TwinVax: Leveraging Digital Twin Simulation to Monitor Vaccine Storage and Population Immunisation

TwinVax: Leveraging Digital Twin Simulation to Monitor Vaccine Storage and Population Immunisation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article TwinVax: Leveraging Digital Twin Simulation to Monitor Vaccine Storage and Population Immunisation Leonardo Oliveira El-Warrak, Claudio Miceli de Farias, Victor Hugo Dias Macedo Azevedo Costa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6245899/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background This paper presents the application of simulation to assess the functionality of a proposed Digital Twin (DT) architecture for immunisation services in primary healthcare centres. The solution is based on Industry 4.0 concepts and technologies, such as IoT, machine learning, and cloud computing, and adheres to the ISO 23247 standard. Methods The system modelling is carried out using the Unified Modelling Language (UML) to define the workflows and processes involved, including vaccine storage temperature monitoring and population vaccination status tracking. The proposed architecture is structured into four domains: observable elements/entities, data collection and device control, digital twin platform, and user domain. To validate the system's performance and feasibility, simulations are conducted using SimPy, enabling the evaluation of its response under various operational scenarios. Results The system facilitates the storage, monitoring, and visualisation of data related to the thermal conditions of ice-lined refrigerators (ILR) and thermal boxes. Additionally, it analyses patient vaccination coverage based on the official immunisation schedule. The key benefits include optimising vaccine storage conditions, reducing dose wastage, continuously monitoring immunisation coverage, and supporting strategic vaccination planning. Conclusion The paper discusses the future impacts of this approach on immunisation management and its scalability for diverse public health contexts. By leveraging advanced technologies and simulation, this digital twin framework aims to improve the performance and overall impact of immunization services. Biological sciences/Immunology/Vaccines Health sciences/Health care Health sciences/Medical research Immunisation Digital Twin Vaccines TwinVax Simulation IoT Figures Figure 1 Figure 2 Figure 3 Contributions to the literature DT appears as one of most discussed technology applications within the Digital Health trend. Digital twins hold the potential to transform healthcare, especially in the management and operational efficiency of healthcare services. The use of digital twins in primary health care is still poorly explored but has great potential for improving the population's health indicators, especially across developing countries. The use of digital twins in immunisation could support the recovery of vaccination coverage. 1- INTRODUCTION Global health has faced unprecedented challenges in recent decades, with the COVID-19 pandemic pushing healthcare systems to their operational limits. This crisis exposed critical vulnerabilities, including an exponential surge in patient volume, shortages of essential resources, technical gaps in healthcare workforce preparedness, and an overload of fragmented or inaccurate information that impeded effective decision-making. Despite immunisation being one of the most impactful public health interventions, the pandemic severely disrupted routine vaccination programs worldwide, particularly in 2020 and 2021, causing significant setbacks in disease prevention efforts [ 1 , 2 ]. Faced with persistent challenges in controlling vaccine-preventable diseases, technological advances have emerged as a vital ally. New technologies offer a solid basis for faster and more effective responses during health crises, while simultaneously improving the general health of populations under normal circumstances. By integrating these solutions into health systems, it is possible to enable a more precise and consistent approach by health teams, improve resource allocation, and strengthen the health response capacity of healthcare systems [ 4 ]. Vaccination remains the cornerstone of disease prevention, offering robust protection against vaccine-preventable illnesses without the risks associated with natural infection. It not only shields individuals from severe disease outcomes but also curtails the spread of pathogens within communities. Achieving high vaccination coverage fosters herd immunity, effectively interrupting disease transmission chains. Beyond health outcomes, vaccination programs yield extensive societal benefits by reducing healthcare burdens, mitigating economic losses, and enhancing community well-being [ 5 , 6 , 7 , 8 ]. The healthcare sector is a dynamic landscape of technological innovation, consistently integrating new tools to enhance service delivery and patient outcomes [ 9 , 10 ]. Vaccines, however, are sensitive biological products that require stringent temperature control to maintain their efficacy. Failures in cold chain management—from storage to transportation—remain a leading cause of vaccine spoilage and wastage. In this context, advanced technologies such as the Internet of Things (IoT) and Digital Twins (DT) offer transformative potential. These technologies enhance real-time monitoring, predictive maintenance, and data-driven decision-making, particularly in settings with complex operational challenges. This paper addresses two critical issues in immunisation programs: (i) vaccine loss due to inadequate temperature control in storage equipment, such as Ice-Lined Refrigerators (ILRs) and thermal boxes, and (ii) suboptimal vaccination coverage in specific target populations. To tackle these challenges, we propose a conceptual framework for a Digital Twin (DT) tailored to support immunisation services within primary healthcare settings. Through simulation, we aim to demonstrate how this DT—referred to as TwinVax—can optimise vaccine storage conditions via real-time temperature monitoring and enhance immunisation coverage through dynamic data analytics, ultimately supporting timely and evidence-based public health decision-making. 2- RELATED STUDIES Technological innovations play a central role in shaping health systems, influencing how services are delivered and the outcomes achieved. These innovations encompass a wide range of technologies used for prevention, diagnosis, treatment, and rehabilitation, including vaccines, diagnostic kits, medications, medical equipment, and procedures. The continuous advancement of technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), 5G, and Big Data has enabled real-time data collection, processing, and storage, fostering new opportunities for healthcare management. Chaudhari, Gangane, and Lahe (2021) [ 11 ] highlight how Digital Twin (DT) technology enhances digital health monitoring within Industry 4.0 concepts, describing DTs as real-time virtual replicas of physical objects that enable continuous health tracking and predictive analytics through IoT and AI integration. Katsoulakis et al. (2024) [ 12 ] explore DT applications in healthcare, emphasizing their role in personalizing treatments and improving patient outcomes through real-time data monitoring and computational models. Similarly, Björnsson et al. (2019) [ 13 ] discuss DTs as tools for personalized medicine, using patient-specific biological, clinical, and behavioral data to simulate and predict treatment responses. Stahlberg et al. (2022) [ 14 ] examine predictive DTs for cancer patients, focusing on integrating patient data, AI, and computational models to simulate disease progression. The study highlights challenges related to data interoperability, model validation, and ethical concerns, while reinforcing DTs' potential to enhance clinical decision-making in oncology. Popa et al. (2021) [ 15 ] analyze DTs from a socioethical perspective, recognizing their benefits in personalized care and decision support but also addressing concerns around data privacy, patient autonomy, and biases in predictive models. Sahal et al. (2022) [ 16 ] investigate the role of emerging technologies—including DTs, blockchain, IoT, and AI—in managing pandemic crises. They propose a blockchain-based framework for decentralized epidemic alerts, stressing the need for secure, real-time data exchange to support COVID-19 response efforts. El-Warrak and Miceli (2024) [ 17 ] categorize DT applications in healthcare into clinical and operational domains, covering personalized care, simulation of biological structures, process optimization, and resource management. Despite challenges related to data integration and privacy, DTs show great potential to improve healthcare quality, remote monitoring, prevention, and decision-making. Uddin et al. (2016) [ 18 ] report on an mHealth intervention in Bangladesh using the "mTika" app to improve vaccination rates in hard-to-reach populations. The initiative significantly increased coverage through electronic birth registration and vaccination reminders, demonstrating mHealth’s effectiveness despite challenges like data standardization and limited follow-up capacity. Demsash et al. (2023) [ 19 ] apply ML algorithms to predict childhood vaccination coverage in Ethiopia, identifying key predictors such as maternal education and healthcare access, though limitations in statistical interpretability were noted. In Brazil, Ribeiro (2022) [ 20 ] proposes a DT architecture to modernize the National Vaccination Plan, enhancing resource management through real-time simulations. In a subsequent study, Ribeiro (2023) [ 21 ] introduces a maturity model to assess public healthcare units' readiness for DT implementation, identifying critical factors such as information security, logistics, and organizational management. The literature review reveals key insights into DT applications in healthcare. Most studies remain theoretical, focusing on opportunities and challenges with limited real-world implementations for evaluation. Some works address Digital Shadows or Digital Models, lacking real-time data exchange, which limits the exploration of DTs’ full potential, such as influencing physical systems through intelligent analysis [ 22 ]. Additionally, DT studies often focus on isolated assets without considering the broader healthcare ecosystem [ 22 , 23 ]. Many rely on simulations disconnected from real systems, neglecting aspects of integration, interoperability, and human involvement [ 11 , 24 , 25 , 26 ]. Finally, while DT functionalities are widely discussed, concrete architectures or methodologies for DT implementation in Primary Health Care are still scarce. 3- METHODS 3.1 - MODELLING THE DIGITAL TWIN IN IMMUNISATION The incorporation of digital models as an integral part of studying and developing physical objects is well-established. Virtual prototyping combines digital technology with engineering and design principles, enabling the efficient and precise creation and refinement of physical products and systems. The ability to virtually simulate and analyse objects before physical production has proven invaluable across various sectors, including manufacturing, engineering, architecture, and medicine. This approach offers benefits such as time savings, cost reductions, and optimized product performance and functionality [ 4 , 25 ]. A model is a representation of a real system, used to conduct simulation studies. To ensure the quality of information derived from the simulation, all elements relevant to capturing essential data from the real system must be incorporated [ 27 , 28 ]. The use of Discrete Event Simulation (DES) in this study is justified by the nature of the data required for modelling the digital twin. DES operates with discrete values that change at specific points in time, allowing clear transitions between states. Additionally, in DES, all data, entities, and activities are identifiable once the model is finalised, enabling a chronological understanding of events. The core of a DT consists of virtual models, making the development of high-precision digital representations essential. These models must accurately capture the physical properties, behaviours, and governing rules of the real object. The creation of a digital twin involves two primary aspects: (i) developing the processes and information requirements of the DT throughout the product life cycle—from asset design to real-world deployment and maintenance; and (ii) implementing the enabling technology to integrate the physical asset with its digital counterpart. This integration ensures real-time sensor data flow, along with operational and transactional information from the organization's core systems, as outlined in a conceptual architecture. A digital twin is characterized by four key features: Modelling and Simulation, Real-time Data Integration, Analysis and Optimization, and Insights and Action. Modelling provides a detailed representation of the physical system, encompassing attributes such as mechanical, electrical, and operational properties. Simulation allows testing under diverse conditions to predict behaviour in real-world scenarios. Real-time data integration, powered by IoT sensors, enhances simulation accuracy, and enables early issue detection. Additionally, advanced analytics and machine learning techniques can uncover patterns, predict failures, and suggest improvements. Real-time or near-real-time physical data updates are critical for refining virtual models and accurately simulating physical processes and their evolution. The network plays a vital role in connecting the physical object (PO) to its virtual representation (VO), enabling real-time data exchange. This connection supports bidirectional communication, where data from the