reversible effect of electronic tuning on the magnetic phase diagram in CeCoIn_{5} driven by electron (Pt and Sn) and hole (Cd, Hg) doping. Consequently, we are able to extract the superconducting pair breaking component for hole and electron dopants with pressure and co-doping studies, respectively. We find that these nominally non-magnetic dopants have a remarkably weak pair breaking effect for a d-wave superconductor. The pair breaking is weaker for hole dopants, which induce magnetic moments, than for electron dopants. Furthermore, both Pt and Sn doping have a similar effect on superconductivity despite being on different dopant sites, arguing against the notion that superconductivity lives predominantly in the CeIn_{3} planes of these materials. In addition, we shed qualitative understanding on the doping dependence with density functional theory calculations.Comment: Accepted for publication in Phys. Rev. Lett. (October 1, 2012

Second record of Pseudimares aphrodite H. Asp?ck et U. Asp?ck, 2009 (Neuroptera, Myrmeleontidae)

Some adults of Pseudimares aphrodite H. Asp?ck et U. Asp?ck, 2009 were observed and photographed while attracted by light in Southern Morocco, in August 2009 and 2011. Only the typus of this species, a male, was known previously from South Morocco too. Moreover the genus Pseudimares Kimmins, 1933 is perhaps the most enigmatic taxon among Neuroptera Myrmeleontidae. Its second species Pseudimares iris Kimmins, 1933 from Southern Iran is known also only in the type series, a male and a female. What little information we know about Pseudimares is reporte

Dissecting a complex chemical stress: chemogenomic profiling of plant hydrolysates.

The efficient production of biofuels from cellulosic feedstocks will require the efficient fermentation of the sugars in hydrolyzed plant material. Unfortunately, plant hydrolysates also contain many compounds that inhibit microbial growth and fermentation. We used DNA-barcoded mutant libraries to identify genes that are important for hydrolysate tolerance in both Zymomonas mobilis (44 genes) and Saccharomyces cerevisiae (99 genes). Overexpression of a Z. mobilis tolerance gene of unknown function (ZMO1875) improved its specific ethanol productivity 2.4-fold in the presence of miscanthus hydrolysate. However, a mixture of 37 hydrolysate-derived inhibitors was not sufficient to explain the fitness profile of plant hydrolysate. To deconstruct the fitness profile of hydrolysate, we profiled the 37 inhibitors against a library of Z. mobilis mutants and we modeled fitness in hydrolysate as a mixture of fitness in its components. By examining outliers in this model, we identified methylglyoxal as a previously unknown component of hydrolysate. Our work provides a general strategy to dissect how microbes respond to a complex chemical stress and should enable further engineering of hydrolysate tolerance

Characterization of New Proteomic Biomarker Candidates in Mucopolysaccharidosis Type IVA

Mucopolysaccharidosis type IVA (MPS IVA) is a lysosomal storage disease caused by mutations in the N-acetylgalactosamine-6-sulfatase (GALNS) gene. Skeletal dysplasia and the related clinical features of MPS IVA are caused by disruption of the cartilage and its extracellular matrix, leading to a growth imbalance. Enzyme replacement therapy (ERT) with recombinant human GALNS has yielded positive results in activity of daily living and endurance tests. However, no data have demonstrated improvements in bone lesions and bone grow thin MPS IVA after ERT, and there is no correlation between therapeutic efficacy and urine levels of keratan sulfate, which accumulates in MPS IVA patients. Using qualitative and quantitative proteomics approaches, we analyzed leukocyte samples from healthy controls (n = 6) and from untreated (n = 5) and ERT-treated (n = 8, sampled before and after treatment) MPS IVA patients to identify potential biomarkers of disease. Out of 690 proteins identified in leukocytes, we selected a group of proteins that were dysregulated in MPS IVA patients with ERT. From these, we identified four potential protein biomarkers, all of which may influence bone and cartilage metabolism: lactotransferrin, coronin 1A, neutral alpha-glucosidase AB, and vitronectin. Further studies of cartilage and bone alterations in MPS IVA will be required to verify the validity of these proteins as potential biomarkers of MPS IVA