Privacy-Preserving-Enabled Lightweight COVID-19 Simulation Model for Mobile Intelligent Application

In order to control the first wave of COVID-19 pandemic in 2020, many models have shown effectiveness in predicting the spread of new coronary pneumonia and the different interventions. However, few models can collect large amounts of high-quality real-time data faster under the premise of protecting privacy, considering the impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant and the mass vaccination program as a new intervention. Therefore, we developed a mobile intelligent application that can collect a large amount of real-time data while protecting privacy and conducted a feasibility study by defining a new COVID-19 mathematical model SEMCVRD. By simulating different intervention measures, the prediction model of the mobile intelligent application used in this article simulates the epidemic situation in the U.K. as an example. The findings are as below: the optimal intervention strategy is to suppress the intervention at [Formula Omitted] (intervention intensity: the average number of contacts per person per day) before the end of March 2021, then gradually release the intervention intensity at a rate of [Formula Omitted], and finally release the intensity to [Formula Omitted] in June 2021. The COVID-19 pandemic will end at the end of June 2021, when the total number of deaths will reach 128772. This strategy will be able to balance the tradeoff between loss of life and economic loss. Compared with the official statistics released by the U.K. government on May 31, 2021, our model can accurately predict the relative error rate of the total number of cases is less than 6.9%, and the relative error rate of the total number of deaths is less than 1%. Furthermore, the model is also suitable for collecting data from countries/regions around the world.

Mixed-Weight Neural Bagging for Detecting m6A Modifications in SARS-CoV-2 RNA Sequencing.

OBJECTIVE: The m6A modification is the most common ribonucleic acid (RNA) modification, playing a role in prompting the virus's gene mutation and protein structure changes in the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Nanopore single-molecule direct RNA sequencing (DRS) provides data support for RNA modification detection, which can preserve the potential m6A signature compared to second-generation sequencing. However, due to insufficient DRS data, there is a lack of methods to find m6A RNA modifications in DRS. Our purpose is to identify m6A modifications in DRS precisely. METHODS: We present a methodology for identifying m6A modifications that incorporated mapping and extracted features from DRS data. To detect m6A modifications, we introduce an ensemble method called mixed-weight neural bagging (MWNB), trained with 5-base RNA synthetic DRS containing modified and unmodified m6A. RESULTS: Our MWNB model achieved the highest classification accuracy of 97.85% and AUC of 0.9968. Additionally, we applied the MWNB model to the COVID-19 dataset; the experiment results reveal a strong association with biomedical experiments. CONCLUSION: Our strategy enables the prediction of m6A modifications using DRS data and completes the identification of m6A modifications on the SARS-CoV-2. SIGNIFICANCE: The Corona Virus Disease 2019 (COVID-19) outbreak has significantly influence, caused by the SARS-CoV-2. An RNA modification called m6A is connected with viral infections. The appearance of m6A modifications related to several essential proteins affects proteins' structure and function. Therefore, finding the location and number of m6A RNA modifications is crucial for subsequent analysis of the protein expression profile.

COVID-19 Surveiller: toward a robust and effective pandemic surveillance system basedon social media mining.

The outbreak of the novel coronavirus, COVID-19, has become one of the most severe pandemics in human history. In this paper, we propose to leverage social media users as social sensors to simultaneously predict the pandemic trends and suggest potential risk factors for public health experts to understand spread situations and recommend proper interventions. More precisely, we develop novel deep learning models to recognize important entities and their relations over time, thereby establishing dynamic heterogeneous graphs to describe the observations of social media users. A dynamic graph neural network model can then forecast the trends (e.g. newly diagnosed cases and death rates) and identify high-risk events from social media. Based on the proposed computational method, we also develop a web-based system for domain experts without any computer science background to easily interact with. We conduct extensive experiments on large-scale datasets of COVID-19 related tweets provided by Twitter, which show that our method can precisely predict the new cases and death rates. We also demonstrate the robustness of our web-based pandemic surveillance system and its ability to retrieve essential knowledge and derive accurate predictions across a variety of circumstances. Our system is also available at http://scaiweb.cs.ucla.edu/covidsurveiller/. This article is part of the theme issue 'Data science approachs to infectious disease surveillance'.