PO updates the virtual model, and insights from the virtual model inform decisions in the physical system. Communication between PO and VO typically involves three stages [ 29 ]: (i) collecting data through direct measurement of physical conditions, (ii) processing and interpreting data at the appropriate level of abstraction, and (iii) updating system states with integrated data from multiple sources. The interface linking the real process to the DT, represented by the simulation model, varies depending on the characteristics of the simulation software and the connectivity capabilities of the physical system. A two-phase approach can be employed for predictive modelling in health service management. The first phase involves offline model development, utilizing Machine Learning (ML) and Deep Learning (DL) techniques, such as classifiers, to train the model with historical data from the DT. In this controlled environment, accuracy is improved before real-time deployment. The second phase consists of deploying the trained model online, closer to the data source, to minimize latency and optimize performance. By leveraging real-time streaming data, the model can quickly detect potential risks, enabling rapid responses and necessary adjustments. This two-phase approach integrates extensive historical data analysis with the agility of real-time processing, ensuring a reliable predictive model for proactive health service management. Optimisation is achieved by refining parameters in the digital model and implementing best practices in the physical system. Insights generated by the DT facilitate continuous improvements in healthcare service delivery [ 30 , 31 ]. Process modelling and simulation are invaluable tools across multiple domains. Recent advances in Industry 4.0, Big Data, IoT, and Sensor Technology have expanded their application in Digital Twins (DT). In this context, process models have evolved from passive tools for hypothesis testing into active components of operational systems. With efficient infrastructures and advanced algorithms, these models can monitor and reflect real-time system states while autonomously executing corrective actions when necessary. The methodology presented builds upon the framework introduced in [ 32 ], originally designed for dynamic modelling in discrete industrial processes. Given the specific needs of immunisation systems—such as vaccine cold chain management and coverage monitoring—this approach has been adapted for healthcare, enabling digital twin applications in immunisation services. The goal is to optimise vaccine storage temperature monitoring and enhance vaccination coverage assessment. Digital modelling consolidates critical information into a computational environment, allowing analysis and prediction of issues such as temperature fluctuations, vaccine loss risks, and gaps in immunisation coverage. While static digital models do not perform real-time physical simulations, they provide a robust foundation for identifying trends, assessing risks, and supporting decision-making processes. The static models within the DT for immunisation are utilized to evaluate cold chain integrity and predict the potential impacts of storage system failures. This includes cross-referencing historical temperature data with distribution patterns to detect risks that may compromise vaccine efficacy. Additionally, digital modelling supports continuous assessment of vaccination coverage, enabling targeted immunisation campaign planning. Although static digital models do not capture real-time environmental changes, their continuous updates with sensor data and vaccination records enhance predictive accuracy and risk identification. By integrating data-driven insights into immunisation management, the DT strengthens proactive decision-making and supports the resilience of vaccination programs. For modelling, immunisation services in Primary Healthcare Centre can be divided into seven main components or entities, as illustrated in Fig. 1 . These components include patients, health human resources, facilities, equipment, health supplies, processes, and partners [ 33 ]. The patients' component includes various types of patients, categorized by age groups, health histories, and specific needs, such as those with acute and chronic diseases, disabilities, or immunological risks. The healthcare human resources encompass nurses, technicians, and operational staff. Healthcare facilities cover the immunization room, waiting area, and staff offices. Equipment pertains to all medical devices, IT infrastructure, and furniture. Healthcare supplies are divided into physical and service supplies. Physical supplies include vaccines, medications, drugs, lab materials, cleaning supplies, treatment materials, and other essentials for maintaining healthcare facilities and equipment. Service supplies consist of crucial services received from partners, such as maintenance for medical equipment, catering for staff, patients, and visitors, and utilities like energy and water. Processes include procedures for treating patients with immunobiologicals, managing medical emergencies, organising the vaccine room, staff scheduling, recording information in systems, inventory monitoring of vaccines, supply chain management, workflow optimization, and other operational processes. Partners include suppliers of equipment and consumables, hospitals, specialized healthcare centres, and others. Digital Twins can be created for all these components. They use data from healthcare facilities, equipment, processes, patients with various needs, supplies, and partners, compiling real-time information from sensors, health information systems, such as electronic medical records (EMR), electronic health records (EHR) and other sources to create digital replicas. For example, digital counterparts can be developed for healthcare facilities such as X-ray rooms and other healthcare processes such as treatment and logistics processes. On the other hand, creating DTs of patients presents one of the most complex challenges in healthcare due to the need to represent diverse patient characteristics such as age, gender, health history, current health status, and healthcare needs. Studies such as those by [ 34 ] have explored the main design requirements and enabling technologies of digital patient twins, as well as the technical challenges present. The complexity involved stems from multiple levels of abstraction, different types of patients, numerous environmental factors, and continuous and rapid changes in healthcare data. Patient digital twins can be developed with varying levels of detail depending on their purpose. For example, refined models can reflect real-time health and environmental information from individual patients, supporting personalized medical services. For most applications in immunization services, these detailed individual models are extremely valid. However, even if there is no detailed health analysis, the immunization service also benefits from an abstract view of aggregated patient data to support high-level decision-making, improving overall efficiency, quality, access, and the cost-effectiveness of vaccination. This model comprises (i) a patient information database populated with clinical data from multiple sources; (ii) a cloud computing platform; (iii) traceability systems using AI; and (iv) blockchain technology. Other components, such as human resources, facilities, and equipment, are less complex and can be generalized based on their specific characteristics. For instance, a digital twin of a nurse would focus on their schedule, work location, and skills, rather than individual traits. Similarly, digital twins for facilities and equipment are relatively static and can be periodically updated as needed. This study will focus on three components of the represented system: the equipment used for vaccine storage, the vaccines themselves, and the patients. For the equipment entity, the monitoring of operational conditions will be based on the temperature attribute. In the case of the vaccine entity, monitoring will be conducted based on the type and number of doses, while for patients, it will pertain to their vaccination schedule and history. The digital twin will function by providing alerts regarding variations in the ideal temperature conditions for storage that may jeopardize the vaccine's efficacy, as well as alerts for timely vaccination needs for patients. Furthermore, the Digital Twin should propose scenario analyses for individuals and/or groups who may delay or miss certain vaccinations. Additionally, considering the vaccination needs according to the vaccination schedule of the target public, the DT could also estimate the ideal quantity of vaccines to be available in the immunisation room. In this work, the events of interest will include the temperature measurements collected by sensors and the vaccines administered to patients by dose and type, as recorded in the information systems designed for this purpose. 3.2 - ARCHITECTURE BASED ON ISO 23247 - THE TWINVAX In this work, the ISO 23247 standard for Digital Twin (DT) frameworks, originally designed for manufacturing, is adapted to a healthcare context. The proposed DT architecture focuses on temperature monitoring through a 2D dashboard and predictive models for anomaly detection and temperature forecasting. It also includes a module for tracking administered vaccine doses by type and dose, enabling cross-referencing with population vaccine needs to support preventive vaccination actions. The DT’s functionalities, aligned with ISO 23247, range from monitoring and remote access to simulation, control, optimisation, and predictive analysis, ensuring effective feedback for both users and equipment operations. Observing the possibility of integration between IoT architectures and the digital twin modelling proposed in ISO 23247 and adapting them to a more simplified and understandable form for healthcare, a 4-layer architecture is proposed, capable of implementing immunisation DT, here, named TwinVax, as seen in Fig. 2 . The TwinVax architecture is structured into four interconnected domains: Observable Manufacturing Elements (OME), Data Collection and Device Control Entity (DCDCE), Digital Twin Platform (DTP), and User Domain (UD). This layered approach ensures efficient data collection, processing, and utilisation for immunisation management. OBSERVABLE MANUFACTURING ELEMENTS (OME) The Observable Manufacturing Elements (OME) domain corresponds to the physical entities layer, which includes the Ice Lined Refrigerators (ILR) and thermal boxes used for vaccine storage. Within these elements, temperature data will be collected through sensors, which are also integral to the monitoring system. Additionally, the target population for immunisation is considered, with their vaccination data being managed and monitored by the system. To configure the digital twin, information from Electronic Health Records (EHR) including Electronic Medical Records (EMR) and specific Immunisation Information Systems regarding patient data and vaccination records will be integrated. It is important to emphasise that the sensors must adhere to technical standards compatible with industrial communication, such as OPC UA, MQTT, and HTTP, ensuring clear modelling of physical entities and efficient data exchange. DATA COLLECTION AND DEVICE CONTROL ENTITY (DCDCE) The second layer corresponds to the connection layer, which encompasses the domain of data collection and control of actuating devices present in the previous Observable Manufacturing Elements (OME) layer. This layer facilitates communication and data transfer between the sensors and the digital twin. Data extraction is performed by temperature sensors (e.g., DS18B20, DHT11, or LM35DZ), with initial transformation occurring through local processing, where a device converts electrical signals into data transferable via standard IoT integration protocols. As the connection layer is based on IoT architecture, temperature sensors will be installed in Ice Lined Refrigerators (ILRs) and thermal boxes to detect variations outside the ideal temperature range, maintained between 2°C and 8°C. These sensors will communicate with the edge layer using the 802.15.4 protocol. Temperature data will be collected once per hour for 5 minutes, contextualized, and analysed by an algorithm to identify potential issues related to the loss of immunogenic potency due to inadequate storage conditions. Proactive temperature management will be ensured by triggering alerts when temperatures exceed 7°C or fall below 3°C, serving as early warnings that allow preventive actions before reaching critical limits. The device responsible for transmitting the temperature data to the cloud is the ESP-32 (WROOM-D model), chosen for its strong connectivity features. The ESP-32 is programmed to collect data from the temperature sensors via GPIO pins and transmit this information using Wi-Fi. A server-side application developed in Node.js runs directly on the ESP-32, enabling data processing and transmission via lightweight protocols such as MQTT or HTTP.An MQTT broker receives the temperature data, processes it, and makes it available for authorized subscribers. Additionally, a Node-RED service running on the ESP-32 Gateway acts as an MQTT subscriber, collecting data from the broker and securely transmitting it to the cloud, facilitating real-time monitoring of refrigeration conditions, visualisation of temperature history, and automated notifications in case of deviations. For enhanced data security, Transport Layer Security (TLS) encryption is applied to all communication between devices, preventing interception or unauthorized modifications. Furthermore, encryption at rest is implemented in storage systems to protect