Digital Twin of COVID-19 Mass Vaccination Centers

Sustaining the mobile medical units to bring equity in healthcare: a PLS-SEM approach

BACKGROUND

Equitable access to healthcare for rural, tribal, and underprivileged people has been an emerging area of interest for researchers, academicians, and policymakers worldwide. Improving equitable access to healthcare requires innovative interventions. This calls for clarifying which operational model of a service innovation needs to be strengthened to achieve transformative change and bring sustainability to public health interventions. The current study aimed to identify the components of an operational model of mobile medical units (MMUs) as an innovative intervention to provide equitable access to healthcare.

METHODS

The study empirically examined the impact of scalability, affordability, replicability (SAR), and immunization performance on the sustainability of MMUs to develop a framework for primary healthcare in the future. Data were collected via a survey answered by 207 healthcare professionals from six states in India. Partial least squares structural equation modeling (PLS-SEM) was conducted to empirically determine the interrelationships among various constructs.

RESULTS

The standardized path coefficients revealed that three factors (SAR) significantly influenced immunization performance as independent variables. Comparing the three hypothesized relationships demonstrates that replicability has the most substantial impact, followed by scalability and affordability. Immunization performance was found to have a significant direct effect on sustainability. For evaluating sustainability, MMUs constitute an essential component and an enabler of a sustainable healthcare system and universal health coverage.

CONCLUSION

This study equips policymakers and public health professionals with the critical components of the MMU operational model leading toward sustainability. The research framework provides reliable grounds for examining the impact of scalability, affordability, and replicability on immunization coverage as the primary public healthcare outcome.

Digital Twins in Healthcare: An Architectural Proposal and Its Application in a Social Distancing Case Study

The digital transformation process fostered by the development of Industry 4.0 technologies has largely affected the health sector, increasing diagnostic capabilities and improving drug effectiveness and treatment delivery. The Digital Twin (DT) technology, based on the virtualization of physical assets/processes and on a bidirectional communication between the digital and physical space for data exchange, is considered a game changer in modern health systems. Digital Twin applications in healthcare are various, ranging from virtualization of hospitals' physical spaces/organizational processes to individuals' physiological/genetic/lifestyle characteristics replication, and include the modeling of public health-related processes for monitoring, optimization and planning purposes. In this paper, motivated by the current COVID-19 pandemic, we focus on the application of the Digital Twin technology for virus containment on the workplace through social distancing. The contribution of this paper is three-fold: i) we review the existing literature on the adoption of the Digital Twin technology in the healthcare domain, and propose a classification of DT applications into four categories; ii) we propose a generalized Digital Twin architecture that can be used as reference to identify the main functional components of a Digital Twin system; iii) we present CanTwin, a real-life industrial case study developed by Hitachi and representing the Digital Twin of a canteen service serving 1100 workers, set up for social distancing monitoring, queue inspection, people counting and tracking, table occupancy supervision.

Applications of Social Media and Digital Technologies in COVID-19 Vaccination: Scoping Review

BACKGROUND

Social media and digital technologies have played essential roles in disseminating information and promoting vaccination during the COVID-19 pandemic. There is a need to summarize the applications and analytical techniques of social media and digital technologies in monitoring vaccine attitudes and administering COVID-19 vaccines.

OBJECTIVE

We aimed to synthesize the global evidence on the applications of social media and digital technologies in COVID-19 vaccination and to explore their avenues to promote COVID-19 vaccination.

METHODS

We searched 6 databases (PubMed, Scopus, Web of Science, Embase, EBSCO, and IEEE Xplore) for English-language articles from December 2019 to August 2022. The search terms covered keywords relating to social media, digital technology, and COVID-19 vaccines. Articles were included if they provided original descriptions of applications of social media or digital health technologies/solutions in COVID-19 vaccination. Conference abstracts, editorials, letters, commentaries, correspondence articles, study protocols, and reviews were excluded. A modified version of the Appraisal Tool for Cross-Sectional Studies (AXIS tool) was used to evaluate the quality of social media-related studies. The review was undertaken with the guidance of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews.