Targeting conserved N-glycosylation blocks SARS-CoV-2 variant infection in vitro.

BACKGROUND: Despite clinical success with anti-spike vaccines, the effectiveness of neutralizing antibodies and vaccines has been compromised by rapidly spreading SARS-CoV-2 variants. Viruses can hijack the glycosylation machinery of host cells to shield themselves from the host's immune response and attenuate antibody efficiency. However, it remains unclear if targeting glycosylation on viral spike protein can impair infectivity of SARS-CoV-2 and its variants. METHODS: We adopted flow cytometry, ELISA, and BioLayer interferometry approaches to assess binding of glycosylated or deglycosylated spike with ACE2. Viral entry was determined by luciferase, immunoblotting, and immunofluorescence assays. Genome-wide association study (GWAS) revealed a significant relationship between STT3A and COVID-19 severity. NF-κB/STT3A-regulated N-glycosylation was investigated by gene knockdown, chromatin immunoprecipitation, and promoter assay. We developed an antibody-drug conjugate (ADC) that couples non-neutralization anti-spike antibody with NGI-1 (4G10-ADC) to specifically target SARS-CoV-2-infected cells. FINDINGS: The receptor binding domain and three distinct SARS-CoV-2 surface N-glycosylation sites among 57,311 spike proteins retrieved from the NCBI-Virus-database are highly evolutionarily conserved (99.67%) and are involved in ACE2 interaction. STT3A is a key glycosyltransferase catalyzing spike glycosylation and is positively correlated with COVID-19 severity. We found that inhibiting STT3A using N-linked glycosylation inhibitor-1 (NGI-1) impaired SARS-CoV-2 infectivity and that of its variants [Alpha (B.1.1.7) and Beta (B.1.351)]. Most importantly, 4G10-ADC enters SARS-CoV-2-infected cells and NGI-1 is subsequently released to deglycosylate spike protein, thereby reinforcing the neutralizing abilities of antibodies, vaccines, or convalescent sera and reducing SARS-CoV-2 variant infectivity. INTERPRETATION: Our results indicate that targeting evolutionarily-conserved STT3A-mediated glycosylation via an ADC can exert profound impacts on SARS-CoV-2 variant infectivity. Thus, we have identified a novel deglycosylation method suitable for eradicating SARS-CoV-2 variant infection in vitro. FUNDING: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

Chinese expert recommendations on management of hepatocellular carcinoma during COVID-19 pandemic: a nationwide multicenter survey.

BACKGROUND: This study aimed to investigate the work status of clinicians in China and their management strategy alteration for patients with hepatocellular carcinoma (HCC) during the COVID-19 pandemic. METHODS: A nationwide online questionnaire survey was conducted in 42 class-A tertiary hospitals across China. Experienced clinicians of HCC-related specialties responded with their work status and management suggestions for HCC patients during the pandemic. RESULTS: 716 doctors responded effectively with a response rate of 60.1%, and 664 were included in the final analysis. Overall, 51.4% (341/664) of clinicians reported more than a 60% reduction of the regular workload and surgeons declared the highest proportion of workload reduction. 92.5% (614/664) of the respondents have been using online medical consultation to substitute for the "face-to-face" visits. Adaptive adjustment for the treatment strategy for HCC was made, including the recommendations of noninvasive and minimally invasive treatments such as transcatheter arterial chemoembolization for early and intermediate stage. Targeted therapy has been the mainstay for advanced stage and also as a bridge therapy for resectable HCC. DISCUSSION: During the COVID-19 pandemic, online medical consultation is recommended to avoid social contact. Targeted therapy as a bridge therapy is recommended for resectable HCC considering the possibility of delayed surgery.

Reservoir hosts prediction for COVID-19 by hybrid transfer learning model.