sensitive data. In addition to the IoT-based monitoring layer, the architecture integrates electronic health records and vaccination registries through standardized interoperability protocols. Data exchange between the Electronic Health Record (EHR), the vaccination registration system, and the digital twin follows the HL7 (Health Level 7) standard, ensuring structured and secure information flow. Both RESTful APIs and GraphQL can be implemented for data synchronization, allowing healthcare professionals to retrieve and manage clinical data and vaccination records efficiently. Given the critical nature of healthcare data, additional security measures are applied to prevent unauthorized access. OAuth 2.0 authentication and Advanced Encryption Standard (AES) encryption ensure that only authorized users can access and manipulate stored information, safeguarding patient confidentiality. Data transmission is also secured using TLS, aligning with industry best practices and regulatory frameworks such as GDPR. The entire system infrastructure is hosted in the cloud, ensuring scalability, reliability, and compliance with security standards while allowing seamless integration of IoT data and healthcare information. The choice of cloud provider is flexible and can be adapted based on specific deployment requirements. This combination of IoT monitoring, secure data transmission, and interoperability between healthcare systems ensures an efficient and proactive approach to vaccine storage and management. DIGITAL TWIN PLATFORM (DTP) At the core of TwinVax, the Digital Twin Platform (DTP) layer is responsible for managing application services, data processing, and system operations. This layer supports critical functions such as data analysis, aggregation, rule application, and storage, ensuring the seamless operation of the TwinVax system. To achieve robust performance and scalability, the architecture leverages Amazon Web Services (AWS), utilizing services like AWS IoT SiteWise and Node-RED for efficient data processing, transformation, and presentation. Communication within the DTP is streamlined using the MQTT protocol, which optimizes IoT data transmission by reducing the computational load on devices, especially low-power ones such as the ESP-32. This efficiency is crucial for maintaining real-time responsiveness while conserving device resources. The data transformation process involves converting raw data collected from sensors and health records into actionable metrics. This includes calculating average daily temperatures and assessing vaccination coverage rates across different population segments, providing valuable insights for immunisation management. For data storage, the DTP employs time-series databases like InfluxDB or TimescaleDB. These databases are specifically designed to handle continuous monitoring data efficiently, supporting the long-term management and analysis of large volumes of time-stamped data generated by vaccine storage monitoring and immunisation tracking. Scalability is a fundamental consideration for TwinVax, particularly when expanding to larger healthcare networks or national health systems. To support this, the architecture adopts a microservices approach, allowing individual system components—such as temperature monitoring, vaccine management, and patient records—to be updated, scaled, and maintained independently. This modularity enhances system flexibility and simplifies maintenance without disrupting overall operations. Additionally, the elasticity of cloud services enables dynamic resource allocation, automatically adjusting computing and storage capacity to meet fluctuating data demands. In scenarios with high data traffic, the use of content distribution networks (CDNs) and load balancing mechanisms ensures efficient data delivery, system reliability, and high availability, even under peak load conditions. USER DOMAIN (UD) This layer is designed to support healthcare professionals and managers by providing access to data visualisation and decision-support tools. It includes services such as temperature monitoring dashboards, predictive vaccination analyses, and automated notifications. The data visualisation aspect relies on interactive dashboards, such as Grafana, which display real-time information with colour-coded indicators—green for normal conditions, yellow for alerts, and red for emergencies. Historical data trends are also available, enabling quick and clear monitoring of the situation. Additionally, predictive analyses help forecast vaccine demand and identify individuals who are due for immunisation, supporting proactive planning. To ensure timely communication, the system includes SMS alerts for staff and patients, push notifications through mobile apps, and on-site visual alarms. These elements together create a comprehensive architecture, allowing for effective vaccine management by enhancing real-time monitoring, forecasting, and data-driven decision-making in immunisation programs. 4- RESULTS To demonstrate the practical applicability of TwinVax, based on the proposed modelling and architecture, an environment for simulating discrete events using Python - SimPy - was employed. SimPy allows for modelling complex systems involving concurrent processes, such as queues, waiting times, and interactions between different entities over time. In this study, SimPy was used to simulate the operation of TwinVax, enabling the evaluation of system behaviour under various conditions, such as temperature variations in storage equipment and fluctuations in vaccination coverage. This validation step ensures that temperature alerts and real-time vaccination coverage analyses function as expected before actual implementation. The simulation was conducted using the WHO vaccination schedule for 2024 (Fig. 3 ) and focused on a population of 3.527 residents of Rio de Janeiro, aged between 1 and 90 years. To ensure the robustness and accuracy of the simulation, additional steps were taken to refine the representation of the population. Stratified sampling was employed to capture the diverse age distribution within the community. Age groups were carefully defined to align with established vaccination schedules and guidelines, ensuring that the simulation accurately reflects the real-world distribution of vaccines and their coverage needs. Specifically, the age groups were selected to represent the distinct vaccination schedules for infants, children, adults, and the elderly, each with different vaccination requirements and intervals. This stratification allows the system to simulate varying vaccination coverage and test TwinVax’s ability to manage vaccines across different stages of life. The stratified sampling divided the population into age groups, with selections made proportionally within each group. This resulted in a sample of 100 individuals, in line with statistical requirements for a 95% confidence level and a 10% margin of error. The following age groups were considered in the stratification: under 1 year (3 individuals), 1–6 years (7 individuals), 7–9 years (3 individuals), 10–19 years (8 individuals), 20–39 years (23 individuals), 40–59 years (28 individuals), and 60 + years (28 individuals). The TwinVax Digital Twin is designed to ensure both the optimal storage conditions of vaccines and the timely administration of vaccines to patients. The system achieves this by continuously monitoring vaccine storage temperatures and tracking vaccination schedules. The operational flow is outlined as follows: Equipment Monitoring : Refrigerators and thermal boxes are equipped with temperature sensors that continuously monitor and transmit the temperature. When the temperature falls below 3°C or exceeds 7°C, the system triggers an alert to prevent vaccine spoilage. Alerts are sent via SMS and WhatsApp to key stakeholders: the immunisation team, health centre manager, and local health authority. The alert message follows a standardized format: Alert: Temperature below 3°C or above 7°C. Please verify storage conditions. Vaccine Monitoring : Each vaccine is identified by its name (e.g., BCG, Hepatitis B) and type (e.g., routine or campaign). The system tracks the required number of doses and ensures that vaccination deadlines are met. Patient Tracking : Each patient profile includes their name, date of birth, and a list of vaccines they need to receive. The system monitors both administered and pending vaccines, ensuring that no dose is missed. In addition, vaccination alerts are generated for patients and directed to both the patient and the healthcare team, including the health unit manager. These alerts follow the format: Vaccination Alert: Patient (patient's name) must receive the (vaccine name) within (X number of days). The number of days for the alert will be determined based on the patient’s age group. For those under 1 year old, alerts are set for 7, 15, and 30 days prior to the vaccination deadline. For individuals over 1 year old, alerts are set for 30, 60, and 90 days before the expected vaccination date. Digital Twin Functionality : The Digital Twin serves as the core of the system, functioning as the "brain" that continuously monitors both temperature and vaccination schedules. In the event of temperature deviations or approaching vaccination deadlines, the Digital Twin sends alerts to the appropriate parties, prompting immediate action. Example 1 (Paediatric Patient: Alice) : Consider Alice, a one-month-old infant, who is scheduled to receive the BCG vaccine within 30 days of birth. The TwinVax system is monitoring the refrigerator that stores the BCG vaccine. If the temperature of the refrigerator falls to 2.8°C, the system immediately triggers an alert: Alert: Temperature below 3°C. Please verify storage conditions. This alert is sent via SMS and WhatsApp to the immunisation team, the unit manager, and the local responsible party. Simultaneously, TwinVax identifies that Alice’s vaccination deadline is approaching. As she must receive the BCG vaccine within the next 15 days, the system sends a second alert: Vaccination Alert: Alice must receive the BCG vaccine within 15 days! Both alerts are triggered simultaneously: the first relates to the preservation of the vaccine’s integrity, while the second ensures the timely administration of the vaccination. If the refrigerator temperature remains outside the ideal range (below 3°C), the team can act swiftly, ensuring the vaccine remains effective until it is administered. Given Alice's age, the vaccination alert is sent to her legal guardian (the responsible party) as she is a minor, as well as to the local healthcare team, accompanied by a suggested date for vaccination. Example 2 (Adult Patient: John) : Now, consider John, a 35-year-old adult, who is due to receive the tetanus vaccine. According to the vaccination schedule, John must receive the vaccine within the next 60 days. TwinVax is monitoring the refrigerator storing the tetanus vaccine. If the temperature of the refrigerator rises to 7.1°C, the system immediately triggers an alert: Alert: Temperature above 7°C. Please verify storage conditions. This alert is sent via SMS and WhatsApp to the immunisation team, the unit manager, and the local responsible party. Simultaneously, TwinVax verifies that John’s vaccination deadline is nearing. As he must receive the tetanus vaccine within 60 days, the system also sends a specific vaccination alert: Vaccination Alert: John must receive the tetanus vaccine within 60 days! These simultaneous alerts help ensure both the quality and safety of the vaccine, as well as serve as a reminder to the team and John regarding the vaccination schedule, preventing the dose from being missed or administered incorrectly. Since John is an adult, the vaccination alert is sent directly to him, as well as to the local healthcare team, again with a suggested vaccination date. In both examples, it is essential to note that: Temperature Alerts: The system detects any variation outside the optimal temperature range (3°C to 7°C), triggering immediate alerts to the responsible parties. This enables rapid corrective actions, such as verifying the functionality of the refrigerator or relocating the vaccine to another unit, ensuring the vaccine’s preservation. Vaccination Alerts: The system monitors the vaccination schedules of patients, sending alerts as the vaccination date approaches. In the case of minors, the alert is directed to the legal guardian (e.g., Alice’s parent), while for adults, it is sent to the patient (e.g., John). In both cases, the alert is also sent to the local healthcare team, accompanied by a suggested vaccination date. For Alice, the suggested vaccination date is 15 days prior to the deadline, while for John, it is 60 days prior to his vaccination date. Immediate Actions: The responsible parties can take immediate actions based on the alerts, including adjusting temperature settings or scheduling the vaccination appointment as required. The multi-alert mechanism ensures a prompt and effective response, preventing errors in vaccine administration and ensuring their quality. Based on these examples, it was possible to verify that the TwinVax system effectively ensured both vaccine integrity and timely vaccination. The system prevented temperature deviations while issuing alerts that ensured on-time vaccinations. This simulation confirmed the TwinVax system's reliability in real-world conditions, validating its ability to support successful immunisation programs. 