Data-Driven COVID-19 Vaccine Development for Janssen

The COVID-19 pandemic has spurred extensive vaccine research worldwide. One crucial part of vaccine development is the phase III clinical trial that assesses the vaccine for safety and efficacy in the prevention of COVID-19. In this work, we enumerate the first successful implementation of using machine learning models to accelerate phase III vaccine trials, working with the single-dose Johnson & Johnson vaccine to predictively select trial sites with naturally high incidence rates ("hotspots"). We develop DELPHI, a novel, accurate, policy-driven machine learning model that serves as the basis of our predictions. During the second half of 2020, the DELPHI-driven site selection identified hotspots with more than 90% accuracy, shortened trial duration by six to eight weeks (approximately 33%), and reduced enrollment by 15,000 (approximately 25%). In turn, this accelerated time to market enabled Janssen's vaccine to receive its emergency use authorization and realize its public health impact earlier than expected. Several geographies identified by DELPHI have since been the first areas to report variants of concern (e.g., Omicron in South Africa), and thus DELPHI's choice of these areas also produced early data on how the vaccine responds to new threats. Johnson & Johnson has also implemented a similar approach across its business including supporting trial site selection for other vaccine programs, modeling surgical procedure demand for its Medical Device unit, and providing guidance on return-to-work programs for its 130,000 employees. Continued application of this methodology can help shorten clinical development and change the economics of drug development by reducing the level of risk and cost associated with investing in novel therapies. This will allow Johnson & Johnson and others to enable more effective delivery of medicines to patients.

COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation

To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant biomedical knowledge in scientific literature to understand the disease mechanism and related biological functions. We have developed a novel and comprehensive knowledge discovery framework, COVID-KG to extract fine-grained multimedia knowledge elements (entities and their visual chemical structures, relations and events) from scientific literature. We then exploit the constructed multimedia knowledge graphs (KGs) for question answering and report generation, using drug repurposing as a case study. Our framework also provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence. All of the data, KGs, reports(1), resources, and shared services are publicly available(2).

COVID-19 Claim Radar: A Structured Claim Extraction and Tracking System

The COVID-19 pandemic has received extensive media coverage, with a vast variety of claims made about different aspects of the virus. In order to track these claims, we present COVID-19 Claim Radar(1), a system that automatically extracts claims relating to COVID-19 in news articles. We provide a comprehensive structured view of such claims, with rich attributes (such as claimers and their affiliations) and associated knowledge elements (such as events, relations and entities). Further, we use this knowledge to identify inter-claim connections such as equivalent, supporting, or refuting relations, with shared structural evidence like claimers, similar centroid events and arguments. In order to consolidate claim structures at the corpus-level, we leverage Wikidata(2) as the hub to merge coreferential knowledge elements, and apply machine translation to aggregate claims from news articles in multiple languages. The system provides users with a comprehensive exposure to COVID-19 related claims, their associated knowledge elements, and related connections to other claims. The system is publicly available on GitHub(3) and DockerHub(4), with complete documentation(5).

Chemical Compositions in Winter PM2.5 in Changzhou of the Yangtze River Delta Region, China: Characteristics and Atmospheric Responses Along With the Different Pollution Levels