The recent outbreak of COVID-19 has infected millions of people around the world, which is leading to the global emergency. In the event of the virus outbreak, it is crucial to get the carriers of the virus timely and precisely, then the animal origins can be isolated for further infection. Traditional identifications rely on fields and laboratory researches that lag the responses to emerging epidemic prevention. With the development of machine learning, the efficiency of predicting the viral hosts has been demonstrated by recent researchers. However, the problems of the limited annotated virus data and imbalanced hosts information restrict these approaches to obtain a better result. To assure the high reliability of predicting the animal origins on COVID-19, we extend transfer learning and ensemble learning to present a hybrid transfer learning model. When predicting the hosts of newly discovered virus, our model provides a novel solution to utilize the related virus domain as auxiliary to help building a robust model for target virus domain. The simulation results on several UCI benchmarks and viral genome datasets demonstrate that our model outperforms the general classical methods under the condition of limited target training sets and class-imbalance problems. By setting the coronavirus as target domain and other related virus as source domain, the feasibility of our approach is evaluated. Finally, we show the animal reservoirs prediction of the COVID-19 for further analysing.

The effect of multiple interventions to balance healthcare demand for controlling COVID-19 outbreaks: a modelling study.

For controlling recent COVID-19 outbreaks around the world, many countries have implemented suppression and mitigation interventions. This work aims to conduct a feasibility study for accessing the effect of multiple interventions to control the COVID-19 breakouts in the UK and other European countries, accounting for balance of healthcare demand. The model is to infer the impact of mitigation, suppression and multiple rolling interventions for controlling COVID-19 outbreaks in the UK, with two features considered: direct link between exposed and recovered population, and practical healthcare demand by separation of infections. We combined the calibrated model with COVID-19 data in London and non-London regions in the UK during February and April 2020. Our finding suggests that rolling intervention is an optimal strategy to effectively control COVID-19 outbreaks in the UK for balancing healthcare demand and morality ratio. It is better to implement regional based interventions with varied intensities and maintenance periods. We suggest an intervention strategy named as "Besieged and rolling interventions" to the UK that take a consistent suppression in London for 100 days and 3 weeks rolling intervention in other regions. This strategy would reduce the overall infections and deaths of COVID-19 outbreaks, and balance healthcare demand in the UK.

Feasibility study of mitigation and suppression strategies for controlling COVID-19 outbreaks in London and Wuhan.

Recent outbreaks of coronavirus disease 2019 (COVID-19) has led a global pandemic cross the world. Most countries took two main interventions: suppression like immediate lockdown cities at epicenter or mitigation that slows down but not stopping epidemic for reducing peak healthcare demand. Both strategies have their apparent merits and limitations; it becomes extremely hard to conduct one intervention as the most feasible way to all countries. Targeting at this problem, this paper conducted a feasibility study by defining a mathematical model named SEMCR, it extended traditional SEIR (Susceptible-Exposed-Infectious-Recovered) model by adding two key features: a direct connection between Exposed and Recovered populations, and separating infections into mild and critical cases. It defined parameters to classify two stages of COVID-19 control: active contain by isolation of cases and contacts, passive contain by suppression or mitigation. The model was fitted and evaluated with public dataset containing daily number of confirmed active cases including Wuhan and London during January 2020 and March 2020. The simulated results showed that 1) Immediate suppression taken in Wuhan significantly reduced the total exposed and infectious populations, but it has to be consistently maintained at least 90 days (by the middle of April 2020). Without taking this intervention, we predict the number of infections would have been 73 folders higher by the middle of April 2020. Its success requires efficient government initiatives and effective collaborative governance for mobilizing of corporate resources to provide essential goods. This mode may be not suitable to other countries without efficient collaborative governance and sufficient health resources. 2) In London, it is possible to take a hybrid intervention of suppression and mitigation for every 2 or 3 weeks over a longer period to balance the total infections and economic loss. While the total infectious populations in this scenario would be possibly 2 times than the one taking suppression, economic loss and recovery of London would be less affected. 3) Both in Wuhan and London cases, one important issue of fitting practical data was that there were a portion (probably 62.9% in Wuhan) of self-recovered populations that were asymptomatic or mild symptomatic. This finding has been recently confirmed by other studies that the seroprevalence in Wuhan varied between 3.2% and 3.8% in different sub-regions. It highlights that the epidemic is far from coming to an end by means of herd immunity. Early release of intervention intensity potentially increased a risk of the second outbreak.