5- DISCUSSION TwinVax was developed as a temperature management solution for immunisation services in primary healthcare centres, focusing on the rigorous control of thermal conditions for the storage of immunobiologicals. The system collects, stores, monitors, and visualises temperature data from sensors installed in Ice Lined Refrigerators (ILRs) and thermal boxes, while also tracking vaccination coverage through data extraction from information systems related to vaccine administration and vaccinated individuals. In designing TwinVax, several critical factors were considered. Data collection involves continuous temperature measurements, electronic health records (EHR) data, vaccination history, types of vaccines, and administration dates. Temperature data is collected hourly for five minutes, while EHR data is gathered at the end of each working day, with adjustments possible based on the healthcare team's needs. In critical situations, the frequency can be increased to provide more detailed real-time insights. The data collection is conducted through sensors connected to an ESP-32 device, which employs communication protocols such as MQTT or HTTP for data transfer. In cases of network unavailability, the ESP-32 temporarily stores data in a local database, such as SQLite, using a FIFO (first in-first out) buffer to maintain the sequence of records. Data transmission to the cloud can occur in hybrid mode, with regular batch uploads. Data storage preferably takes place in the cloud, utilising databases like InfluxDB or TimescaleDB, which offer scalability and easy access. When connectivity is compromised, local storage is used. The data is organised for streamlined access, adhering to retention and cleaning policies to ensure data integrity and compliance. Immunisation service actions are based on analyses conducted within the digital twin environment. Physical interventions may be necessary in critical cases, such as abrupt temperature fluctuations, with real-time notifications sent to healthcare teams via SMS and WhatsApp. Vaccination coverage is a key indicator of programme performance, assessed through registries, routine reports, and household surveys. Effective monitoring helps identify individuals in need of immunisation, enabling timely and informed decision-making. Additionally, automated alerts are generated to remind individuals of their vaccination deadlines, improving adherence to immunisation schedules. TwinVax employs interactive dashboards, such as Grafana, to provide real-time visualisation of storage conditions and vaccination coverage. The system stores historical data on temperature, vaccination records, and demographic information for analytical and reporting purposes. Visualisation tools include temperature graphs, alerts, and vaccination coverage data, with visual indicators reflecting current conditions. The system identifies individuals approaching their vaccination dates and generates timely alerts to improve adherence. Integration with EHRs enables proactive communication with patients through SMS reminders, reinforcing adherence to vaccination schedules. Additionally, TwinVax can be configured to support predictive analyses, such as forecasting vaccine demand and identifying storage equipment with recurring temperature deviations. Machine learning models, including decision trees and Support Vector Machines (SVMs), could enhance these capabilities by analysing historical trends and environmental factors. For healthcare professionals, TwinVax provides an intuitive interface for real-time monitoring of storage conditions and vaccination coverage, supporting evidence-based decision-making on temperature control and immunisation strategies. Despite its benefits, TwinVax faces challenges such as integrating heterogeneous data systems, connectivity issues in remote areas, and limitations in real-time data synchronisation. Adapting an industry-oriented architecture (ISO 23247) to the public health context also requires the simplification of concepts and terminologies to ensure effective implementation. Data security and privacy are significant challenges, particularly given stringent regulations such as the GDPR. TwinVax employs encryption, restricted access controls, and auditing mechanisms to ensure compliance. Training healthcare teams to utilise these tools effectively is another challenge, considering the learning curve associated with adopting new technologies. The sustainability and scalability of TwinVax depend on financial and technological resources, considering the costs of IoT infrastructure, cloud storage, and continuous maintenance. Governance and compliance are fundamental, with audits conducted to track and validate system actions, especially in critical situations. Data access is restricted to authorised users, with secure, auditable APIs facilitating interaction with external systems. Accurate data interpretation is crucial for effective vaccination strategies, enabling proactive interventions to improve coverage. Ethical considerations are also pertinent, ensuring the system benefits all populations equitably. Automated decisions should be supported by human oversight to consider social and cultural aspects. Transparency in data usage and clear communication with the public are essential for building trust in the technology and its role in advancing public health. Nonetheless, the TwinVax stands out as a robust and innovative solution for modernising primary healthcare, contributing to the efficiency of immunisation management and improving public health outcomes. 6- CONCLUSIONS TwinVax represents a transformative approach to immunisation services within primary healthcare, combining real-time temperature monitoring with vaccination coverage tracking. Its integration of IoT devices, cloud-based data storage, and advanced analytics ensures rigorous control over the thermal conditions necessary for vaccine preservation, while also facilitating timely, data-driven decisions in immunisation management. By leveraging continuous data collection from sensors and electronic health records, TwinVax enables proactive responses to critical situations, such as temperature deviations, through real-time alerts and interactive dashboards. The system’s predictive capabilities, supported by machine learning models, further enhance its utility by forecasting vaccine demand, identifying individuals at risk of non-adherence, and optimising resource allocation. The deployment of TwinVax marks a significant advancement in public health management, particularly in maintaining vaccine efficacy and minimising wastage. By defining minimum standards for vaccine storage and enabling scenario analyses to identify at-risk groups, it fosters a data-driven approach to immunisation strategies. This ensures vaccines are managed and administered safely, effectively, and efficiently, reducing risks to patients and healthcare systems alike. Despite challenges related to data integration, connectivity in remote areas, and compliance with stringent data security regulations, TwinVax demonstrates resilience through robust encryption protocols, secure data access controls, and comprehensive auditing mechanisms. Its adaptability to varying healthcare environments and its capacity to support both local and cloud-based operations underscore its scalability and sustainability. Ethical considerations surrounding automated decision-making are mitigated through human oversight and transparent data governance, ensuring equitable access and fostering public trust. Furthermore, the architecture's flexibility allows for the integration of additional modules, accommodating future technological advancements and expanding its applicability across healthcare domains. In conclusion, TwinVax exemplifies the potential of digital twin technology to revolutionise immunisation services. By enhancing vaccine safety, reducing wastage, and supporting proactive health management, it offers a robust, scalable solution aligned with the dynamic needs of health systems. Declarations Ethical approval and consent to participate International ethical legislation was respected. There was no need for approval by a research ethics committee because the study was carried out using data from Scientific Databases, which is publicly accessible, unrestricted and does not identify people in any way. Consent for publication Not applicable. Data availability The data used in this research are entirely hypothetical and have been generated solely for academic and experimental purposes. They do not reflect real information, official statistics, or any aspect of reality. Any resemblance to actual data is purely coincidental. The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Competing interests The authors declare no competing interests. Funding Not applicable. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors Author contributions L.W. – Leonardo de Oliveira El-Warrak V.M. - Victor Hugo Dias Macedo De Azevedo Costa C.M.F – Claudio Miceli de Farias L.W. conceptualized the paper. V.M. acquired the data. L.W. and V.M. conducted the data analysis. L.W. produced the figure and tables. L.W led the authorship of each draft and the final version of the manuscript. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6245899","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":441810441,"identity":"b23a341a-5655-48cb-b705-557118fa0544","order_by":0,"name":"Leonardo Oliveira El-Warrak","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYBACPhhDgr0BTPMzMBxgw6sFLivBcwBMSzYQr0UiAaaFgYAW9uZjEj/33JOXnPn44cMfDIclzBsPsD2uwKeF51iaZM+zYsPZ0mnGxjxALTIHDrAbnsGnRSLH7AbPgQTGedI5bNIMDIfrJIB+AbkOtxb5N2Y3/xxIsJ8neYZNEuQwwlokeMxuA21JnC3BA2QTpYUnLf23zIGE5Jk9IL8YpAO1HGw3xKeFn/3wYcM3BxJsZxw/DAyxCmsJCYnDxx7i04IGDIBY4iAJGqAWk6xjFIyCUTAKhjkAAFWkRuwtEeWnAAAAAElFTkSuQmCC","orcid":"","institution":"Federal University of Rio de Janeiro (UFRJ)","correspondingAuthor":true,"prefix":"","firstName":"Leonardo","middleName":"Oliveira","lastName":"El-Warrak","suffix":""},{"id":441810442,"identity":"0cc0f2bc-3c4b-4fb1-82a8-6db20528e1c9","order_by":1,"name":"Claudio Miceli de Farias","email":"","orcid":"","institution":"Federal University of Rio de Janeiro (UFRJ)","correspondingAuthor":false,"prefix":"","firstName":"Claudio","middleName":"Miceli","lastName":"de Farias","suffix":""},{"id":441810443,"identity":"e902d71c-872e-46b4-9b0b-650a4a86c1cd","order_by":2,"name":"Victor Hugo Dias Macedo Azevedo Costa","email":"","orcid":"","institution":"State University of Rio de Janeiro (UERJ)","correspondingAuthor":false,"prefix":"","firstName":"Victor","middleName":"Hugo Dias Macedo Azevedo","lastName":"Costa","suffix":""}],"badges":[],"createdAt":"2025-03-17 15:08:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6245899/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6245899/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80727550,"identity":"c91f5857-d456-44b5-a626-f6c857fa4eb7","added_by":"auto","created_at":"2025-04-16 12:03:09","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":40624,"visible":true,"origin":"","legend":"\u003cp\u003eMain components in Healthcare Process\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6245899/v1/def5f82defce87bd1b24f028.jpg"},{"id":80727551,"identity":"d896776a-3bf8-4a3a-83d3-dfa7af8eb083","added_by":"auto","created_at":"2025-04-16 12:03:10","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":98138,"visible":true,"origin":"","legend":"\u003cp\u003eIllustration of the Digital Twin named TwinVax\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6245899/v1/e13a1b529e36ff68c338d9b2.jpg"},{"id":80728632,"identity":"8ce59f2a-cc4f-4b68-a9dd-a740196a77a7","added_by":"auto","created_at":"2025-04-16 12:11:10","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":89569,"visible":true,"origin":"","legend":"\u003cp\u003eExample of Vaccination Schedule for children under 15 mouths - WHO\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6245899/v1/00728ec32b426b0b576681bc.jpg"},{"id":80729317,"identity":"334a2fcf-9f6b-4292-8ce1-97755a33f8e7","added_by":"auto","created_at":"2025-04-16 12:19:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":831927,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6245899/v1/7fe74251-0c47-44cb-8c0a-94ad5ea3581e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"TwinVax: Leveraging Digital Twin Simulation to Monitor Vaccine Storage and Population Immunisation","fulltext":[{"header":"Contributions to the literature","content":"\u003cul\u003e\n \u003cli\u003eDT appears as one of most discussed technology applications within the Digital Health trend.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDigital twins hold the potential to transform healthcare, especially in the management and operational efficiency of healthcare services.\u003c/li\u003e\n \u003cli\u003eThe use of digital twins in primary health care is still poorly explored but has great potential for improving the population\u0026apos;s health indicators,\u0026nbsp;especially across developing countries.\u003c/li\u003e\n \u003cli\u003eThe use of digital twins in immunisation could support the recovery of vaccination coverage.