RESULTS

A total of 178 articles were included in our review, including 114 social media articles and 64 digital technology articles. Social media has been applied for sentiment/emotion analysis, topic analysis, behavioral analysis, dissemination and engagement analysis, and information quality analysis around COVID-19 vaccination. Of these, sentiment analysis and topic analysis were the most common, with social media data being primarily analyzed by lexicon-based and machine learning techniques. The accuracy and reliability of information on social media can seriously affect public attitudes toward COVID-19 vaccines, and misinformation often leads to vaccine hesitancy. Digital technologies have been applied to determine the COVID-19 vaccination strategy, predict the vaccination process, optimize vaccine distribution and delivery, provide safe and transparent vaccination certificates, and perform postvaccination surveillance. The applied digital technologies included algorithms, blockchain, mobile health, the Internet of Things, and other technologies, although with some barriers to their popularization.

CONCLUSIONS

The applications of social media and digital technologies in addressing COVID-19 vaccination-related issues represent an irreversible trend. Attention should be paid to the ethical issues and health inequities arising from the digital divide while applying and promoting these technologies.

Discrete-event simulation study of a COVID-19 mass vaccination centre

The global spread of COVID-19 and the declaration of the pandemic status made by the World Health Organization (WHO) led to the establishment of mass vaccination campaigns. The challenges posed by the request to immunise the entire population necessitated the set-up of new vaccination sites, named Mass Vaccination Centres (MVCs), capable of handling large numbers of patients rapidly and safely. The present study focused on the evolution of MVC performances, in terms of the maximum number of vaccinated patients and primary resource utilisation ratio, while involving statistics belonging to the patient dimension. The research involved the creation of a digital model of the MVC, using the Discrete-Event Simulation (DES) software (FlexSim Healthcare), and consequent what-if analyses. The results were derived from the study of an existing facility, located within a sports centre in the province of Bergamo (Italy) and operating with an advanced MVC organisational model, in compliance with the national anti-SARS-CoV-2 legislation. The research provided additional evidence on innovative MVC organisational models, identifying an optimal MVC configuration. Besides, the obtained results remain relevant for countries where a significant portion of the population has not yet addressed the emergency, either for upcoming vaccination treatments. Furthermore, the methodology adopted in the present article proved to be a valuable resource in the analysis of the healthcare processes.

Digital twin in healthcare: Recent updates and challenges

As simulation is playing an increasingly important role in medicine, providing the individual patient with a customised diagnosis and treatment is envisaged as part of future precision medicine. Such customisation will become possible through the emergence of digital twin (DT) technology. The objective of this article is to review the progress of prominent research on DT technology in medicine and discuss the potential applications and future opportunities as well as several challenges remaining in digital healthcare. A review of the literature was conducted using PubMed, Web of Science, Google Scholar, Scopus and related bibliographic resources, in which the following terms and their derivatives were considered during the search: DT, medicine and digital health virtual healthcare. Finally, analyses of the literature yielded 465 pertinent articles, of which we selected 22 for detailed review. We summarised the application examples of DT in medicine and analysed the applications in many fields of medicine. It revealed encouraging results that DT is being increasing applied in medicine. Results from this literature review indicated that DT healthcare, as a key fusion approach of future medicine, will bring the advantages of precision diagnose and personalised treatment into reality.

Multi-Disciplinary Design and Implementation of a Mass Vaccination Clinic Mobile Application to Support Decision-Making

Mass vaccination clinics are complex systems that combine professionals who do not typically work together. Coordinating vaccine preparation and patient intake is critically important to maintain patient flow equilibrium, requiring continuous communication and shared decision-making to reduce vaccine waste.

OBJECTIVES

(1) To develop a mobile application (app) that can address the information needs of vaccination clinic stakeholders for end-of-day doses decision-making in mass immunization settings; and (2) to understand usability and clinical implementation among multi-disciplinary users.

METHODS

Contextual inquiry guided 71.5 hours of observations to inform design characteristics. Rapid iterative testing and evaluation were performed to validate and improve the design. Usability and integration were evaluated through observations, interviews, and the system usability scale.

RESULTS

Designing the app required consolidating contextual factors to support information and workload needs. Twenty-four participants used the app at four clinics who reported its effectiveness in reducing stress and improving communication efficiency and satisfaction. They also discussed positive workflow changes and design recommendations to improve its usefulness. The average system usability score was 87 (n = 22).

DISCUSSION

There is significant potential for mobile apps to improve workflow efficiencies for information sharing and decision-making in vaccination clinics when designed for established cultures and usability, thereby providing frontline workers with greater time to focus on patient care and immunization needs. However, designing and implementing digital systems for dynamic settings is challenging when healthcare teams constantly adapt to evolving complexities. System-level barriers to adoption require further investigation. Future research should explore the implementation of the app within global contexts.

