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.

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.