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1- INTRODUCTION","content":"\u003cp\u003eGlobal health has faced unprecedented challenges in recent decades, with the COVID-19 pandemic pushing healthcare systems to their operational limits. This crisis exposed critical vulnerabilities, including an exponential surge in patient volume, shortages of essential resources, technical gaps in healthcare workforce preparedness, and an overload of fragmented or inaccurate information that impeded effective decision-making. Despite immunisation being one of the most impactful public health interventions, the pandemic severely disrupted routine vaccination programs worldwide, particularly in 2020 and 2021, causing significant setbacks in disease prevention efforts [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFaced with persistent challenges in controlling vaccine-preventable diseases, technological advances have emerged as a vital ally. New technologies offer a solid basis for faster and more effective responses during health crises, while simultaneously improving the general health of populations under normal circumstances. By integrating these solutions into health systems, it is possible to enable a more precise and consistent approach by health teams, improve resource allocation, and strengthen the health response capacity of healthcare systems [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Vaccination remains the cornerstone of disease prevention, offering robust protection against vaccine-preventable illnesses without the risks associated with natural infection. It not only shields individuals from severe disease outcomes but also curtails the spread of pathogens within communities. Achieving high vaccination coverage fosters herd immunity, effectively interrupting disease transmission chains. Beyond health outcomes, vaccination programs yield extensive societal benefits by reducing healthcare burdens, mitigating economic losses, and enhancing community well-being [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe healthcare sector is a dynamic landscape of technological innovation, consistently integrating new tools to enhance service delivery and patient outcomes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Vaccines, however, are sensitive biological products that require stringent temperature control to maintain their efficacy. Failures in cold chain management\u0026mdash;from storage to transportation\u0026mdash;remain a leading cause of vaccine spoilage and wastage. In this context, advanced technologies such as the Internet of Things (IoT) and Digital Twins (DT) offer transformative potential. These technologies enhance real-time monitoring, predictive maintenance, and data-driven decision-making, particularly in settings with complex operational challenges.\u003c/p\u003e \u003cp\u003eThis paper addresses two critical issues in immunisation programs: (i) vaccine loss due to inadequate temperature control in storage equipment, such as Ice-Lined Refrigerators (ILRs) and thermal boxes, and (ii) suboptimal vaccination coverage in specific target populations. To tackle these challenges, we propose a conceptual framework for a Digital Twin (DT) tailored to support immunisation services within primary healthcare settings. Through simulation, we aim to demonstrate how this DT\u0026mdash;referred to as TwinVax\u0026mdash;can optimise vaccine storage conditions via real-time temperature monitoring and enhance immunisation coverage through dynamic data analytics, ultimately supporting timely and evidence-based public health decision-making.\u003c/p\u003e"},{"header":"2- RELATED STUDIES","content":"\u003cp\u003eTechnological innovations play a central role in shaping health systems, influencing how services are delivered and the outcomes achieved. These innovations encompass a wide range of technologies used for prevention, diagnosis, treatment, and rehabilitation, including vaccines, diagnostic kits, medications, medical equipment, and procedures. The continuous advancement of technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), 5G, and Big Data has enabled real-time data collection, processing, and storage, fostering new opportunities for healthcare management.\u003c/p\u003e \u003cp\u003eChaudhari, Gangane, and Lahe (2021) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] highlight how Digital Twin (DT) technology enhances digital health monitoring within Industry 4.0 concepts, describing DTs as real-time virtual replicas of physical objects that enable continuous health tracking and predictive analytics through IoT and AI integration. Katsoulakis et al. (2024) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] explore DT applications in healthcare, emphasizing their role in personalizing treatments and improving patient outcomes through real-time data monitoring and computational models. Similarly, Bj\u0026ouml;rnsson et al. (2019) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] discuss DTs as tools for personalized medicine, using patient-specific biological, clinical, and behavioral data to simulate and predict treatment responses.\u003c/p\u003e \u003cp\u003eStahlberg et al. (2022) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] examine predictive DTs for cancer patients, focusing on integrating patient data, AI, and computational models to simulate disease progression. The study highlights challenges related to data interoperability, model validation, and ethical concerns, while reinforcing DTs' potential to enhance clinical decision-making in oncology. Popa et al. (2021) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] analyze DTs from a socioethical perspective, recognizing their benefits in personalized care and decision support but also addressing concerns around data privacy, patient autonomy, and biases in predictive models.\u003c/p\u003e \u003cp\u003eSahal et al. (2022) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] investigate the role of emerging technologies\u0026mdash;including DTs, blockchain, IoT, and AI\u0026mdash;in managing pandemic crises. They propose a blockchain-based framework for decentralized epidemic alerts, stressing the need for secure, real-time data exchange to support COVID-19 response efforts. El-Warrak and Miceli (2024) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] categorize DT applications in healthcare into clinical and operational domains, covering personalized care, simulation of biological structures, process optimization, and resource management. Despite challenges related to data integration and privacy, DTs show great potential to improve healthcare quality, remote monitoring, prevention, and decision-making.\u003c/p\u003e \u003cp\u003eUddin et al. (2016) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] report on an mHealth intervention in Bangladesh using the \"mTika\" app to improve vaccination rates in hard-to-reach populations. The initiative significantly increased coverage through electronic birth registration and vaccination reminders, demonstrating mHealth\u0026rsquo;s effectiveness despite challenges like data standardization and limited follow-up capacity. Demsash et al. (2023) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] apply ML algorithms to predict childhood vaccination coverage in Ethiopia, identifying key predictors such as maternal education and healthcare access, though limitations in statistical interpretability were noted.\u003c/p\u003e \u003cp\u003eIn Brazil, Ribeiro (2022) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] proposes a DT architecture to modernize the National Vaccination Plan, enhancing resource management through real-time simulations. In a subsequent study, Ribeiro (2023) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] introduces a maturity model to assess public healthcare units' readiness for DT implementation, identifying critical factors such as information security, logistics, and organizational management.\u003c/p\u003e \u003cp\u003eThe literature review reveals key insights into DT applications in healthcare. Most studies remain theoretical, focusing on opportunities and challenges with limited real-world implementations for evaluation. Some works address Digital Shadows or Digital Models, lacking real-time data exchange, which limits the exploration of DTs\u0026rsquo; full potential, such as influencing physical systems through intelligent analysis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Additionally, DT studies often focus on isolated assets without considering the broader healthcare ecosystem [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Many rely on simulations disconnected from real systems, neglecting aspects of integration, interoperability, and human involvement [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Finally, while DT functionalities are widely discussed, concrete architectures or methodologies for DT implementation in Primary Health Care are still scarce.\u003c/p\u003e"},{"header":"3- METHODS","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 - MODELLING THE DIGITAL TWIN IN IMMUNISATION\u003c/h2\u003e \u003cp\u003eThe incorporation of digital models as an integral part of studying and developing physical objects is well-established. Virtual prototyping combines digital technology with engineering and design principles, enabling the efficient and precise creation and refinement of physical products and systems. The ability to virtually simulate and analyse objects before physical production has proven invaluable across various sectors, including manufacturing, engineering, architecture, and medicine. This approach offers benefits such as time savings, cost reductions, and optimized product performance and functionality [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA model is a representation of a real system, used to conduct simulation studies. To ensure the quality of information derived from the simulation, all elements relevant to capturing essential data from the real system must be incorporated [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The use of Discrete Event Simulation (DES) in this study is justified by the nature of the data required for modelling the digital twin. DES operates with discrete values that change at specific points in time, allowing clear transitions between states. Additionally, in DES, all data, entities, and activities are identifiable once the model is finalised, enabling a chronological understanding of events.\u003c/p\u003e \u003cp\u003eThe core of a DT consists of virtual models, making the development of high-precision digital representations essential. These models must accurately capture the physical properties, behaviours, and governing rules of the real object. The creation of a digital twin involves two primary aspects: (i) developing the processes and information requirements of the DT throughout the product life cycle\u0026mdash;from asset design to real-world deployment and maintenance; and (ii) implementing the enabling technology to integrate the physical asset with its digital counterpart. This integration ensures real-time sensor data flow, along with operational and transactional information from the organization's core systems, as outlined in a conceptual architecture.\u003c/p\u003e \u003cp\u003eA digital twin is characterized by four key features: Modelling and Simulation, Real-time Data Integration, Analysis and Optimization, and Insights and Action. Modelling provides a detailed representation of the physical system, encompassing attributes such as mechanical, electrical, and operational properties. Simulation allows testing under diverse conditions to predict behaviour in real-world scenarios. Real-time data integration, powered by IoT sensors, enhances simulation accuracy, and enables early issue detection. Additionally, advanced analytics and machine learning techniques can uncover patterns, predict failures, and suggest improvements.\u003c/p\u003e \u003cp\u003eReal-time or near-real-time physical data updates are critical for refining virtual models and accurately simulating physical processes and their evolution. The network plays a vital role in connecting the physical object (PO) to its virtual representation (VO), enabling real-time data exchange. This connection supports bidirectional communication, where data from the PO updates the virtual model, and insights from the virtual model inform decisions in the physical system. Communication between PO and VO typically involves three stages [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]: (i) collecting data through direct measurement of physical conditions, (ii) processing and interpreting data at the appropriate level of abstraction, and (iii) updating system states with integrated data from multiple sources. The interface linking the real process to the DT, represented by the simulation model, varies depending on the characteristics of the simulation software and the connectivity capabilities of the physical system.\u003c/p\u003e \u003cp\u003eA two-phase approach can be employed for predictive modelling in health service management. The first phase involves offline model development, utilizing Machine Learning (ML) and Deep Learning (DL) techniques, such as classifiers, to train the model with historical data from the DT. In this controlled environment, accuracy is improved before real-time deployment. The second phase consists of deploying the trained model online, closer to the data source, to minimize latency and optimize performance. By leveraging real-time streaming data, the model can quickly detect potential risks, enabling rapid responses and necessary adjustments. This two-phase approach integrates extensive historical data analysis with the agility of real-time processing, ensuring a reliable predictive model for proactive health service management. Optimisation is achieved by refining parameters in the digital model and implementing best practices in the physical system. Insights generated by the DT facilitate continuous improvements in healthcare service delivery [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eProcess modelling and simulation are invaluable tools across multiple domains. Recent advances in Industry 4.0, Big Data, IoT, and Sensor Technology have expanded their application in Digital Twins (DT). In this context, process models have evolved from passive tools for hypothesis testing into active components of operational systems. With efficient infrastructures and advanced algorithms, these models can monitor and reflect real-time system states while autonomously executing corrective actions when necessary.