Changzhou, a typical industrial city located in the center of the Yangtze River Delta (YRD) region, has experienced serious air pollution in winter. However, Changzhou still receives less attention compared with other big cities in YRD. In this study, a four-month PM2.5 sampling campaign was conducted in Changzhou, China from 1 November 2019, to 1 February 2020. The period covers the entire wintertime and includes first week of the Level 1 response stage of the lockdown period due to the outbreak of COVID-19. The mean PM2.5 concentrations were 67.9 +/- 29.0 mu gm(-3), ranging from 17.4 to 157.4 mu gm(-3). Secondary inorganic ions were the most abundant species, accounting for 37 and 50% during the low and high PM2.5 pollution periods, respectively. Nitrogen oxidation ratio (NOR) during the high PM concentration level period was twice the low PM concentration period whereas sulfur oxidation ratio (SOR) showed a less significant increase. This represents that nitrate formation is potentially the predominant factor controlling the occurrence of PM pollution. The analysis of NOR, SOR as functions of relative humidity (RH) and ozone (O-3) concentrations suggest that the sulfate formation was mainly through aqueous-phase reaction, while nitrate formation was driven by both photochemistry and heterogeneous reaction. And, excess ammonium could promote the formation of nitrate during the high PM period, indicating that ammonia gas played a critical role in regulating nitrate. Furthermore, a special period-Chinese New Year overlapping first week of COVID-19 lockdown period, offered a precious window to study the impact of human activity pattern changes on air pollution variation. During the special period, the average PM2.5 mean concentration was 60.4 mu gm(-3), which did not show in a low value as expected. The declines in nitrogen oxide (NOx) emissions led to rapid increases in O-3 and atmospheric oxidizing capacity, as well as sulfate formation. The chemical profiles and compositions obtained during different periods provide a scientific basis for establishing efficient atmospheric governance policies in the future.

Negative pressure drainage relieves respiratory discomfort for patients with medical face masks under ocular surgeries during COVID-19

AIM: To report our precaution practices for ocular surgeries under local anesthesia during COVID-19 outbreak and evaluate the respiration situation among the patients with medical face masks under ocular surgeries. METHODS: Sixty Chinese patients needed eye surgery treatment were recruited and given medical face masks as one of the COVID-19 precaution practices during eye surgery with local anesthesia. Oxygen supplementation and negative pressure drainage were applied to relieve the potential respiratory discomfort, and the respiratory comfort score was evaluated. RESULTS: Patients with medical face masks experienced mild to moderate respiratory discomfort with an overall mean score of 2.34±0.73. Supplementation of oxygen together with negative pressure drainage relieved this discomfort (overall mean score of 0.15±0.75;P<0.001). There is no gender and operation time difference on respiratory discomfort or discomfort relieve. Failure in negative pressure drainage led to severe respiratory discomfort. CONCLUSION: Negative pressure drainage could maintain the respiratory circulation in patients with medical face mask under eye surgery with local anesthesia. Application of medical face masks in patients under surgeries is recommended to protect the medical practitioners during the operations within COVID-19 outbreak. Copyright 2021 by the IJO Press.

A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave.

Disease modelling has had considerable policy impact during the ongoing COVID-19 pandemic, and it is increasingly acknowledged that combining multiple models can improve the reliability of outputs. Here we report insights from ten weeks of collaborative short-term forecasting of COVID-19 in Germany and Poland (12 October-19 December 2020). The study period covers the onset of the second wave in both countries, with tightening non-pharmaceutical interventions (NPIs) and subsequently a decay (Poland) or plateau and renewed increase (Germany) in reported cases. Thirteen independent teams provided probabilistic real-time forecasts of COVID-19 cases and deaths. These were reported for lead times of one to four weeks, with evaluation focused on one- and two-week horizons, which are less affected by changing NPIs. Heterogeneity between forecasts was considerable both in terms of point predictions and forecast spread. Ensemble forecasts showed good relative performance, in particular in terms of coverage, but did not clearly dominate single-model predictions. The study was preregistered and will be followed up in future phases of the pandemic.

Standardizing Components and Rotating Workers Using GT-Based Algorithm-A Case Study

In both manufacturing and remanufacturing systems, exploiting bulk buying and avoiding delivery delays due to material shortages are crucial. One method that aids in these processes is component standardization. Additionally, company managers seek to reduce labor costs and mitigate the risk of sudden worker resignation or absence due to, for example, reasons associated with the COVID-19 pandemic. The aforementioned problems could be solved using the sorting algorithm proposed in this study. The concept of the proposed algorithm is based on group technology. One numerical example and two case studies are presented to demonstrate the utility of the proposed algorithm. The first example suggested that the performance of the algorithm proposed in this study is superior to another one in the literature. The second one demonstrated that the algorithm in this work achieves component standardization by reducing an initial number of 12 components down to 6. The final case study provides an effective means of grouping workers with similar operational abilities and suggests how to assign new tasks to other skilled workers if a worker resigns suddenly or cannot attend work due to pandemic prevention measures.