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The COVID-19 pandemic has accelerated innovations for supporting learning and teaching online. However, online learning also means a reduction of opportunities in direct communication between teachers and students. Given the inevitable diversity in learning progress and achievements for individual online learners, it is difficult for teachers to give personalized guidance to a large number of students. The personalized guidance may cover many aspects, including recommending tailored exercises to a specific student according to the student's knowledge gaps on a subject. In this paper, we propose a personalized exercise recommendation method named causal deep learning (CDL) based on the combination of causal inference and deep learning. Deep learning is used to train and generate initial feature representations for the students and the exercises, and intervention algorithms based on causal inference are then applied to further tune these feature representations. Afterwards, deep learning is again used to predict individual students' score ratings on exercises, from which the Top-N ranked exercises are recommended to similar students who likely need enhancing of skills and understanding of the subject areas indicated by the chosen exercises. Experiments of CDL and four baseline methods on two real-world datasets demonstrate that CDL is superior to the existing methods in terms of capturing students' knowledge gaps in learning and more accurately recommending appropriate exercises to individual students to help bridge their knowledge gaps. © 2022 The Author(s).
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Inferring individual human mobility at a given time is not only beneficial for personalized location-based services, but also crucial for trajectory tracking of the confirmed cases in the context of the COVID-19 pandemic. However, individual generated trajectory data using mobile Apps is characterized by implicit feedback, which means only a few individual-location interactions can be observed. Existing studies based on such sparse trajectory data are not sufficient to infer individual's missing mobility in his/her historical trajectory and further predict individual's future mobility given a specific time. To address this concern, in this paper, we propose a temporal-context-aware approach that incorporates multiple factors to model the time sensitive individual-location interactions in a bottom-up way. Based on the idea of feature fusion, the driving effect of heterogeneous information such as time, space, category and sentiment on individual's mobile behavior is gradually strengthened, so that the temporal context when a check-in occurs can be accurately depicted. We leverage Bayesian Personalized Ranking (BPR) to optimize the model, where a novel negative sampling method is employed to alleviate data sparseness. Based on three real-world datasets, we evaluate the proposed approach with regard to two different tasks, namely, missing mobility inference and future mobility prediction at a given time. The empirical results encouragingly demonstrate that our approach outperforms multiple baselines in terms of two evaluation metrics, i.e., accuracy and average percentile rank. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Purpose: Because of the globalization of the knowledge economy, intellectual property (IP) rights have become an important tool for maintaining market leadership and controlling emerging market shares. This paper aims to identify the IP risks that China's strategic emerging industries face in the process of knowledge management in the post-COVID-19 pandemic era seeking to minimize these risks and reduce unnecessary losses. Design/methodology/approach: Based on an analysis of the current situation in China's strategic emerging industries, this paper qualitatively organizes the various types of IP risks faced by China's strategic emerging industries in their development with knowledge creation, knowledge transfer and knowledge application. This paper further analyzes the factors triggering the risks and proposes endogenous and exogenous IP risk-prevention strategies for China's strategic emerging industries from the perspective of knowledge management. Findings: Adopting a knowledge management perspective, this paper identifies three main intellectual property risks in the knowledge creation, transfer, application processes of knowledge management for China's emerging industries, including infringement risks related to independent innovation, leakage risks related to international cooperation and ownership risks related to technology transfer. Research limitations/implications: Based on the entire technology–product–application process and from a knowledge management perspective, the IP risks in the development of China's strategic emerging industries are comprehensively elaborated in this paper, providing a theoretical basis for avoiding IP risks that is also widely applicable to other knowledge-intensive industries. Originality/value: This paper explicates the IP risk faced by China's strategic emerging industries in each step of the knowledge management process and suggestions from knowledge management strategy, tools and implementation support mechanism holds promise for business, industry and government IP risk prevention are elaborated specially to promote the development of China's strategic emerging industries. On the one hand, this paper expanded the research on knowledge management by exploring the relationship between knowledge management and intellectual property rights variables. On the other hand, the findings have practical significance for the stable, long term and efficient development of strategic emerging industries in China as well as other knowledge-intensive industries. Empirical analyses on this subject are suggested for future studies. © 2022, Emerald Publishing Limited.