\u003c/p\u003e \u003cp\u003eThe methodology presented builds upon the framework introduced in [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], originally designed for dynamic modelling in discrete industrial processes. Given the specific needs of immunisation systems\u0026mdash;such as vaccine cold chain management and coverage monitoring\u0026mdash;this approach has been adapted for healthcare, enabling digital twin applications in immunisation services. The goal is to optimise vaccine storage temperature monitoring and enhance vaccination coverage assessment.\u003c/p\u003e \u003cp\u003eDigital modelling consolidates critical information into a computational environment, allowing analysis and prediction of issues such as temperature fluctuations, vaccine loss risks, and gaps in immunisation coverage. While static digital models do not perform real-time physical simulations, they provide a robust foundation for identifying trends, assessing risks, and supporting decision-making processes. The static models within the DT for immunisation are utilized to evaluate cold chain integrity and predict the potential impacts of storage system failures. This includes cross-referencing historical temperature data with distribution patterns to detect risks that may compromise vaccine efficacy. Additionally, digital modelling supports continuous assessment of vaccination coverage, enabling targeted immunisation campaign planning.\u003c/p\u003e \u003cp\u003eAlthough static digital models do not capture real-time environmental changes, their continuous updates with sensor data and vaccination records enhance predictive accuracy and risk identification. By integrating data-driven insights into immunisation management, the DT strengthens proactive decision-making and supports the resilience of vaccination programs.\u003c/p\u003e \u003cp\u003eFor modelling, immunisation services in Primary Healthcare Centre can be divided into seven main components or entities, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. These components include patients, health human resources, facilities, equipment, health supplies, processes, and partners [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe patients' component includes various types of patients, categorized by age groups, health histories, and specific needs, such as those with acute and chronic diseases, disabilities, or immunological risks. The healthcare human resources encompass nurses, technicians, and operational staff. Healthcare facilities cover the immunization room, waiting area, and staff offices. Equipment pertains to all medical devices, IT infrastructure, and furniture. Healthcare supplies are divided into physical and service supplies. Physical supplies include vaccines, medications, drugs, lab materials, cleaning supplies, treatment materials, and other essentials for maintaining healthcare facilities and equipment. Service supplies consist of crucial services received from partners, such as maintenance for medical equipment, catering for staff, patients, and visitors, and utilities like energy and water. Processes include procedures for treating patients with immunobiologicals, managing medical emergencies, organising the vaccine room, staff scheduling, recording information in systems, inventory monitoring of vaccines, supply chain management, workflow optimization, and other operational processes. Partners include suppliers of equipment and consumables, hospitals, specialized healthcare centres, and others.\u003c/p\u003e \u003cp\u003eDigital Twins can be created for all these components. They use data from healthcare facilities, equipment, processes, patients with various needs, supplies, and partners, compiling real-time information from sensors, health information systems, such as electronic medical records (EMR), electronic health records (EHR) and other sources to create digital replicas. For example, digital counterparts can be developed for healthcare facilities such as X-ray rooms and other healthcare processes such as treatment and logistics processes.\u003c/p\u003e \u003cp\u003eOn the other hand, creating DTs of patients presents one of the most complex challenges in healthcare due to the need to represent diverse patient characteristics such as age, gender, health history, current health status, and healthcare needs. Studies such as those by [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] have explored the main design requirements and enabling technologies of digital patient twins, as well as the technical challenges present. The complexity involved stems from multiple levels of abstraction, different types of patients, numerous environmental factors, and continuous and rapid changes in healthcare data. Patient digital twins can be developed with varying levels of detail depending on their purpose. For example, refined models can reflect real-time health and environmental information from individual patients, supporting personalized medical services. For most applications in immunization services, these detailed individual models are extremely valid. However, even if there is no detailed health analysis, the immunization service also benefits from an abstract view of aggregated patient data to support high-level decision-making, improving overall efficiency, quality, access, and the cost-effectiveness of vaccination. This model comprises (i) a patient information database populated with clinical data from multiple sources; (ii) a cloud computing platform; (iii) traceability systems using AI; and (iv) blockchain technology. Other components, such as human resources, facilities, and equipment, are less complex and can be generalized based on their specific characteristics. For instance, a digital twin of a nurse would focus on their schedule, work location, and skills, rather than individual traits. Similarly, digital twins for facilities and equipment are relatively static and can be periodically updated as needed.\u003c/p\u003e \u003cp\u003eThis study will focus on three components of the represented system: the equipment used for vaccine storage, the vaccines themselves, and the patients. For the equipment entity, the monitoring of operational conditions will be based on the temperature attribute. In the case of the vaccine entity, monitoring will be conducted based on the type and number of doses, while for patients, it will pertain to their vaccination schedule and history. The digital twin will function by providing alerts regarding variations in the ideal temperature conditions for storage that may jeopardize the vaccine's efficacy, as well as alerts for timely vaccination needs for patients.\u003c/p\u003e \u003cp\u003eFurthermore, the Digital Twin should propose scenario analyses for individuals and/or groups who may delay or miss certain vaccinations. Additionally, considering the vaccination needs according to the vaccination schedule of the target public, the DT could also estimate the ideal quantity of vaccines to be available in the immunisation room. In this work, the events of interest will include the temperature measurements collected by sensors and the vaccines administered to patients by dose and type, as recorded in the information systems designed for this purpose.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 - ARCHITECTURE BASED ON ISO 23247 - THE TWINVAX\u003c/h2\u003e \u003cp\u003eIn this work, the ISO 23247 standard for Digital Twin (DT) frameworks, originally designed for manufacturing, is adapted to a healthcare context. The proposed DT architecture focuses on temperature monitoring through a 2D dashboard and predictive models for anomaly detection and temperature forecasting. It also includes a module for tracking administered vaccine doses by type and dose, enabling cross-referencing with population vaccine needs to support preventive vaccination actions. The DT\u0026rsquo;s functionalities, aligned with ISO 23247, range from monitoring and remote access to simulation, control, optimisation, and predictive analysis, ensuring effective feedback for both users and equipment operations.\u003c/p\u003e \u003cp\u003eObserving the possibility of integration between IoT architectures and the digital twin modelling proposed in ISO 23247 and adapting them to a more simplified and understandable form for healthcare, a 4-layer architecture is proposed, capable of implementing immunisation DT, here, named TwinVax, as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe TwinVax architecture is structured into four interconnected domains: Observable Manufacturing Elements (OME), Data Collection and Device Control Entity (DCDCE), Digital Twin Platform (DTP), and User Domain (UD). This layered approach ensures efficient data collection, processing, and utilisation for immunisation management.\u003c/p\u003e \u003cp\u003e \u003cb\u003eOBSERVABLE MANUFACTURING ELEMENTS (OME)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe Observable Manufacturing Elements (OME) domain corresponds to the physical entities layer, which includes the Ice Lined Refrigerators (ILR) and thermal boxes used for vaccine storage. Within these elements, temperature data will be collected through sensors, which are also integral to the monitoring system. Additionally, the target population for immunisation is considered, with their vaccination data being managed and monitored by the system. To configure the digital twin, information from Electronic Health Records (EHR) including Electronic Medical Records (EMR) and specific Immunisation Information Systems regarding patient data and vaccination records will be integrated. It is important to emphasise that the sensors must adhere to technical standards compatible with industrial communication, such as OPC UA, MQTT, and HTTP, ensuring clear modelling of physical entities and efficient data exchange.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDATA COLLECTION AND DEVICE CONTROL ENTITY (DCDCE)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe second layer corresponds to the connection layer, which encompasses the domain of data collection and control of actuating devices present in the previous Observable Manufacturing Elements (OME) layer. This layer facilitates communication and data transfer between the sensors and the digital twin. Data extraction is performed by temperature sensors (e.g., DS18B20, DHT11, or LM35DZ), with initial transformation occurring through local processing, where a device converts electrical signals into data transferable via standard IoT integration protocols.\u003c/p\u003e \u003cp\u003eAs the connection layer is based on IoT architecture, temperature sensors will be installed in Ice Lined Refrigerators (ILRs) and thermal boxes to detect variations outside the ideal temperature range, maintained between 2\u0026deg;C and 8\u0026deg;C. These sensors will communicate with the edge layer using the 802.15.4 protocol. Temperature data will be collected once per hour for 5 minutes, contextualized, and analysed by an algorithm to identify potential issues related to the loss of immunogenic potency due to inadequate storage conditions. Proactive temperature management will be ensured by triggering alerts when temperatures exceed 7\u0026deg;C or fall below 3\u0026deg;C, serving as early warnings that allow preventive actions before reaching critical limits.\u003c/p\u003e \u003cp\u003eThe device responsible for transmitting the temperature data to the cloud is the ESP-32 (WROOM-D model), chosen for its strong connectivity features. The ESP-32 is programmed to collect data from the temperature sensors via GPIO pins and transmit this information using Wi-Fi. A server-side application developed in Node.js runs directly on the ESP-32, enabling data processing and transmission via lightweight protocols such as MQTT or \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eHTTP.An\u003c/span\u003e\u003cspan address=\"http://HTTP.An\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e MQTT broker receives the temperature data, processes it, and makes it available for authorized subscribers. Additionally, a Node-RED service running on the ESP-32 Gateway acts as an MQTT subscriber, collecting data from the broker and securely transmitting it to the cloud, facilitating real-time monitoring of refrigeration conditions, visualisation of temperature history, and automated notifications in case of deviations.\u003c/p\u003e \u003cp\u003eFor enhanced data security, Transport Layer Security (TLS) encryption is applied to all communication between devices, preventing interception or unauthorized modifications. Furthermore, encryption at rest is implemented in storage systems to protect sensitive data.\u003c/p\u003e \u003cp\u003eIn addition to the IoT-based monitoring layer, the architecture integrates electronic health records and vaccination registries through standardized interoperability protocols. Data exchange between the Electronic Health Record (EHR), the vaccination registration system, and the digital twin follows the HL7 (Health Level 7) standard, ensuring structured and secure information flow. Both RESTful APIs and GraphQL can be implemented for data synchronization, allowing healthcare professionals to retrieve and manage clinical data and vaccination records efficiently.\u003c/p\u003e \u003cp\u003eGiven the critical nature of healthcare data, additional security measures are applied to prevent unauthorized access. OAuth 2.0 authentication and Advanced Encryption Standard (AES) encryption ensure that only authorized users can access and manipulate stored information, safeguarding patient confidentiality. Data transmission is also secured using TLS, aligning with industry best practices and regulatory frameworks such as GDPR.