Flattening the Curve after the Initial Outbreak of Coronavirus Disease 2019: A Data-Driven Modeling Analysis for the Omicron Pandemic in China.

China is relaxing COVID-19 measures from the "dynamic zero tolerance" (DZT) level. The "flatten-the-curve" (FTC) strategy, which decreases and maintains the low rate of infection to avoid overwhelming the healthcare system by adopting relaxed nonpharmaceutical interventions (NPIs) after the outbreak, has been perceived as the most appropriate and effective method in preventing the spread of the Omicron variant. Hence, we established an improved data-driven model of Omicron transmission based on the age-structured stochastic compartmental susceptible-latent-infectious-removed-susceptible model constructed by Cai to deduce the overall prevention effect throughout China. At the current level of immunity without the application of any NPIs, more than 1.27 billion (including asymptomatic individuals) were infected within 90 days. Moreover, the Omicron outbreak would result in 1.49 million deaths within 180 days. The application of FTC could decrease the number of deaths by 36.91% within 360 days. The strict implementation of FTC policy combined with completed vaccination and drug use, which only resulted in 0.19 million deaths in an age-stratified model, will help end the pandemic within about 240 days. The pandemic would be successfully controlled within a shorter period of time without a high fatality rate; therefore, the FTC policy could be strictly implemented through enhancement of immunity and drug use.

Rapid and Accurate Detection of SARS-CoV-2 Using an iPad-Controlled, High-Throughput, Portable, and Multiplex Hive-Chip Platform (HiCube).

Rapid and accurate detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is one of the most effective measures to control the coronavirus disease 2019 (COVID-19) pandemic. However, there is still lack of an ideal detection platform capable of high sample throughput, portability, and multiplicity. Herein, by combining Hive-Chip (capillary microarray) and reverse transcriptional loop-mediated isothermal amplification (RT-LAMP), we developed an iPad-controlled, high-throughput (48 samples at one run), portable (smaller than a backpack), multiplex (monitoring 8 gene fragments in one reaction), and real-time detection platform for SARS-CoV-2 detection. This platform is composed of a portable Hive-Chip device (HiCube; 32.7 × 29.7 × 20 cm, 5 kg), custom-designed software, and optimized Hive-Chips. RT-LAMP primers targeting seven SARS-CoV-2 genes (S, E, M, N, ORF1ab, ORF3a, and ORF7a) and one positive control (human RNase P) were designed and prefixed in the Hive-Chip. On-chip RT-LAMP showed that the limit of detection (LOD) of SARS-CoV-2 synthetic RNAs is 1 copy/µL, and there is no cross-reaction among different target genes. The platform was validated by 100 clinical samples of SARS-CoV-2, and the results were highly consistent with those of the traditional real-time PCR assay. In addition, on-chip detection of 6 other respiratory pathogens showed no cross-reactivity. Overall, our platform has great potential for fast, accurate, and on-site detection of SARS-CoV-2.

Reply to Chan, et al.

We appreciate the comments from Chan et al. for our study, and have carefully responded to the comments of Chan et al. and are very grateful for their praise of our research. We agree that smoking might be a risk factor of the severity of COVID-19 as mentioned by Chan et al., but in our study, smoking was not so robust compared with our conclusion. Also, we strongly agreed with the opinion of Chan, et al. that COVID-19 patients with diabetes or other chronic diseases might worsen the situation of the disease. But these factors were out of the scope of our study and we had published other research on this topic related to diabetes. Because of the limited sample size and original medical records, our study could not cover many factors suggested as Chan, et al. But we wish our study will be a useful and meaningful pilot study for the future studies. This article is protected by copyright. All rights reserved.

The clinical and bioinformatics analysis for the role of antihypertension drugs on mortality among patients with hypertension hospitalized with COVID-19.

