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Since the first suspected case of coronavirus disease-2019 (COVID-19) on December 1st, 2019, in Wuhan, Hubei Province, China, a total of 40,235 confirmed cases and 909 deaths have been reported in China up to February 10, 2020, evoking fear locally and internationally. Here, based on the publicly available epidemiological data for Hubei, China from January 11 to February 10, 2020, we provide estimates of the main epidemiological parameters. In particular, we provide an estimation of the case fatality and case recovery ratios, along with their 90% confidence intervals as the outbreak evolves. On the basis of a Susceptible-Infected-Recovered-Dead (SIDR) model, we provide estimations of the basic reproduction number (R0), and the per day infection mortality and recovery rates. By calibrating the parameters of the SIRD model to the reported data, we also attempt to forecast the evolution of the of the outbreak at the epicenter three weeks ahead, i.e. until February 29. As the number of infected individuals, especially of those with asymptomatic or mild courses, is suspected to be much higher than the official numbers, which can be considered only as a subset of the actual numbers of infected and recovered cases in the total population, we have repeated the calculations under a second scenario that considers twenty times the number of confirmed infected cases and forty times the number of recovered, leaving the number of deaths unchanged. Based on the reported data, the expected value of R0 as computed considering the period from the 11th of January until the 18th of January, using the official counts of confirmed cases was found to be ~4.6, while the one computed under the second scenario was found to be ~3.2. Thus, based on the SIRD simulations, the estimated average value of R0 was found to be ~2.6 based on confirmed cases and ~2 based on the second scenario. Our forecasting flashes a note of caution for the presently unfolding outbreak in China. Based on the official counts for confirmed cases, the simulations suggest that the cumulative number of infected could reach 180,000 (with lower bound of 45,000) by February 29. Regarding the number of deaths, simulations forecast that on the basis of the up to the 10th of February reported data, the death toll might exceed 2,700 (as a lower bound) by February 29. Our analysis further reveals a significant decline of the case fatality ratio from January 26 to which various factors may have contributed, such as the severe control measures taken in Hubei, China (e.g. quarantine and hospitalization of infected individuals), but mainly because of the fact that the actual cumulative numbers of infected and recovered cases in the population most likely are much higher than the reported ones. Thus, in a scenario where we have taken twenty times the confirmed number of infected and forty times the confirmed number of recovered cases, the case fatality ratio is around 0.15% in the total population. Importantly, based on this scenario, simulations suggest a slow down of the outbreak in Hubei at the end of February.

Background The emergence of the novel coronavirus in Wuhan, Hubei Province, China, in December 2019 marked the synchronization of the world to a peculiar clock that is counting infected cases and deaths instead of hours and minutes. The pandemic, highly transmissible severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has indeed caused considerable morbidity and mortality and drastically changed our everyday lives. As we continue to become acquainted with the seventh coronavirus known to infect our species, a number of its characteristics keep surprising us. Among those is the wide spectrum of clinical manifestations of the resulting coronavirus disease 2019 (COVID-19), which ranges from asymptomatic or mildly symptomatic infections to severe pneumonia, respiratory failure, and death. Main body Data, now from patient populations, are beginning to accumulate on human genetic factors that may contribute to the observed diversified disease severity. Therefore, we deemed it prudent to review the associations between specific human genetic variants and clinical disease severity or susceptibility to infection that have been reported in the literature to date (at the time of writing this article in early August 2020 with updates in mid-September). With this work, we hope (i) to assist the fast-paced biomedical research efforts to combat the virus by critically summarizing current knowledge on the potential role of host genetics, and (ii) to help guide current genetics and genomics research towards candidate gene variants that warrant further investigation in larger studies. We found that determinants of differing severity of COVID-19 predominantly include components of the immune response to the virus, while determinants of differing susceptibility to SARS-CoV-2 mostly entail genes related to the initial stages of infection (i.e., binding of the cell surface receptor and entry). Conclusion Elucidating the genetic determinants of COVID-19 severity and susceptibility to SARS-CoV-2 infection would allow for the stratification of individuals according to risk so that those at high risk would be prioritized for immunization, for example, if or when safe and effective vaccines are developed. Our enhanced understanding of the underlying biological mechanisms could also guide personalized therapeutics. Such knowledge is already beginning to provide clues that help explain, at least in part, current epidemiologic observations regarding the typically more severe or benign disease course in older males and children, respectively.