\u003c/p\u003e \u003cp\u003eThe entire system infrastructure is hosted in the cloud, ensuring scalability, reliability, and compliance with security standards while allowing seamless integration of IoT data and healthcare information. The choice of cloud provider is flexible and can be adapted based on specific deployment requirements. This combination of IoT monitoring, secure data transmission, and interoperability between healthcare systems ensures an efficient and proactive approach to vaccine storage and management.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDIGITAL TWIN PLATFORM (DTP)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAt the core of TwinVax, the Digital Twin Platform (DTP) layer is responsible for managing application services, data processing, and system operations. This layer supports critical functions such as data analysis, aggregation, rule application, and storage, ensuring the seamless operation of the TwinVax system. To achieve robust performance and scalability, the architecture leverages Amazon Web Services (AWS), utilizing services like AWS IoT SiteWise and Node-RED for efficient data processing, transformation, and presentation.\u003c/p\u003e \u003cp\u003eCommunication within the DTP is streamlined using the MQTT protocol, which optimizes IoT data transmission by reducing the computational load on devices, especially low-power ones such as the ESP-32. This efficiency is crucial for maintaining real-time responsiveness while conserving device resources. The data transformation process involves converting raw data collected from sensors and health records into actionable metrics. This includes calculating average daily temperatures and assessing vaccination coverage rates across different population segments, providing valuable insights for immunisation management.\u003c/p\u003e \u003cp\u003eFor data storage, the DTP employs time-series databases like InfluxDB or TimescaleDB. These databases are specifically designed to handle continuous monitoring data efficiently, supporting the long-term management and analysis of large volumes of time-stamped data generated by vaccine storage monitoring and immunisation tracking.\u003c/p\u003e \u003cp\u003eScalability is a fundamental consideration for TwinVax, particularly when expanding to larger healthcare networks or national health systems. To support this, the architecture adopts a microservices approach, allowing individual system components\u0026mdash;such as temperature monitoring, vaccine management, and patient records\u0026mdash;to be updated, scaled, and maintained independently. This modularity enhances system flexibility and simplifies maintenance without disrupting overall operations. Additionally, the elasticity of cloud services enables dynamic resource allocation, automatically adjusting computing and storage capacity to meet fluctuating data demands. In scenarios with high data traffic, the use of content distribution networks (CDNs) and load balancing mechanisms ensures efficient data delivery, system reliability, and high availability, even under peak load conditions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eUSER DOMAIN (UD)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis layer is designed to support healthcare professionals and managers by providing access to data visualisation and decision-support tools. It includes services such as temperature monitoring dashboards, predictive vaccination analyses, and automated notifications. The data visualisation aspect relies on interactive dashboards, such as Grafana, which display real-time information with colour-coded indicators\u0026mdash;green for normal conditions, yellow for alerts, and red for emergencies. Historical data trends are also available, enabling quick and clear monitoring of the situation.\u003c/p\u003e \u003cp\u003eAdditionally, predictive analyses help forecast vaccine demand and identify individuals who are due for immunisation, supporting proactive planning. To ensure timely communication, the system includes SMS alerts for staff and patients, push notifications through mobile apps, and on-site visual alarms. These elements together create a comprehensive architecture, allowing for effective vaccine management by enhancing real-time monitoring, forecasting, and data-driven decision-making in immunisation programs.\u003c/p\u003e \u003c/div\u003e"},{"header":"4- RESULTS","content":"\u003cp\u003eTo demonstrate the practical applicability of TwinVax, based on the proposed modelling and architecture, an environment for simulating discrete events using Python - SimPy - was employed. SimPy allows for modelling complex systems involving concurrent processes, such as queues, waiting times, and interactions between different entities over time. In this study, SimPy was used to simulate the operation of TwinVax, enabling the evaluation of system behaviour under various conditions, such as temperature variations in storage equipment and fluctuations in vaccination coverage. This validation step ensures that temperature alerts and real-time vaccination coverage analyses function as expected before actual implementation.\u003c/p\u003e \u003cp\u003eThe simulation was conducted using the WHO vaccination schedule for 2024 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and focused on a population of 3.527 residents of Rio de Janeiro, aged between 1 and 90 years. To ensure the robustness and accuracy of the simulation, additional steps were taken to refine the representation of the population. Stratified sampling was employed to capture the diverse age distribution within the community. Age groups were carefully defined to align with established vaccination schedules and guidelines, ensuring that the simulation accurately reflects the real-world distribution of vaccines and their coverage needs.\u003c/p\u003e \u003cp\u003eSpecifically, the age groups were selected to represent the distinct vaccination schedules for infants, children, adults, and the elderly, each with different vaccination requirements and intervals. This stratification allows the system to simulate varying vaccination coverage and test TwinVax\u0026rsquo;s ability to manage vaccines across different stages of life.\u003c/p\u003e \u003cp\u003eThe stratified sampling divided the population into age groups, with selections made proportionally within each group. This resulted in a sample of 100 individuals, in line with statistical requirements for a 95% confidence level and a 10% margin of error. The following age groups were considered in the stratification: under 1 year (3 individuals), 1\u0026ndash;6 years (7 individuals), 7\u0026ndash;9 years (3 individuals), 10\u0026ndash;19 years (8 individuals), 20\u0026ndash;39 years (23 individuals), 40\u0026ndash;59 years (28 individuals), and 60\u0026thinsp;+\u0026thinsp;years (28 individuals).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe TwinVax Digital Twin is designed to ensure both the optimal storage conditions of vaccines and the timely administration of vaccines to patients. The system achieves this by continuously monitoring vaccine storage temperatures and tracking vaccination schedules. The operational flow is outlined as follows:\u003c/p\u003e \u003cp\u003e \u003cb\u003eEquipment Monitoring\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eRefrigerators and thermal boxes are equipped with temperature sensors that continuously monitor and transmit the temperature. When the temperature falls below 3\u0026deg;C or exceeds 7\u0026deg;C, the system triggers an alert to prevent vaccine spoilage. Alerts are sent via SMS and WhatsApp to key stakeholders: the immunisation team, health centre manager, and local health authority. The alert message follows a standardized format:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAlert: Temperature below 3\u0026deg;C or above 7\u0026deg;C. Please verify storage conditions.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eVaccine Monitoring\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eEach vaccine is identified by its name (e.g., BCG, Hepatitis B) and type (e.g., routine or campaign). The system tracks the required number of doses and ensures that vaccination deadlines are met.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePatient Tracking\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eEach patient profile includes their name, date of birth, and a list of vaccines they need to receive. The system monitors both administered and pending vaccines, ensuring that no dose is missed. In addition, vaccination alerts are generated for patients and directed to both the patient and the healthcare team, including the health unit manager. These alerts follow the format:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eVaccination Alert: Patient (patient's name) must receive the (vaccine name) within (X number of days).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe number of days for the alert will be determined based on the patient\u0026rsquo;s age group. For those under 1 year old, alerts are set for 7, 15, and 30 days prior to the vaccination deadline. For individuals over 1 year old, alerts are set for 30, 60, and 90 days before the expected vaccination date.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDigital Twin Functionality\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eThe Digital Twin serves as the core of the system, functioning as the \"brain\" that continuously monitors both temperature and vaccination schedules. In the event of temperature deviations or approaching vaccination deadlines, the Digital Twin sends alerts to the appropriate parties, prompting immediate action.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eExample 1\u003c/strong\u003e \u003cp\u003e \u003cb\u003e(Paediatric Patient: Alice)\u003c/b\u003e:\u003c/p\u003e \u003c/p\u003e \u003cp\u003eConsider Alice, a one-month-old infant, who is scheduled to receive the BCG vaccine within 30 days of birth. The TwinVax system is monitoring the refrigerator that stores the BCG vaccine. If the temperature of the refrigerator falls to 2.8\u0026deg;C, the system immediately triggers an alert:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAlert: Temperature below 3\u0026deg;C. Please verify storage conditions.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThis alert is sent via SMS and WhatsApp to the immunisation team, the unit manager, and the local responsible party. Simultaneously, TwinVax identifies that Alice\u0026rsquo;s vaccination deadline is approaching. As she must receive the BCG vaccine within the next 15 days, the system sends a second alert:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eVaccination Alert: Alice must receive the BCG vaccine within 15 days!\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eBoth alerts are triggered simultaneously: the first relates to the preservation of the vaccine\u0026rsquo;s integrity, while the second ensures the timely administration of the vaccination. If the refrigerator temperature remains outside the ideal range (below 3\u0026deg;C), the team can act swiftly, ensuring the vaccine remains effective until it is administered. Given Alice's age, the vaccination alert is sent to her legal guardian (the responsible party) as she is a minor, as well as to the local healthcare team, accompanied by a suggested date for vaccination.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eExample 2\u003c/strong\u003e \u003cp\u003e \u003cb\u003e(Adult Patient: John)\u003c/b\u003e:\u003c/p\u003e \u003c/p\u003e \u003cp\u003eNow, consider John, a 35-year-old adult, who is due to receive the tetanus vaccine. According to the vaccination schedule, John must receive the vaccine within the next 60 days. TwinVax is monitoring the refrigerator storing the tetanus vaccine. If the temperature of the refrigerator rises to 7.1\u0026deg;C, the system immediately triggers an alert:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAlert: Temperature above 7\u0026deg;C. Please verify storage conditions.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThis alert is sent via SMS and WhatsApp to the immunisation team, the unit manager, and the local responsible party. Simultaneously, TwinVax verifies that John\u0026rsquo;s vaccination deadline is nearing. As he must receive the tetanus vaccine within 60 days, the system also sends a specific vaccination alert:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eVaccination Alert: John must receive the tetanus vaccine within 60 days!\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThese simultaneous alerts help ensure both the quality and safety of the vaccine, as well as serve as a reminder to the team and John regarding the vaccination schedule, preventing the dose from being missed or administered incorrectly. Since John is an adult, the vaccination alert is sent directly to him, as well as to the local healthcare team, again with a suggested vaccination date.\u003c/p\u003e \u003cp\u003eIn both examples, it is essential to note that:\u003c/p\u003e \u003cp\u003eTemperature Alerts: The system detects any variation outside the optimal temperature range (3\u0026deg;C to 7\u0026deg;C), triggering immediate alerts to the responsible parties. This enables rapid corrective actions, such as verifying the functionality of the refrigerator or relocating the vaccine to another unit, ensuring the vaccine\u0026rsquo;s preservation.\u003c/p\u003e \u003cp\u003eVaccination Alerts: The system monitors the vaccination schedules of patients, sending alerts as the vaccination date approaches. In the case of minors, the alert is directed to the legal guardian (e.g., Alice\u0026rsquo;s parent), while for adults, it is sent to the patient (e.g., John). In both cases, the alert is also sent to the local healthcare team, accompanied by a suggested vaccination date. For Alice, the suggested vaccination date is 15 days prior to the deadline, while for John, it is 60 days prior to his vaccination date.