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With the spread of COVID-19 around the world, the education industry faces enormous challenges. Some colleges and universities have launched online teaching. Comprehensive online teaching and student health checkups help students complete the set teaching content and return to school as soon as possible. With the development of big data, combined with the epidemic risk we are facing, the rational use of big data and the internet for innovative online education has become a mainstream teaching method. Colleges and universities are not yet familiar with the development prospects and future of online education. Through the research of this paper, we can understand the combination of online education and the development of big data and promote its application in colleges and universities. Not only have innovative online education platforms such as MOOC and DingTalk been widely used, but innovative online education methods such as virtual classrooms also have been created. Based on the current epidemic background, this paper analyzes the development of online education, introduces the impact of the combination of online education and big data, and introduces innovative online education technologies and their effects. It helps online education under the influence of the new coronavirus epidemic, operating big data technology to analyze the current prospects and development of online education, showing the combination of big data technology and online education through the analysis of big data technology, and ending with more expectations on other aspects of the use of big data, which affects the online education industry as well as other industries. Finally, we summarize the combination of big data and innovative online education since the emergence of COVID-19 and introduce the concepts and methods of combining online education and big data technology in detail. The online education platform also makes a reasonable introduction. The thesis can be used to understand the problems and challenges faced by innovative online education in the context of the new coronavirus epidemic and look forward to the future on this basis. © 2022 The Authors
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The ongoing COVID-19 pandemic has been a daunting challenge for healthcare systems worldwide. The World Health Organization has recommended various measures to reduce or limit the spread of the virus, one of which includes the use of face masks. This increase in their demand has provided a unique opportunity to improve the technology by offering, in addition to their inherent protection, therapeutic benefits. One such benefit involves inhaled nitric oxide (iNO) therapy. iNO has proven to be a beneficial therapeutic in patients with acute, hypoxemic respiratory failure and lung injury. Specifically, its potential application stems from its ability to rapidly increase oxygen partial pressure in arterial blood. However, iNO treatments generally require pressurized gas cylinders which are coupled with high costs and lack of portability. A face mask which can deliver therapeutic NO is developed using humidity-triggered NO-releasing nanoparticles. This platform can deliver a low dose of 2.1–2.5 ppm NO for 90 min in a sustained manner. Moreover, it can be stored for extended periods of time and can be easily transported due to its light weight. This NO mask has the potential to alleviate the strain that affects financially limited healthcare systems in developing regions. © 2023 The Authors. Advanced Materials Technologies published by Wiley-VCH GmbH.
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Inferring individual human mobility at a given time is not only beneficial for personalized location-based services but also crucial for tracking trajectory of the confirmed cases in the COVID-19 pandemic. However, individual-generated trajectory data from mobile Apps are characterized by implicit feedback, which means only a few individual-location interactions can be observed. Existing studies based on such sparse trajectory data are not sufficient to infer an individual's missing mobility in his/her historical trajectory and further predict an individual's future mobility at a given time under a unified framework. To address this concern, in this article, we propose a temporal-context-aware framework that incorporates multiple factors to model the time-sensitive individual-location interactions in a bottom-up way. Based on the idea of feature fusion, the driving effect of heterogeneous information on an individual's mobility is gradually strengthened, so that the temporal-spatial context when a check-in occurs can be accurately perceived. We leverage Bayesian personalized ranking (BPR) to optimize the model, where a novel negative sampling method is employed to alleviate data sparseness. Based on three real-world datasets, we evaluate the proposed approach with regard to two different tasks, namely, missing mobility inference and future mobility prediction at a given time. Experimental results encouragingly demonstrate that our approach outperforms multiple baselines in terms of two evaluation metrics. Furthermore, the predictability of individual mobility within different time windows is also revealed.
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To contain the spread of the virus at ports, many countries have implemented quarantine policies for vessels from abroad during COVID-19. In response, vessels chose to skip the port to save time or undergo a 14-day quarantine to ensure critical supplies, both of which significantly affected the performance of the port network. However, due to the combined effect of many factors, data analysis techniques can hardly identify the impact of quarantine policies on the outcomes. Therefore, to enable both networkwide performance assessment and detailed evaluation for individual vessels and ports under such an unprecedented policy, a microscopic simulation model for the global port network (GPN) is desired. The proposed simulation method is based on real-world vessel movement data from Automatic Identification Systems (AIS) combined with a port database. It is found that the effect of the quarantine policy on a particular port consists of two parts, i.e. the direct impact caused by vessels' port skipping and the indirect impact caused by network interaction, which is further determined by the location of, and the policy implemented by the port. Furthermore, the ability of the global port network to maintain its performance under different levels of pandemic situations and different rates for vessels to skip the ports requiring quarantine is investigated. Interestingly, in most cases, a moderate port skipping rate (mostly between 20% and 50%) could help improve network performance. The results and presented simulation method can assist policymakers in coping with COVID-19 and potential global catastrophes.
