A novel coronavirus disease, designated as COVID-19, has become a pandemic worldwide. This study aims to estimate the incubation period and serial interval of COVID-19. We collected contact tracing data in a municipality in Hubei province during a full outbreak period. The date of infection and infector–infectee pairs were inferred from the history of travel in Wuhan or exposed to confirmed cases. The incubation periods and serial intervals were estimated using parametric accelerated failure time models, accounting for interval censoring of the exposures. Our estimated median incubation period of COVID-19 is 5.4 days (bootstrapped 95% confidence interval (CI) 4.8–6.0), and the 2.5th and 97.5th percentiles are 1 and 15 days, respectively; while the estimated serial interval of COVID-19 falls within the range of −4 to 13 days with 95% confidence and has a median of 4.6 days (95% CI 3.7–5.5). Ninety-five per cent of symptomatic cases showed symptoms by 13.7 days (95% CI 12.5–14.9). The incubation periods and serial intervals were not significantly different between male and female, and among age groups. Our results suggest a considerable proportion of secondary transmission occurred prior to symptom onset. And the current practice of 14-day quarantine period in many regions is reasonable.

Background: Factors associated with the incubation period of COVID-19 are not fully known. The aim of this study was to estimate the incubation period of COVID-19 using epidemiological contact tracing data, and to explore whether there were different incubation periods among different age gr1oups. Methods: We collected contact tracing data in a municipality in Hubei province during the full outbreak period of COVID-19. The exposure periods were inferred from the history of travel in Wuhan and/or history of exposure to confirmed cases. The incubation periods were estimated using parametric accelerated failure time models accounting for interval censoring of exposures. Results: The incubation period of COVID-19 follows a Weibull distribution and has a median of 5.8 days with a bootstrap 95% CI: 5.4– 6.7 days. Of the symptomatic cases, 95% showed symptoms by 14.3 days (95% CI: 13.0– 15.7), and 99% showed symptoms by 18.7 days (95% CI: 16.7– 20.9). The incubation periods were not found significantly different between male and female. Elderly cases had significant longer incubation periods than young age cases (HR 1.49 with 95% CI: 1.09– 2.05). The median incubation period was estimated at 4.0 days (95% CI: 3.5– 4.4) for cases aged under 30, 5.8 days (95% CI: 5.6– 6.0) for cases aged between 30 and 59, and 7.7 days (95% CI: 6.9– 8.4) for cases aged greater than or equal to 60. Conclusion: The current practice of a 14-day quarantine period in many regions is reasonable for any age. Older people infected with SARS-CoV2 have longer incubation period than that of younger people. Thus, more attention should be paid to asymptomatic elderly people who had a history of exposure.

Background: Undergraduate medical (UM) students faced the difficulties inherent in medical careers due to the coronavirus (COVID-19) outbreak. Thus, imperative containment measures might affect UM students’ career intentions. Information on the factors that may be associated with these students’ career change intentions is limited. Methods: We conducted a cross-sectional survey in August 2020 to investigate the impact of the COVID-19 pandemic on career intention and the associated factors in UM students. Univariate analyses and logistic regression analysis were performed to identify said factors. Results: A total of 2040 medical students from the Hubei University of Medicine were surveyed. Univariate analyses showed that grade, attitude towards healthcare, and the degree of the COVID-19 pandemic’s impact on the students’ lives were associated with changes in career choice (P<0.05). Logistic regression analysis showed that Grade 2, Grade 5, attitude towards a medical career, and having relatives with a medical background were associated with changes in career choice. The degree of the COVID-19 pandemic’s impact was a common and significant factor associated with career preference, career perspective, and ideal workplace. Conclusions: Changes in career intentions were particularly influenced by grade, attitude towards being a health worker, and the degree of COVID-19’s impact on the participants’ lives. Treating large-scale public health emergencies rationally, setting up correct views of occupation choice, and building reasonable career planning may reduce the loss of medical talent.

Background and Aims: Patients with critical coronavirus disease 2019 (COVID-19) have a mortality rate higher than 50%. The purpose of this study was to establish a model for the prediction of the risk of severe disease and/or death in patients with COVID-19 on admission.Materials and Methods: Patients diagnosed with COVID-19 in four hospitals in China from January 22, 2020 to April 15, 2020 were retrospectively enrolled. The demographic, laboratory, and clinical data of the patients with COVID-19 were collected. The independent risk factors related to the severity of and death due to COVID-19 were identified with a multivariate logistic regression; a nomogram and prediction model were established. The area under the receiver operating characteristic curve (AUROC) and predictive accuracy were used to evaluate the model's effectiveness.Results: In total, 582 patients with COVID-19, including 116 patients with severe disease, were enrolled. Their comorbidities, body temperature, neutrophil-to-lymphocyte ratio (NLR), platelet (PLT) count, and levels of total bilirubin (Tbil), creatinine (Cr), creatine kinase (CK), and albumin (Alb) were independent risk factors for severe disease. A nomogram was generated based on these eight variables with a predictive accuracy of 85.9% and an AUROC of 0.858 (95% CI, 0.823–0.893). Based on the nomogram, the CANPT score was established with cut-off values of 12 and 16. The percentages of patients with severe disease in the groups with CANPT scores <12, ≥12, and <16, and ≥16 were 4.15, 27.43, and 69.64%, respectively. Seventeen patients died. NLR, Cr, CK, and Alb were independent risk factors for mortality, and the CAN score was established to predict mortality. With a cut-off value of 15, the predictive accuracy was 97.4%, and the AUROC was 0.903 (95% CI 0.832, 0.974).Conclusions: The CANPT and CAN scores can predict the risk of severe disease and mortality in COVID-19 patients on admission.

The structure, stability, charge transfer, and chemical bonding properties of palladium-doped magnesium clusters, PdMg n (n = 2-20), are comprehensively researched by CALYPSO software within first-principles DFT computations. It is shown that the cluster structure evolved from a tetrahedral-based structure to a cage-like structure, and interestingly, the single Pd atom always immerses in the Mg n clusters except for PdMg 2 and PdMg 3. Stability studies indicate that PdMg 4 , PdMg 7 , PdMg 10 , and PdMg 15 clusters have relative robust stability and can be identified as 'magic' clusters. Most importantly, chemical bonding studies reveal that Pd-Mg is always non-covalent bond (closed-shell) interaction in all PdMg n (n = 2-20) clusters, and PdMg 6 and PdMg 7 are the critical sizes at which Mg-Mg covalent interactions occur. The geometric structure database of transition metal-doped Mg clusters will be enriched by this study, and it also provides, at least theoretically, new members of cage-like structures for magnesium-based hydrogen storage nanomaterials.

A DFT study of MgH _n ( n = 1–20) clusters shows that a single Mg atom exhibits a magical ability of "odd–even recognition" of the attraction of H atoms, and MgH ₇ and MgH ₁₂ clusters have an impressive storage capacity of 22.69 wt% and 33.47 wt%.