Background: The coronavirus disease 2019 (COVID-19) outbreak originating in Wuhan, Hubei province, China, coincided with chunyun, the period of mass migration for the annual Spring Festival. To contain its spread, China adopted unprecedented nationwide interventions on January 23 2020. These policies included large-scale quarantine, strict controls on travel and extensive monitoring of suspected cases. However, it is unknown whether these policies have had an impact on the epidemic. We sought to show how these control measures impacted the containment of the epidemic. Methods: We integrated population migration data before and after January 23 and most updated COVID-19 epidemiological data into the Susceptible-Exposed-Infectious-Removed (SEIR) model to derive the epidemic curve. We also used an artificial intelligence (AI) approach, trained on the 2003 SARS data, to predict the epidemic. Results: We found that the epidemic of China should peak by late February, showing gradual decline by end of April. A five-day delay in implementation would have increased epidemic size in mainland China three-fold. Lifting the Hubei quarantine would lead to a second epidemic peak in Hubei province in mid-March and extend the epidemic to late April, a result corroborated by the machine learning prediction. Conclusions: Our dynamic SEIR model was effective in predicting the COVID-19 epidemic peaks and sizes. The implementation of control measures on January 23 2020 was indispensable in reducing the eventual COVID-19 epidemic size.

Please cite this article as: Liang W-hua, Guan W-jie, Li C-chen, et al. Clinical characteristics and outcomes of hospitalised patients with COVID-19 treated in Hubei (epicenter) and outside Hubei (non-epicenter): A Nationwide Analysis of China. Eur Respir J 2020; in press (https://doi.Abstract BACKGROUND: During the outbreak of coronavirus disease 2019 (COVID-19), consistent and considerable differences in disease severity and mortality rate of patients treated in Hubei province compared to those in other parts of China has been observed. We sought to compare the clinical characteristics and outcomes of patients being treated inside and outside Hubei province, and explore the factors underlying these differences. METHODS:Collaborating with the National Health Commission, we established a retrospective cohort to study hospitalized COVID-19 cases in China. Clinical characteristics, the rate of severe events and deaths, and the time to critical illness (invasive ventilation or intensive care unit admission or death) were compared between patients in and outside of Hubei. The impact of Wuhan-related exposure (a presumed key factor that drove the severe situation in Hubei, as Wuhan is the epicenter as well the administrative center of Hubei province) and the duration between symptom onset and admission on prognosis were also determined. RESULTS:Upon data cut-off (Jan 31st, 2020), 1,590 cases from 575 hospitals in 31 provincial administrative regions were collected (core cohort). The overall rate of severe cases and mortality was 16.0% and 3.2%, respectively. Patients in Hubei (predominantly with Wuhan-related exposure, 597/647, 92.3%) were older (mean: 49.7 vs. 44.9 years), had more cases with comorbidity (32.9% vs. 19.7%), higher symptomatic burden, abnormal radiologic manifestations, and, especially, a longer waiting time between symptom onset and admission (5.7 vs. 4.5 days) compared with patients outside Hubei. Patients in Hubei [severe event rate 23.0% vs. 11.1%, death rate 7.3% vs. 0.3%, hazards ratio (HR) for critical illness 1.59, 95%CI 1.05-2.41] have a poorer prognosis compared with patients outside of Hubei after adjusting for age and comorbidity. However, among patients outside of Hubei, the duration from symptom onset to hospitalization (mean: 4.4 vs. 4.7 days) and prognosis (HR 0.84, were similar between patients with or without Wuhan-related exposure. In the overall population, the waiting time, but neither treated in Hubei nor Wuhan-related exposure, remained an independent prognostic factor (HR 1.05, 1.01-1.08).CONCLUSION: There were more severe cases and poorer outcomes for COVID-19 patients treated in Hubei, which might be attributed to the prolonged duration of symptom onset to hospitalization in the epicenter. Future studies to determine the reason for delaying hospitalization are warranted.