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Due to the COVID-19 global pandemic, there are more needs for remote patient care especially in rehabilitation requiring direct contact. However, traditional Chinese rehabilitation technologies, such as gua sha, often need to be implemented by well-trained professionals. To automate and professionalize gua sha, it is necessary to record the nursing and rehabilitation process and reproduce the process in developing smart gua sha equipment. This paper proposes a new signal processing and sensor fusion method for developing a piece of smart gua sha equipment. A novel stabilized numerical integration method based on information fusion and detrended fluctuation analysis (SNIF-DFA) is performed to obtain the velocity and displacement information during gua sha operation. The experimental results show that the proposed method outperforms the traditional numerical integration method with respect to information accuracy and realizes accurate position calculations. This is of great significance in developing robots or automated machines that reproduce the nursing and rehabilitation operations of medical professionals. IEEE
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Filtration to optimal exactness is mandatory since the options inundate the online world. Knowledge graph embedding is extraordinarily contributing to the recommendations, but the existing knowledge graph (KG)-based recommendation methods only exploit the correlations among the preferences and stand-alone entities, without bonding the cocurricular features and tendencies of the context. Additionally, the integration of the location-based current data of coronavirus disease 2019 (COVID-19) into the KG is necessary for the recommendation of region-aware precautionary alerts to the concerned people-an essential application of the current and future Internet of Medical Things. Therefore, in this article, we propose a novel deep collaborative alert recommendation (DCA) approach to cope with the situation. Particularly, DCA collects current online data about COVID-19, purifies, and transforms them to the KG. Furthermore, it independently encapsulates the cocurricular features and tendencies of the context in the embedding space and encodes them to the independent hidden factors via a graph neural network. The bi-end hidden factors are computed via matrix factorization to infer the potential connections. Moreover, a relevance estimator and a cross transistor are configured to enhance the generalization capability of the model. Experiments on two real-world datasets are performed to evaluate the effectiveness of DCA. Results and analysis show that the proposed approach has outperformed the baseline methods with fine improvements in providing the required recommendations.
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Purpose: Because of the globalization of the knowledge economy, intellectual property (IP) rights have become an important tool for maintaining market leadership and controlling emerging market shares. This paper aims to identify the IP risks that China’s strategic emerging industries face in the process of knowledge management in the post-COVID-19 pandemic era seeking to minimize these risks and reduce unnecessary losses. Design/methodology/approach: Based on an analysis of the current situation in China’s strategic emerging industries, this paper qualitatively organizes the various types of IP risks faced by China’s strategic emerging industries in their development with knowledge creation, knowledge transfer and knowledge application. This paper further analyzes the factors triggering the risks and proposes endogenous and exogenous IP risk-prevention strategies for China’s strategic emerging industries from the perspective of knowledge management. Findings: Adopting a knowledge management perspective, this paper identifies three main intellectual property risks in the knowledge creation, transfer, application processes of knowledge management for China’s emerging industries, including infringement risks related to independent innovation, leakage risks related to international cooperation and ownership risks related to technology transfer. Research limitations/implications: Based on the entire technology–product–application process and from a knowledge management perspective, the IP risks in the development of China’s strategic emerging industries are comprehensively elaborated in this paper, providing a theoretical basis for avoiding IP risks that is also widely applicable to other knowledge-intensive industries. Originality/value: This paper explicates the IP risk faced by China’s strategic emerging industries in each step of the knowledge management process and suggestions from knowledge management strategy, tools and implementation support mechanism holds promise for business, industry and government IP risk prevention are elaborated specially to promote the development of China’s strategic emerging industries. On the one hand, this paper expanded the research on knowledge management by exploring the relationship between knowledge management and intellectual property rights variables. On the other hand, the findings have practical significance for the stable, long term and efficient development of strategic emerging industries in China as well as other knowledge-intensive industries. Empirical analyses on this subject are suggested for future studies. © 2022, Emerald Publishing Limited.