Tracing and measuring the COVID-19 Colombian vaccination network

The COVID-19 vaccination process in Colombia has been a major challenge not only in terms of public health but also in terms of supply chain management and logistics processes. To support the monitoring of these processes and associated decision-making, a dashboard was designed in Google Data Studio focused on analyzing the progress of COVID-19 vaccination and its logistics efficiency. This article describes the design and implementation of the dashboard using a design science approach and discusses the main lessons learned. During its development, four major challenges were identified: the search for and availability of data sources, the definition and standardization of metrics, the extraction of data in different formats; and finally, the validation of the metrics. Despite these challenges, the dashboard became a source of information for different stakeholders in the Colombian COVID-19 vaccination network, facilitating the monitoring of key performance indicators (KPIs), supporting decision-making, and policy evaluation. This reaffirms the importance of having open information to generate knowledge for both public and private entities as well as for the public. The main contribution of this work is the definition and standardization of the KPIs and it is therefore expected that this experience will serve as an insightful input for designing mass vaccination strategies.

Using simulation modelling and systems science to help contain COVID-19: A systematic review

This study systematically reviews applications of three simulation approaches, that is, system dynamics model (SDM), agent-based model (ABM) and discrete event simulation (DES), and their hybrids in COVID-19 research and identifies theoretical and application innovations in public health. Among the 372 eligible papers, 72 focused on COVID-19 transmission dynamics, 204 evaluated both pharmaceutical and non-pharmaceutical interventions, 29 focused on the prediction of the pandemic and 67 investigated the impacts of COVID-19. ABM was used in 275 papers, followed by 54 SDM papers, 32 DES papers and 11 hybrid model papers. Evaluation and design of intervention scenarios are the most widely addressed area accounting for 55% of the four main categories, that is, the transmission of COVID-19, prediction of the pandemic, evaluation and design of intervention scenarios and societal impact assessment. The complexities in impact evaluation and intervention design demand hybrid simulation models that can simultaneously capture micro and macro aspects of the socio-economic systems involved.

Integrating Digital Twins and Deep Learning for Medical Image Analysis in the era of COVID-19

Digital twins is a virtual representation of a device and process that captures the physical properties of the environment and operational algorithms/techniques in the context of medical devices and technology. It may allow and facilitate healthcare organizations to determine ways to improve medical processes, enhance the patient experience, lower operating expenses, and extend the value of care. Considering the current pandemic situation of COVID-19, various medical devices, e.g., X-rays and CT scan machines and processes, are constantly being used to collect and analyze medical images. In this situation, while collecting and processing an extensive volume of data in the form of images, machines and processes sometimes suffer from system failures that can create critical issues for hospitals and patients. Thus, in this regard, we introduced a digital twin based smart healthcare system integrated with medical devices so that it can be utilized to collect information about the current health condition, configuration, and maintenance history of the device/machine/system. Furthermore, the medical images, i.e., X-rays, are further analyzed by a deep learning model to detect the infection of COVID-19. The designed system is based on Cascade RCNN architecture. In this architecture, detector stages are deeper and are more sequentially selective against close and small false positives. It is a multi stage extension of the Recurrent Convolution Neural Network (RCNN) model and sequentially trained using the output of one stage for the training of the other one. At each stage, the bounding boxes are adjusted in order to locate a suitable value of nearest false positives during training of the different stages. In this way, an arrangement of detectors is adjusted to increase Intersection over Union (IoU) that overcome the problem of overfitting. We trained the model for X-ray images as the model was previously trained on another data set. The developed system achieves good accuracy during the detection phase of the COVID-19. Experimental outcomes reveal the efficiency of the detection architecture, which gains a mean Average Precision (mAP) rate of 0.94.

