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.

A trial of arbidol hydrochloride in adults with COVID-19.

BACKGROUND: To date, there is no effective medicine to treat coronavirus disease 2019 (COVID-19), and the antiviral efficacy of arbidol in the treatment for COVID-19 remained equivocal and controversial. The purpose of this study was to evaluate the efficacy and safety of arbidol tablets in the treatment of COVID-19. METHODS: This was a prospective, open-label, controlled and multicenter investigator-initiated trial involving adult patients with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Patients were stratified 1:2 to either standard-of-care (SOC) or SOC plus arbidol tablets (oral administration of 200 mg per time, three times a day for 14 days). The primary endpoint was negative conversion of SARS-CoV-2 within the first week. The rates and 95% confidential intervals were calculated for each variable. RESULTS: A total of 99 patients with laboratory-confirmed SARS-CoV-2 infection were enrolled; 66 were assigned to the SOC plus arbidol tablets group, and 33 to the SOC group. The negative conversion rate of SARS-CoV-2 within the first week in patients receiving arbidol tablets was significantly higher than that of the SOC group (70.3% [45/64] vs. 42.4% [14/33]; difference of conversion rate 27.9%; 95% confidence interval [CI], 7.7%-48.1%; P  = 0.008). Compared to those in the SOC group, patients receiving arbidol tablets had a shorter duration of clinical recovery (median 7.0 days vs. 12.0 days; hazard ratio [HR]: 1.877, 95% CI: 1.151-3.060, P = 0.006), symptom of fever (median 3.0 days vs. 12.0 days; HR: 18.990, 95% CI: 5.350-67.410, P < 0.001), as well as hospitalization (median 12.5 days vs. 20.0 days; P < 0.001). Moreover, the addition of arbidol tablets to SOC led to more rapid normalization of declined blood lymphocytes (median 10.0 days vs. 14.5 days; P > 0.05). The most common adverse event in the arbidol tablets group was the elevation of transaminase (5/200, 2.5%), and no one withdrew from the study due to adverse events or disease progression. CONCLUSIONS: SOC plus arbidol tablets significantly increase the negative conversion rate of SARS-CoV-2 within the first week anas, accelerate the recovery of COVID-19 patients. During the treatment with arbidol tablets, we find no significant serious adverse events. TRIAL REGISTRATION: Chinese Clinical Trial Registry, NCT04260594, www.clinicaltrials.gov/ct2/show/NCT04260594?term=NCT04260594&draw=2&rank=1.

Advances in nanoparticle‐based lateral flow immunoassay for point‐of‐care testing

As a representative technology for point‐of‐care testing (POCT), lateral flow immunoassay (LFIA) has been broadly used to detect analytes in many fields. However, its clinical application is severely limited by the unsatisfactory sensitivity, which makes it difficult to obtain accurate results when detecting biomarkers of trace levels, especially in complex matrices. Nanoparticles have been introduced into LFIA for years and become an indispensable part, acting not only as carriers that load and enrich biomolecules, such as antibodies and dyes, but also a miniature platform applied for creative design and construction of nanoprobes. Due to the unique properties at the nanoscale, including the mimetic enzyme activity, the characteristic plasma resonance spectrum and so on, nanomaterials exhibit great potential in the development of novel LFIA and high‐sensitivity detection.

Self-Ensembling Co-Training Framework for Semi-Supervised COVID-19 CT Segmentation.

The coronavirus disease 2019 (COVID-19) has become a severe worldwide health emergency and is spreading at a rapid rate. Segmentation of COVID lesions from computed tomography (CT) scans is of great importance for supervising disease progression and further clinical treatment. As labeling COVID-19 CT scans is labor-intensive and time-consuming, it is essential to develop a segmentation method based on limited labeled data to conduct this task. In this paper, we propose a self-ensembled co-training framework, which is trained by limited labeled data and large-scale unlabeled data, to automatically extract COVID lesions from CT scans. Specifically, to enrich the diversity of unsupervised information, we build a co-training framework consisting of two collaborative models, in which the two models teach each other during training by using their respective predicted pseudo-labels of unlabeled data. Moreover, to alleviate the adverse impacts of noisy pseudo-labels for each model, we propose a self-ensembling strategy to perform consistency regularization for the up-to-date predictions of unlabeled data, in which the predictions of unlabeled data are gradually ensembled via moving average at the end of every training epoch. We evaluate our framework on a COVID-19 dataset containing 103 CT scans. Experimental results show that our proposed method achieves better performance in the case of only 4 labeled CT scans compared to the state-of-the-art semi-supervised segmentation networks.

Pandemic analysis of infection and death correlated with genomic open reading frame 10 mutation in severe acute respiratory syndrome coronavirus 2 victims.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues the pandemic spread of the coronavirus disease 2019 (COVID-19), over 60 million people confirmed infected and at least 1.8 million dead. One of the most known features of this RNA virus is its easiness to be mutated. In late 2020, almost no region of this SARS-CoV-2 genome can be found completely conserved within the original Wuhan coronavirus. Any information of the SARS-CoV-2 variants emerged through as time being will be evaluated for diagnosis, treatment, and prevention of COVID-19. METHODS: We extracted more than two million data of SARS-CoV-2 infected patients from the open COVID-19 dashboard. The sequences of the 38-amino acid putative open reading frame 10 (Orf10) protein within infected patients were gathered output through from National Center for Biotechnology Information and the mutation rates in each position were analyzed and presented in each month of 2020. The mutation rates of A8 and V30 within Orf10 are displayed in selected counties: United States, India, German, and Japan. RESULTS: The numbers of COVID-19 patients are correlated to the death numbers, but not with the death rates (stable and <3%). The amino acid positions locating at A8(F/G/L), I13, and V30(L) within the Orf10 sequence stay the highest mutation rate; N5, N25, and N36 rank at the lowest one. A8F expressed highly dominant in Japan (over 80%) and German (around 40%) coming to the end of 2020, but no significant finding in other countries. CONCLUSION: The results demonstrate via mutation analysis of Orf10 can be further combined with advanced tools such as molecular simulation, artificial intelligence, and biosensors that can practically revealed for protein interactions and thus to imply the authentic Orf10 function of SARS-CoV-2 in the future.