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On May 13, 2022, World Health Organization(WHO) Position Paper on Influenza Vaccine (2022 edition) was published. This position paper updates information on influenza epidemiology, high risk population, the impact of immunization on disease, influenza vaccines and effectiveness and safety, and propose WHO's position and recommendation that all countries should consider implementing seasonal influenza vaccine immunization programmes to prepare for an influenza pandemic. In addition, it proposes that the influenza surveillance platform can be integrated with the surveillance of other respiratory viruses, such as SARS-CoV-2 and Respiratory Syncytial Virus. This position paper has some implications for the prevention and control of influenza and other respiratory infectious diseases in China: (1) Optimize influenza vaccine policies to facilitate the implementation of immunization services; (2) Influenza prevention and control should from the perspective of Population Medicine focus on the individual and community to integrate with "Promotion, Prevention, Diagnosis, Control, Treatment, Rehabilitation"; (3) Incorporate prevention and control of other respiratory infectious diseases such as influenza, COVID-19, respiratory syncytial virus and adenovirus, and intelligently monitor by integrating multi-channel data to achieve the goal of co-prevention and control of multiple diseases.
Subject(s)
COVID-19 , Influenza Vaccines , Influenza, Human , Humans , Influenza, Human/epidemiology , Influenza, Human/prevention & control , SARS-CoV-2 , World Health OrganizationABSTRACT
In nature, the interaction between pathogens and their hosts is only one of a handful of interaction relationships between species, including parasitism, predation, competition, symbiosis, commensalism, and among others. From a non-anthropocentric view, parasitism has relatively fewer essential differences from the other relationships;but from an anthropocentric view, parasitism and predation against humans and their well-beings and belongings are frequently related to heinous diseases. Specifically, treating (managing) diseases of humans, crops and forests, pets, livestock, and wildlife constitute the so-termed medical enterprises (sciences and technologies) humans endeavor in biomedicine and clinical medicine, veterinary, plant protection, and wildlife conservation. In recent years, the significance of ecological science to medicines has received rising attentions, and the emergence and pandemic of COVID-19 appear accelerating the trend. The facts that diseases are simply one of the fundamental ecological relationships in nature, and the study of the relationships between species and their environment is a core mission of ecology highlight the critical importance of ecological science. Nevertheless, current studies on the ecology of medical enterprises are highly fragmented. Here, we (i) conceptually overview the fields of disease ecology of wildlife, cancer ecology and evolution, medical ecology of human microbiome-associated diseases and infectious diseases, and integrated pest management of crops and forests, across major medical enterprises. (ii) Explore the necessity and feasibility for a unified medical ecology that spans biomedicine, clinical medicine, veterinary, crop (forest and wildlife) protection, and biodiversity conservation. (iii) Suggest that a unified medical ecology of human diseases is both necessary and feasible, but laissez-faire terminologies in other human medical enterprises may be preferred. (iv) Suggest that the evo-eco paradigm for cancer research can play a similar role of evo-devo in evolutionary developmental biology. (v) Summarized 40 key ecological principles/theories in current disease-, cancer-, and medical-ecology literatures. (vi) Identified key cross-disciplinary discovery fields for medical/disease ecology in coming decade including bioinformatics and computational ecology, single cell ecology, theoretical ecology, complexity science, and the integrated studies of ecology and evolution. Finally, deep understanding of medical ecology is of obvious importance for the safety of human beings and perhaps for all living things on the planet. Copyright © 2022 Ma and Zhang.