"Long COVID" results after hospitalization for SARS-CoV-2 infection

Long-term sequelae of symptomatic infection caused by SARS-CoV-2 are largely undiscovered. We performed a prospective cohort study on consecutively hospitalized Sars-CoV-2 patients (March–May 2020) for evaluating COVID-19 outcomes at 6 and 12 months. After hospital discharge, patients were addressed to two follow-up pathways based on respiratory support needed during hospitalization. Outcomes were assessed by telephone consultation or ambulatory visit. Among 471 patients, 80.9% received no respiratory support during hospitalization; 19.1% received non-invasive ventilation (NIV) or invasive mechanical ventilation (IMV). 58 patients died during hospitalization, therefore 413 were enrolled for follow-up. At 6 months, among 355 patients, the 30.3% had any symptoms, 18.0% dyspnea, 6.2% neurological symptoms. Fifty-two out of 105 had major damages in interstitial computed tomography images. NIV/IMV patients had higher probability to suffer of symptoms (aOR = 4.00, 95%CI:1.99–8.05), dyspnea (aOR = 2.80, 95%CI:1.28–6.16), neurological symptoms (aOR = 9.72, 95%CI:2.78–34.00). At 12 months, among 344, the 25.3% suffered on any symptoms, 12.2% dyspnea, 10.1% neurological symptoms. Severe interstitial lesions were present in 37 out of 47 investigated patients. NIV/IMV patients in respect to no respiratory support, had higher probability of experiencing symptoms (aOR = 3.66, 95%CI:1.73–7.74), neurological symptoms (aOR = 8.96, 95%CI:3.22–24.90). COVID-19 patients showed prolonged sequelae up to 12 months, highlighting the need of follow-up pathways for post-COVID-19 syndrome.

Lab Scale Model Experiment of Smart Hopper System to Remove Blockages Using Machine Vision and Collaborative Robot

In this study, we propose a smart hopper system that automatically unblocks obstructions caused by rocks dropped into hoppers at mining sites. The proposed system captures RGB (red green blue) and D (depth) images of the upper surfaces of hopper models using an RGB-D camera and transmits them to a computer. Then, a virtual hopper system is used to identify rocks via machine vision-based image processing techniques, and an appropriate motion is simulated in a robot arm. Based on the simulation, the robot arm moves to the location of the rock in the real world and removes it from the actual hopper. The recognition accuracy of the proposed model is evaluated in terms of the quantity and location of rocks. The results confirm that rocks are accurately recognized at all positions in the hopper by the proposed system.

COVID-19 outbreak and the role of digital twin

COVID-19 has transformed the life of human beings and digital twin infrastructure can facilitates working remotely during COVID-19 outbreak by reducing burden on services and infrastructure. Currently, many organizations are installing and developing devices such as thermal cameras, sensors aiming to minimize human contact and so forth, in addition to enforcing social distancing resulting in reducing the risk of transmission. Due to economic reasons, lockdown restrictions are being relaxed/lifted in many countries and Pakistan which is one of the most densely populated countries in the world with a population of 220 + million is no exception. Though, Pakistan contained the first two waves of coronavirus infections reasonably well but the country is struggling to contain the third wave of the spread due to violations of social distancing norms. While our predictions may deviate from official statistics due to lack of mass testing and existence of asymptomatic infections, the described approach predicts the possible actual burden of infection over times. In view of the unique demographics, our data quantify the efficacy of social distancing as an effective measure to forestall the infection. We highlight few areas where digital twins can be created/deployed to provide services and essential facilities to citizens as COVID-19 is expected to have permanent impact on the way we work.

Digital twin-driven smart supply chain

Today’s supply chain is becoming complex and fragile. Hence, supply chain managers need to create and unlock the value of the smart supply chain. A smart supply chain requires connectivity, visibility, and agility, and it needs be integrated and intelligent. The digital twin (DT) concept satisfies these requirements. Therefore, we propose creating a DT-driven supply chain (DTSC) as an innovative and integrated solution for the smart supply chain. We provide background information to explain the DT concept and to demonstrate the method for building a DTSC by using the DT concept. We discuss three research opportunities in building a DTSC, including supply chain modeling, real-time supply chain optimization, and data usage in supply chain collaboration. Finally, we highlight a motivating case from JD.COM, China’s largest retailer by revenue, in applying the DTSC platform to address supply chain network reconfiguration challenges during the COVID-19 pandemic.

The application of industry 4.0 technologies in pandemic management: Literature review and case study

The Covid-19 pandemic impact on people's lives has been devastating. Around the world, people have been forced to stay home, resorting to the use of digital technologies in an effort to continue their life and work as best they can. Covid-19 has thus accelerated society's digital transformation towards Industry 4.0 (the fourth industrial revolution). Using scientometric analysis, this study presents a systematic literature review of the themes within Industry 4.0. Thematic analysis reveals that the Internet of Things (IoT), Artificial Intelligence (AI), Cloud computing, Machine learning, Security, Big Data, Blockchain, Deep learning, Digitalization, and Cyber-physical system (CPS) to be the key technologies associated with Industry 4.0. Subsequently, a case study using Industry 4.0 technologies to manage the Covid-19 pandemic is discussed. In conclusion, Covid-19,is clearly shown to be an accelerant in the progression towards Industry 4.0. Moreover, the technologies of this digital transformation can be expected to be invoked in the management of future pandemics.