Post-COVID syndrome is increasingly recognized as a new clinical entity in the context of SARS-CoV-2 infection. Symptoms persisting for more than three weeks after the diagnosis of COVID-19 characterize the post-COVID syndrome. Its incidence ranges from 10% to 35%, however, rates as high as 85% have been reported among patients with a history of hospitalization. Currently, there is no consensus on the classification of post-COVID syndrome. We reviewed the published information on post-COVID syndrome, putting emphasis on its pathogenesis. The pathogenesis of post-COVID syndrome is multi-factorial and more than one mechanism may be implicated in several clinical manifestations. Prolonged inflammation has a key role in its pathogenesis and may account for some neurological complications, cognitive dysfunction, and several other symptoms. A multisystem inflammatory syndrome in adults (MIS-A) of all ages has been also described recently, similarly to multisystem inflammatory syndrome in children (MIS-C). The post-infectious inflammatory pathogenetic mechanism of MIS-A is supported by the fact that its diagnosis is established through serology in up to one third of cases. Other pathogenetic mechanisms that are implicated in post-COVID syndrome include immune-mediated vascular dysfunction, thromboembolism, and nervous system dysfunction. Although the current data are indicating that the overwhelming majority of patients with post-COVID syndrome have a good prognosis, registries to actively follow them are needed in order to define the full clinical spectrum and its long-term outcome. A consensus-based classification of post-COVID syndrome is essential to guide clinical, diagnostic, and therapeutic management. Further research is also imperative to elucidate the pathogenesis of post-COVID syndrome.

SummaryToll-like receptors (TLRs) are the best-studied family of pattern-recognition receptors (PRRs), whose task is to rapidly recognize evolutionarily conserved structures on the invading microorganisms. Through binding to these patterns, TLRs trigger a number of proinflammatory and anti-microbial responses, playing a key role in the first line of defence against the pathogens also promoting adaptive immunity responses. Growing amounts of data suggest that single nucleotide polymorphisms (SNPs) on the various human TLR proteins are associated with altered susceptibility to infection. This review summarizes the role of TLRs in innate immunity, their ligands and signalling and focuses on the TLR SNPs which have been linked to infectious disease susceptibility.

Background There is limited information on the association between upper respiratory tract (URT) viral loads, host factors, and disease severity in SARS-CoV-2–infected patients. Methods We studied 1122 patients (mean age, 46 years) diagnosed by polymerase chain reaction (PCR). URT viral load, measured by PCR cycle threshold, was categorized as high, moderate, or low. Results There were 336 (29.9%) patients with comorbidities; 309 patients (27.5%) had high, 316 (28.2%) moderate, and 497 (44.3%) low viral load. In univariate analyses, compared to patients with moderate or low viral load, patients with high viral load were older, more often had comorbidities, developed Symptomatic disease (COVID-19), were intubated, and died. Patients with high viral load had longer stay in intensive care unit and longer intubation compared to patients with low viral load (P values < .05 for all comparisons). Patients with chronic cardiovascular disease, hypertension, chronic pulmonary disease, immunosuppression, obesity, and chronic neurological disease more often had high viral load (P value < .05 for all comparisons). In multivariate analysis high viral load was associated with COVID-19. Level of viral load was not associated with any other outcome. Conclusions URT viral load could be used to identify patients at higher risk for morbidity or severe outcome.