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Speech-based automatic smoker identification (also known as smoker/non-smoker classification) aims to identify speakers' smoking status from their speech. In the COVID-19 pandemic, speech-based automatic smoker identification approaches have received more attention in smoking cessation research due to low cost and contactless sample collection. This study focuses on determining the best acoustic features for smoker identification. In this paper, we investigate the performance of four acoustic feature sets/representations extracted using three feature extraction/learning approaches: (i) hand-crafted feature sets including the extended Geneva Minimalistic Acoustic Parameter Set and the Computational Paralinguistics Challenge Set, (ii) the Bag-of-Audio-Words representations, (iii) the neural representations extracted from raw waveform signals by SincNet. Experimental results show that: (i) SincNet feature representations are the most effective for smoker identification and outperform the MFCC baseline features by 16% in absolute accuracy;(ii) the performance of hand-crafted feature sets and the Bag-of-Audio-Words representations rely on the scale of the dimensions of feature vectors. © 2022 IEEE
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As it is reported, the detailed COVID-19 cases have exceeded 400 million worldwide. And there is another outbreak of the COVID-19 infection in England due to the emergence of a new variant: Omicron. Through implementing distinctive control measures, most vaccination has been accomplished to an expansive levels in this country and is as of now in advance. Due to the popularity of vaccines and the success of anti-epidemic measures, the English government decide to announce the last legal restrictions being lifted by February 24, which means that English will be the first country to declare victory over COVID-19. To judge whether the decision is correct or not, we estimate confirmed cases, death, daily new confirmed cases and trend through modeling and simulating method. Considering the effectiveness of vaccines, we raise a new epidemic model named SEIR-V. Our results appear that if transmission rate increases by 15% compared to the current rate due to unwinding of social distancing conditions, the daily new cases can crest to 200k per day around April 1, 2022. The combination of vaccination and controlled legal restrictions is the key to tackling the emergency of the new variant Omicron epidemic. Considering that the new variant strains increase transmissibility and have high resistance to vaccines, the English government should continue the current epidemic prevention measures to avoid the emergence of a second wave of the epidemic. © 2022 SPIE
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Power laws (PLs) have been found to describe a wide variety of natural (physical, biological, astronomic, meteorological, and geological) and man-made (social, financial, and computational) phenomena over a wide range of magnitudes, although their underlying mechanisms are not always clear. In statistics, PL distribution is often found to fit data exceptionally well when the normal (Gaussian) distribution fails. Nevertheless, predicting PL phenomena is notoriously difficult because of some of its idiosyncratic properties, such as lack of well-defined average value and potentially unbounded variance. Taylor's power law (TPL) is a PL first discovered to characterize the spatial and/or temporal distribution of biological populations. It has also been extended to describe the spatiotemporal heterogeneities (distributions) of human microbiomes and other natural and artificial systems, such as fitness distribution in computational (artificial) intelligence. The PL with exponential cutoff (PLEC) is a variant of power-law function that tapers off the exponential growth of power-law function ultimately and can be particularly useful for certain predictive problems, such as biodiversity estimation and turning-point prediction for Coronavirus Diease-2019 (COVID-19) infection/fatality. Here, we propose coupling (integration) of TPL and PLEC to offer a methodology for quantifying the uncertainty in certain estimation (prediction) problems that can be modeled with PLs. The coupling takes advantage of variance prediction using TPL and asymptote estimation using PLEC and delivers CI for the asymptote. We demonstrate the integrated approach to the estimation of potential (dark) biodiversity of the American gut microbiome (AGM) and the turning point of COVID-19 fatality. We expect this integrative approach should have wide applications given duel (contesting) relationship between PL and normal statistical distributions. Compared with the worldwide COVID-19 fatality number on January 24th, 2022 (when this paper is online), the error rate of the prediction with our coupled power laws, made in the May 2021 (based on the fatality data then alone), is approximately 7% only. It also predicted that the turning (inflection) point of the worldwide COVID-19 fatality would not occur until the July of 2022, which contrasts with a recent prediction made by Murray on January 19th of 2022, who suggested that the "end of the pandemic is near " by March 2022.
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With the impact of COVID-19, more people are choosing to travel by private cars, which will cause problems such as traffic congestion. It is essential for traffic engineers to have real-time traffic volume, speed, and individual vehicle length. In this study, the ACC7350 millimeter-wave radar was tested, and its advantages and disadvantages were analyzed in vehicle speed, distance from the radar, and vehicle trajectory. The speed detection error between MWR and GPS was within ±6%, and the distance detection error was ±20%. Then the traffic flow detection results of the camera and millimeter-wave radar were compared and analyzed. Results show that the mistakes of traffic flow detection based on vision and MWR are ±4% and ±13%, respectively. Finally, we proposed a traffic data processing method combined with a camera-based target tracking algorithm. © 2021 SPIE.