Comorbidities such as hypertension could exacerbate symptoms of coronaviral disease 2019 (COVID)-19 infection. Patients with hypertension may receive both anti-COVID-19 and antihypertension therapies when infected with COVID-19. However, it is not clear how different classes of anti-hypertension drugs impact the outcome of COVID-19 treatment. Herein, we explore the association between the inpatient use of different classes of anti-hypertension drugs and mortality among patients with hypertension hospitalized with COVID-19. We totally collected data from 278 patients with hypertension diagnosed with COVID-19 admitted to hospitals in Wuhan from February 1 to April 1, 2020. A retrospective study was conducted and single-cell RNA-sequencing (RNA-Seq) analysis of treatment-related genes was performed. The results showed that Angiotensin II receptor blocker (ARB) and calcium channel blocker (CCB) drugs significantly increased the survival rate but the use of angiotensin-converting enzyme inhibitor/ß-block/diuretic drugs did not affect the mortality caused by COVID-19. Based on the analysis of four public data sets of single-cell RNA-Seq on COVID-19 patients, we concluded that JUN, LST1 genes may play a role in the effect of ARB on COVID-19-related mortality, whereas CALM1 gene may contribute to the effect of CCB on COVID-19-related mortality. Our results provide guidance on the selection of antihypertension drugs for hypertensive patients infected with COVID-19.

Non-linear link between temperature difference and COVID-19: Excluding the effect of population density.

INTRODUCTION: The spatiotemporal patterns of Corona Virus Disease 2019 (COVID-19) is detected in the United States, which shows temperature difference (TD) with cumulative hysteresis effect significantly changes the daily new confirmed cases after eliminating the interference of population density. METHODOLOGY: The nonlinear feature of updated cases is captured through Generalized Additive Mixed Model (GAMM) with threshold points; Exposure-response curve suggests that daily confirmed cases is changed at the different stages of TD according to the threshold points of piecewise function, which traces out the rule of updated cases under different meteorological condition. RESULTS: Our results show that the confirmed cases decreased by 0.390% (95% CI: -0.478 ~ -0.302) for increasing each one degree of TD if TD is less than 11.5°C; It will increase by 0.302% (95% CI: 0.215 ~ 0.388) for every 1°C increase in the TD (lag0-4) at the interval [11.5, 16]; Meanwhile the number of newly confirmed COVID-19 cases will increase by 0.321% (95% CI: 0.142 ~ 0.499) for every 1°C increase in the TD (lag0-4) when the TD (lag0-4) is over 16°C, and the most fluctuation occurred on Sunday. The results of the sensitivity analysis confirmed our model robust. CONCLUSIONS: In US, this interval effect of TD reminds us that it is urgent to control the spread and infection of COVID-19 when TD becomes greater in autumn and the ongoing winter.

Clinical characteristics of 14 COVID-19 deaths in Tianmen, China: a single-center retrospective study.

BACKGROUND: The treatment of critically ill patients with COVID-19 who were hospitalized in Wuhan has been reported. However, the clinical characteristics of patients who died of COVID-19 in regions with relatively scarce healthcare resources remain unknown. METHODS: In this retrospective study, a total of 14 patients who were admitted from January 18 to February 11, 2020 and died of COVID-19 were evaluated. The epidemiological, symptomatic, laboratory, radiological and treatment records were reviewed and analyzed. RESULTS: The mean age of the 14 patients was 56.7 (SD 15.3) years, and 8 (57.1%) were older than 50 years. Eight (57.1%) were men, and 11 (78.6%) had one or more high risk factors. The most common chronic diseases among these patients were cardiovascular disease (7, 50.0%), hypertension (6, 42.9%), and chronic kidney disease (5, 35.7%). General symptoms included cough (12, 85.7%), fever (11, 78.6%), and dyspnea (10, 71.4%). The median duration from the onset of symptoms to death was 11 (IQR 6.5-19.5) days, and the median duration from admission to death was 4.5 (1.0-11.8) days. Patients who died within 4.5 days had more severe pulmonary lesions, significantly reduced lymphocytes and elevated C-reactive protein (CRP). Most patients had organ dysfunction, including 13 (92.9%) with acute respiratory distress syndrome (ARDS), 4 (28.6%) with cardiac injury, 3 (21.4%) with acute kidney injury, and 3 (21.4%) with liver dysfunction. CONCLUSIONS: Elderly SARS-CoV-2-infected patients with comorbidities, especially those with ARDS and severe chest CT findings on admission, are at increased risk of death and deserve special attention and quality medical treatment.