From women for women: The role of social media in online nonprofit activities during Wuhan lockdown

The article examines the role of social media in mitigating information asymmetry and coordination problems during COVID-19 epidemic crisis. We use "Sisters-Fight-Epidemic" online volunteering project during the outbreak of COVID-19 in Wuhan, China, as a case to demonstrate how social media plays a role as a mechanism in linking multiple stakeholders and shaping their actions during the epidemic response. We show that social media facilitates the self-organizing processes of volunteers and develops the emergency information networks, therefore enabling a relatively efficient relief responses to the needs of epidemic victims particularly female medical workers. This article also identifies spontaneous online volunteering project as a new form of nonprofit organization and as a new emergent response group that can leverage the strengths of social media in disaster responses to enable effective coordination, initiate advocacy, and improve transparency of relief efforts.

A smartphone-based online tool for prehospital self-triage of COVID-19.

The COVID-19 epidemic has swept across China and spread to other countries. The rapid spreading of COVID-19 and panic combined with the lack of a hierarchical medical system in China have resulted in a huge number of hospital visiting which are overwhelming local medical system and increasing the incidence of cross infection. To meliorate this situation, we adopted the management concept of the system of Tiered Diagnosis and Treatment and developed an online tool for self-triage based on the mostly used multi-purpose smartphone app Wechat in China. This online tool helps people perform self-triage so that they can decide whether to quarantine at home or visit hospital. This tool further provides instructions for home quarantine and help patients make an appointment online if hospital visiting suggested. This smartphone application can reduce the burden on hospitals without losing the truly COVID-19 patients and protect people from the danger of cross infection.

Modelling and Predicting of Minimal CFR During Initial COVID-19 Epidemics from 214 Nations (preprint)

Background: The basic integrating responding capacity (IRC) against the COVID-19 epidemics is essential fundament for national responding strategy and can’t be evaluated quantitatively worldwide. To explore the applicable parameter labeling IRC from national daily CFRs and predicting method of minimal CFR during initial COVID-19 epidemic.Methods: Daily case fatality rates of COVID-19 cases since the first COVID-19 death in 214 nations were explored and found that similar falling zones marked with two turning points within the fitting curves occurred for many nations. The turning points could be quantified with parameters for the day duration (T1, T2 and ΔT) and for the three-day moving arithmetic average CFRs (CFR1, CFR2, and ΔCFR) under wave theory for 71 nations after screening. Two prediction models of CFR2 were established with multiple linear regressions (M1) and multi-order curve regressions (M2).Findings: Among the 92 nations, the 3DMA CFRs curves were arising continuously for 21 nations. Three types of falling zones could be classified with strong, moderate and weak IRC in the other 71 nations, the range of CFR2 was 0·0682~32·5804 percent. Only the minimal CFR showed significant correlations with 9 independent national indicators in 65 nations with CFRs under 7 percent. Model M1 showed that Log(POPU), B1K, and HHS made significant positive contributions, and Log(GDP), A65, DGDP, P1K, N1K and BMI (21·8 ~ 29·5) made negative contributions to the minimal CFR against COVID-19 epidemics for most nations. CFR2 was predicted well with model M1 for 57 nations and with model M2 for 59 nations for internal evaluation.Interpretation: The national minimal CFR could be predicted with models successfully for most nations based on some national, which provided the essential information in advance to establish suitable national responding strategies against COVID-19 epidemics worldwide.Funding Statement: This study was supported by National Natural Science Foundation of China (21976169) and Beijing Natural Science Foundation Project (8182055). Declaration of Interests: The authors declare no competing interests.Ethics Approval Statement: Data collection and analysis to be part of a continuing public health outbreak were thus considered exempt from institutional review board approval.

Infection Control against COVID-19 in Departments of Radiology.

The COVID-19 epidemic, which is caused by the novel coronavirus SARS-CoV-2, has spread rapidly to become a world-wide pandemic. Chest radiography and chest CT are frequently used to support the diagnosis of COVID-19 infection. However, multiple cases of COVID-19 transmission in radiology department have been reported. Here we summarize the lessons we learned and provide suggestions to improve the infection control and prevention practices of healthcare workers in departments of radiology.

CT Imaging and Differential Diagnosis of COVID-19.

Since the beginning of 2020, coronavirus disease 2019 (COVID-19) has spread throughout China. This study explains the findings from lung computed tomography images of some patients with COVID-19 treated in this medical institution and discusses the difference between COVID-19 and other lung diseases.

Corona Virus International Public Health Emergencies: Implications for Radiology Management.

The outbreak of 2019 novel coronavirus (2019-nCoV) pneumonia was reported in Wuhan, Hubei Province, China in December 2019 and has spread internationally. This article discusses how radiology departments can most effectively respond to this public health emergency.