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Hydrogen peroxide (H2O2) solution and its aerosols are common disinfectants, especially for urgent reuse of personal protective equipment during the COVID-19 pandemic. Highly sensitive and selective evaluation of the H2O2 concentration is key to customizing the sufficient disinfection process and avoiding disinfection overuse. Amperometric electrochemical detection is an effective means but poses challenges originated from the precarious state of H2O2. Here, an atomic Co-N-x-C site anchored neuronal-like carbon modified amperometric sensor (denoted as the CoSA-N/C@rGO sensor) is designed, which exhibits a broad detection range (from 250 nM to 50 mM), superior sensitivity (743.3 mu A mM(-1) cm(-2), the best among carbon-based amperometric sensors), strong selectivity (no response to interferents), powerful reliability (only 2.86% decay for one week) and fast response (just 5 s) for residual H2O2 detection. We validated the accuracy and practicability of the CoSA-N/C@rGO sensor in the actual H2O2 disinfection process of personal protective equipment. Further characterization verifies that the electrocatalytic activity and selective reduction of H2O2 is determined by the atomically dispersed Co-N-x-C sites and the high oxygen content of CoSA-N/C@rGO, where the response time and reliability of H2O2 detection is determined by the neuronal-like structure with high nitrogen content. Our findings pave the way for developing a sensor with superior sensitivity, selectivity and stability, rendering promising applications such as medical care and environmental treatment.
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Introduction: The emergence of the SARS-CoV-2 virus, the cause of the COVID-19 pandemic, in late 2019 put every country on high alert and led to major changes in global diagnostic testing capability in infectious disease. From the outset it was apparent that local health authorities were under-prepared and under-staffed to cope with the rapid onset and spread of the disease. Demand for SAR-CoV-2 testing soared, highlighting the limitations of capacity in existing infectious disease laboratories along with requests from governments to support growing testing need. We partnered with US and UK Governments to establish, supply, staff and operate three large-scale, high-throughput SARS-CoV-2 testing facilities. These were ultimately established in Valencia, CA, offering testing of up to 150k samples per day, and in Loughborough and Newport, UK, offering a combined testing of up to 70k samples per day. The biggest challenge faced globally was the unprecedented scale of testing required and the timeframe to deliver a reliable and sensitive high-throughput assay. The benefits of industry and government partnerships become evident along with having a dedicated supply chain to feed the reagent and consumable needs for high-throughput testing as well as a highly accurate test with a fast turnaround time. Experts from multiple divisions, including R&D, Genomics, Enterprise, and regional centres were bought into the project, resulting in the establishment of SARS-CoV-2 testing within the three facilities in approximately eight weeks. Clinical testing experts in high-throughput, newborn screening, and rare disease testing, built molecular testing pipelines for the facilities based around the use of real-time polymerase chain reaction (RT-PCR) assays and sequencing. Laboratories were setup to meet the requirements set by various regulatory and accreditation agencies such as Clinical Laboratory Improvement Amendments, College of American Pathologies, the UK National Health Service validation group and ISO15189. Methods: Underpinning the testing was the massive IT and bioinformatics effort to enable reporting of the testing outcomes to the relevant authorities. We were able to deploy a novel LIMS system that is used throughout the laboratories to maintain sample chain of custody from arrival at the facility to reporting of results and incorporating interpretive software to support clinical interpretation of the resulting RT-PCR data. The LIMS systems are constantly undergoing improvement to support interpretation and troubleshooting. Local experts in clinical interpretation and reporting were onboarded to augment data analysis and ensure high-quality and reliable reporting whilst ensuring that clinical governance remains at the centre of all activities. Results: Before any SARS-CoV-2 testing was able to commence, several significant challenges were overcome by combining the expertise of our global teams with the local knowledge and support of the respective Governments. Experts in logistics and program management were able to convert three empty facilities with no pre-existing laboratory infrastructure into fully functional clinical testing laboratories within eight weeks. Our assay manufacturing capacity was majorly expanded to accommodate the requirements of SARS-CoV-2 testing, with all three facilities operating on automated platforms and utilizing chemistry with a dedicated secure supply chain. The final major challenge was rapid onboarding and training of staff for the facilities, and a year out, the two active facilities are currently employing over 600 individuals. Conclusion: To date the three facilities have performed over 12 million SARS-CoV-2 RT-PCR assays and SARS-CoV-2 testing will continue into 2022. The number of cases is again growing globally, and with the emergence of new variants and continual uncertainty about the impact on existing vaccines, there is an ongoing requirement for this scale of testing. From the experience of the SARS-CoV-2 global pandemic, the benefits of industry and government collaboration or the public has become much clearer, including greater access to large-scale testing options, significant reductions in time-to-testing and reporting and the rapid deployment of modern, cutting edge technology in diagnostic and monitoring programmes and eventually reduced costs to health services from mass-production. Ultimately the longevity of the individual testing facilities is unclear, but the future of large-scale clinical testing has changed forever and the legacy of this is the clear benefit to everybody when industry and governments work together to provide the public high quality and reliable testing operations.