We compared six colistin susceptibility testing (ST) methods on 61 carbapenem-nonsusceptible Klebsiella pneumoniae (n ‫؍‬ 41) and Acinetobacter baumannii (n ‫؍‬ 20) clinical isolates with provisionally elevated colistin MICs by routine ST. Colistin MICs were determined by broth microdilution (BMD), BMD with 0.002% polysorbate 80 (P80) (BMD-P80), agar dilution (AD), Etest, Vitek2, and MIC test strip (MTS). BMD was used as the reference method for comparison. The EUCAST-recommended susceptible and resistant breakpoints of <2 and >2 g/ml, respectively, were applied for both K. pneumoniae and A. baumannii. The proportions of colistin-resistant strains were 95.1, 77, 96.7, 57.4, 65.6, and 98.4% by BMD, BMD-P80, AD, Etest, MTS, and Vitek2, respectively. The Etest and MTS methods produced excessive rates of very major errors (VMEs) (39.3 and 31.1%, respectively), while BMD-P80 produced 18% VMEs, AD produced 3.3% VMEs, and Vitek2 produced no VMEs. Major errors (MEs) were rather limited by all tested methods. These data show that gradient diffusion methods may lead to inappropriate colistin therapy. Clinical laboratories should consider the use of automated systems, such as Vitek2, or dilution methods for colistin ST.T he increasing occurrence of infections due to multidrug-resistant (MDR) Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacteriaceae led to the revival of old and neglected antibiotics that may remain active, such as polymyxins (polymyxin B and colistin) (1, 2). Colistin is increasingly being used as a last-resort treatment option for infections caused by MDR organisms (2, 3), particularly carbapenem-resistant (CR) Gramnegative bacteria (4). However, during the last years, increasing colistin resistance emerged worldwide, especially among Klebsiella pneumoniae and A. baumannii isolates, further limiting treatment options (5-7). In Europe, the evolving colistin resistance is more pronounced in southern countries (notably Greece, Romania, and Italy) (8-10).Rapid and reliable colistin susceptibility testing (ST) is needed in routine clinical laboratories to allow appropriate therapeutic decision-making. Thus far, few studies have assessed the performance of colistin ST methods, displaying controversial results, and thus, the most accurate one is still challenging (11).Disk diffusion, commonly used in many clinical laboratories, yielded high error rates compared to MIC-based methods and is considered unreliable for the detection of colistin resistance (12)(13)(14). Among commercial methods, gradient diffusion strips are convenient tests for determining colistin MICs, but their performance is not well established. Some studies demonstrated very good correlations between the results of Etest (bioMérieux, Marcy l'Etoile, France) and broth microdilution (BMD) or agar dilution (AD) methods for colistin , while other reports questioned the reliability of Etest (18,19). Another gradient diffusion test, MIC test strip (MTS) (Liofilchem SRL, Italy), has not been evaluated for colistin ST to the best of our k...

Background There is limited information on SARS‐CoV‐2 infection clustering within families with children. We aimed to study the transmission dynamics of SARS‐CoV‐2 within families with children in Greece. Methods We studied 23 family clusters of COVID‐19. Infection was diagnosed by RT‐PCR in respiratory specimens. The level of viral load was categorized as high, moderate, or low based on the cycle threshold values. Results There were 109 household members (66 adults and 43 children). The median attack rate per cluster was 60% (range: 33.4%‐100%). An adult member with COVID‐19 was the first case in 21 (91.3%) clusters. Transmission of infection occurred from an adult to a child in 19 clusters and/or from an adult to another adult in 12 clusters. There was no evidence of child‐to‐adult or child‐to‐child transmission. In total 68 household members (62.4%) tested positive. Children were more likely to have an asymptomatic SARS‐CoV‐2 infection compared to adults (40% versus 10.5%, p‐value=0.021). In contrast, adults were more likely to develop a severe clinical course compared to children (8.8% versus 0%, p‐value=0.021). In addition, infected children were significantly more likely to have a low viral load while adults were more likely to have a moderate viral load (40.7% and 18.5% versus 13.8% and 51.7%, respectively; p‐value=0.016). Conclusions While children become infected by SARS‐CoV‐2, they do not appear to transmit infection to others. Furthermore, children more frequently have an asymptomatic or mild course compared to adults. Further studies are needed to elucidate the role of viral load on these findings. This article is protected by copyright. All rights reserved.