The sudden deterioration of patients with novel coronavirus disease 2019 (COVID-19) into critical illness is of major concern. It is imperative to identify these patients early. We show that a deep learning-based survival model can predict the risk of COVID-19 patients developing critical illness based on clinical characteristics at admission. We develop this model using a cohort of 1590 patients from 575 medical centers, with internal validation performance of concordance index 0.894 We further validate the model on three separate cohorts from Wuhan, Hubei and Guangdong provinces consisting of 1393 patients with concordance indexes of 0.890, 0.852 and 0.967 respectively. This model is used to create an online calculation tool designed for patient triage at admission to identify patients at risk of severe illness, ensuring that patients at greatest risk of severe illness receive appropriate care as early as possible and allow for effective allocation of health resources.

BackgroundThe catalytically active 66-kDa subunit of the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase (RT) consists of DNA polymerase, connection, and ribonuclease H (RNase H) domains. Almost all known RT inhibitor resistance mutations identified to date map to the polymerase domain of the enzyme. However, the connection and RNase H domains are not routinely analysed in clinical samples and none of the genotyping assays available for patient management sequence the entire RT coding region. The British Columbia Centre for Excellence in HIV/AIDS (the Centre) genotypes clinical isolates up to codon 400 in RT, and our retrospective statistical analyses of the Centre's database have identified an N348I mutation in the RT connection domain in treatment-experienced individuals. The objective of this multidisciplinary study was to establish the in vivo relevance of this mutation and its role in drug resistance.Methods and FindingsThe prevalence of N348I in clinical isolates, the time taken for it to emerge under selective drug pressure, and its association with changes in viral load, specific drug treatment, and known drug resistance mutations was analysed from genotypes, viral loads, and treatment histories from the Centre's database. N348I increased in prevalence from below 1% in 368 treatment-naïve individuals to 12.1% in 1,009 treatment-experienced patients (p = 7.7 × 10−12). N348I appeared early in therapy and was highly associated with thymidine analogue mutations (TAMs) M41L and T215Y/F (p < 0.001), the lamivudine resistance mutations M184V/I (p < 0.001), and non-nucleoside RTI (NNRTI) resistance mutations K103N and Y181C/I (p < 0.001). The association with TAMs and NNRTI resistance mutations was consistent with the selection of N348I in patients treated with regimens that included both zidovudine and nevirapine (odds ratio 2.62, 95% confidence interval 1.43–4.81). The appearance of N348I was associated with a significant increase in viral load (p < 0.001), which was as large as the viral load increases observed for any of the TAMs. However, this analysis did not account for the simultaneous selection of other RT or protease inhibitor resistance mutations on viral load. To delineate the role of this mutation in RT inhibitor resistance, N348I was introduced into HIV-1 molecular clones containing different genetic backbones. N348I decreased zidovudine susceptibility 2- to 4-fold in the context of wild-type HIV-1 or when combined with TAMs. N348I also decreased susceptibility to nevirapine (7.4-fold) and efavirenz (2.5-fold) and significantly potentiated resistance to these drugs when combined with K103N. Biochemical analyses of recombinant RT containing N348I provide supporting evidence for the role of this mutation in zidovudine and NNRTI resistance and give some insight into the molecular mechanism of resistance.ConclusionsThis study provides the first in vivo evidence that treatment with RT inhibitors can select a mutation (i.e., N348I) outside the polymerase domain of the HIV-1 RT that confe...

Summary The interaction between respiratory pathogens and their hosts is complex and incompletely understood. This is particularly true when pathogens encounter the mucus layer covering the respiratory tract. The mucus layer provides an essential first host barrier to inhaled pathogens that can prevent pathogen invasion and subsequent infection. Respiratory mucus has numerous functions and interactions, both with the host and with pathogens. This review summarizes the current understanding of respiratory mucus and its interactions with the respiratory pathogens Pseudomonas aeruginosa, respiratory syncytial virus and influenza viruses, with particular focus on influenza virus transmissibility and host-range specificity. Based on current findings we propose that respiratory mucus represents an understudied host-restriction factor for influenza virus.