\u003c/p\u003e \u003cp\u003eImmediate Actions: The responsible parties can take immediate actions based on the alerts, including adjusting temperature settings or scheduling the vaccination appointment as required. The multi-alert mechanism ensures a prompt and effective response, preventing errors in vaccine administration and ensuring their quality.\u003c/p\u003e \u003cp\u003eBased on these examples, it was possible to verify that the TwinVax system effectively ensured both vaccine integrity and timely vaccination. The system prevented temperature deviations while issuing alerts that ensured on-time vaccinations. This simulation confirmed the TwinVax system's reliability in real-world conditions, validating its ability to support successful immunisation programs.\u003c/p\u003e"},{"header":"5- DISCUSSION","content":"\u003cp\u003eTwinVax was developed as a temperature management solution for immunisation services in primary healthcare centres, focusing on the rigorous control of thermal conditions for the storage of immunobiologicals. The system collects, stores, monitors, and visualises temperature data from sensors installed in Ice Lined Refrigerators (ILRs) and thermal boxes, while also tracking vaccination coverage through data extraction from information systems related to vaccine administration and vaccinated individuals.\u003c/p\u003e \u003cp\u003eIn designing TwinVax, several critical factors were considered. Data collection involves continuous temperature measurements, electronic health records (EHR) data, vaccination history, types of vaccines, and administration dates. Temperature data is collected hourly for five minutes, while EHR data is gathered at the end of each working day, with adjustments possible based on the healthcare team's needs. In critical situations, the frequency can be increased to provide more detailed real-time insights.\u003c/p\u003e \u003cp\u003eThe data collection is conducted through sensors connected to an ESP-32 device, which employs communication protocols such as MQTT or HTTP for data transfer. In cases of network unavailability, the ESP-32 temporarily stores data in a local database, such as SQLite, using a FIFO (first in-first out) buffer to maintain the sequence of records. Data transmission to the cloud can occur in hybrid mode, with regular batch uploads.\u003c/p\u003e \u003cp\u003eData storage preferably takes place in the cloud, utilising databases like InfluxDB or TimescaleDB, which offer scalability and easy access. When connectivity is compromised, local storage is used. The data is organised for streamlined access, adhering to retention and cleaning policies to ensure data integrity and compliance.\u003c/p\u003e \u003cp\u003eImmunisation service actions are based on analyses conducted within the digital twin environment. Physical interventions may be necessary in critical cases, such as abrupt temperature fluctuations, with real-time notifications sent to healthcare teams via SMS and WhatsApp. Vaccination coverage is a key indicator of programme performance, assessed through registries, routine reports, and household surveys. Effective monitoring helps identify individuals in need of immunisation, enabling timely and informed decision-making. Additionally, automated alerts are generated to remind individuals of their vaccination deadlines, improving adherence to immunisation schedules.\u003c/p\u003e \u003cp\u003eTwinVax employs interactive dashboards, such as Grafana, to provide real-time visualisation of storage conditions and vaccination coverage. The system stores historical data on temperature, vaccination records, and demographic information for analytical and reporting purposes. Visualisation tools include temperature graphs, alerts, and vaccination coverage data, with visual indicators reflecting current conditions.\u003c/p\u003e \u003cp\u003eThe system identifies individuals approaching their vaccination dates and generates timely alerts to improve adherence. Integration with EHRs enables proactive communication with patients through SMS reminders, reinforcing adherence to vaccination schedules. Additionally, TwinVax can be configured to support predictive analyses, such as forecasting vaccine demand and identifying storage equipment with recurring temperature deviations. Machine learning models, including decision trees and Support Vector Machines (SVMs), could enhance these capabilities by analysing historical trends and environmental factors. For healthcare professionals, TwinVax provides an intuitive interface for real-time monitoring of storage conditions and vaccination coverage, supporting evidence-based decision-making on temperature control and immunisation strategies.\u003c/p\u003e \u003cp\u003eDespite its benefits, TwinVax faces challenges such as integrating heterogeneous data systems, connectivity issues in remote areas, and limitations in real-time data synchronisation. Adapting an industry-oriented architecture (ISO 23247) to the public health context also requires the simplification of concepts and terminologies to ensure effective implementation.\u003c/p\u003e \u003cp\u003eData security and privacy are significant challenges, particularly given stringent regulations such as the GDPR. TwinVax employs encryption, restricted access controls, and auditing mechanisms to ensure compliance. Training healthcare teams to utilise these tools effectively is another challenge, considering the learning curve associated with adopting new technologies.\u003c/p\u003e \u003cp\u003eThe sustainability and scalability of TwinVax depend on financial and technological resources, considering the costs of IoT infrastructure, cloud storage, and continuous maintenance.\u003c/p\u003e \u003cp\u003eGovernance and compliance are fundamental, with audits conducted to track and validate system actions, especially in critical situations. Data access is restricted to authorised users, with secure, auditable APIs facilitating interaction with external systems. Accurate data interpretation is crucial for effective vaccination strategies, enabling proactive interventions to improve coverage. Ethical considerations are also pertinent, ensuring the system benefits all populations equitably. Automated decisions should be supported by human oversight to consider social and cultural aspects. Transparency in data usage and clear communication with the public are essential for building trust in the technology and its role in advancing public health.\u003c/p\u003e \u003cp\u003eNonetheless, the TwinVax stands out as a robust and innovative solution for modernising primary healthcare, contributing to the efficiency of immunisation management and improving public health outcomes.\u003c/p\u003e"},{"header":"6- CONCLUSIONS","content":"\u003cp\u003eTwinVax represents a transformative approach to immunisation services within primary healthcare, combining real-time temperature monitoring with vaccination coverage tracking. Its integration of IoT devices, cloud-based data storage, and advanced analytics ensures rigorous control over the thermal conditions necessary for vaccine preservation, while also facilitating timely, data-driven decisions in immunisation management.\u003c/p\u003e \u003cp\u003eBy leveraging continuous data collection from sensors and electronic health records, TwinVax enables proactive responses to critical situations, such as temperature deviations, through real-time alerts and interactive dashboards. The system\u0026rsquo;s predictive capabilities, supported by machine learning models, further enhance its utility by forecasting vaccine demand, identifying individuals at risk of non-adherence, and optimising resource allocation.\u003c/p\u003e \u003cp\u003eThe deployment of TwinVax marks a significant advancement in public health management, particularly in maintaining vaccine efficacy and minimising wastage. By defining minimum standards for vaccine storage and enabling scenario analyses to identify at-risk groups, it fosters a data-driven approach to immunisation strategies. This ensures vaccines are managed and administered safely, effectively, and efficiently, reducing risks to patients and healthcare systems alike.\u003c/p\u003e \u003cp\u003eDespite challenges related to data integration, connectivity in remote areas, and compliance with stringent data security regulations, TwinVax demonstrates resilience through robust encryption protocols, secure data access controls, and comprehensive auditing mechanisms. Its adaptability to varying healthcare environments and its capacity to support both local and cloud-based operations underscore its scalability and sustainability.\u003c/p\u003e \u003cp\u003eEthical considerations surrounding automated decision-making are mitigated through human oversight and transparent data governance, ensuring equitable access and fostering public trust. Furthermore, the architecture's flexibility allows for the integration of additional modules, accommodating future technological advancements and expanding its applicability across healthcare domains.\u003c/p\u003e \u003cp\u003eIn conclusion, TwinVax exemplifies the potential of digital twin technology to revolutionise immunisation services. By enhancing vaccine safety, reducing wastage, and supporting proactive health management, it offers a robust, scalable solution aligned with the dynamic needs of health systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInternational ethical legislation was respected. There was no need for approval by a research ethics committee because the study was carried out using data from Scientific Databases, which is publicly accessible, unrestricted and does not identify people in any way.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this research are entirely hypothetical and have been generated solely for academic and experimental purposes. They do not reflect real information, official statistics, or any aspect of reality. Any resemblance to actual data is purely coincidental. The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eL.W. – Leonardo de Oliveira El-Warrak\u003c/p\u003e\n\u003cp\u003eV.M. - Victor Hugo Dias Macedo De Azevedo Costa\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eC.M.F – Claudio Miceli de Farias\u003c/p\u003e\n\u003cp\u003eL.W. conceptualized the paper. V.M. acquired the data. L.W. and V.M. conducted the data analysis. L.W. produced the figure and tables. L.W led the authorship of each draft and the final version of the manuscript. L.W., V.M. and C.M.F reviewed and revised each draft of the manuscript and provided intellectual content. All authors read and approved of the final manuscript. L.W. acts as guarantor for this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHopkins KL, Underwood T, Iddrisu I, Woldemeskel H, Bon HB, Brouwers S, De Almeida S, Fol N, Malhotra A, Prasad S, Bharadwaj S, Bhatnagar A, Knobler S, Lihemo G. Community-Based Approaches to Increase COVID-19 Vaccine Uptake and Demand: Lessons Learned from Four UNICEF-Supported Interventions. Vaccines (Basel). 2023 June 30;11(7):1180. doi: 10.3390/vaccines11071180. PMID: 37514996; PMCID: PMC10384848.\u003c/li\u003e\n\u003cli\u003eWeston, B. W., Swingen, Z. N., Gramann, S., \u0026amp; Pojar, D. 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Mag. of Global Internet Wkg. 37, 2 (March/April 2023), 262\u0026ndash;269. https://doi.org/10.1109/MNET.118.2200071\u003cu\u003e.\u003c/u\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Immunisation, Digital Twin, Vaccines, TwinVax, Simulation, IoT","lastPublishedDoi":"10.21203/rs.3.rs-6245899/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6245899/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis paper presents the application of simulation to assess the functionality of a proposed Digital Twin (DT) architecture for immunisation services in primary healthcare centres. The solution is based on Industry 4.0 concepts and technologies, such as IoT, machine learning, and cloud computing, and adheres to the ISO 23247 standard.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe system modelling is carried out using the Unified Modelling Language (UML) to define the workflows and processes involved, including vaccine storage temperature monitoring and population vaccination status tracking. The proposed architecture is structured into four domains: observable elements/entities, data collection and device control, digital twin platform, and user domain. To validate the system's performance and feasibility, simulations are conducted using SimPy, enabling the evaluation of its response under various operational scenarios.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe system facilitates the storage, monitoring, and visualisation of data related to the thermal conditions of ice-lined refrigerators (ILR) and thermal boxes. Additionally, it analyses patient vaccination coverage based on the official immunisation schedule. The key benefits include optimising vaccine storage conditions, reducing dose wastage, continuously monitoring immunisation coverage, and supporting strategic vaccination planning.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe paper discusses the future impacts of this approach on immunisation management and its scalability for diverse public health contexts. By leveraging advanced technologies and simulation, this digital twin framework aims to improve the performance and overall impact of immunization services.\u003c/p\u003e","manuscriptTitle":"TwinVax: Leveraging Digital Twin Simulation to Monitor Vaccine Storage and Population Immunisation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-16 12:03:05","doi":"10.21203/rs.3.rs-6245899/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dd9b7772-f7d6-44a7-a190-437ae9f16bb8","owner":[],"postedDate":"April 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47045795,"name":"Biological sciences/Immunology/Vaccines"},{"id":47045796,"name":"Health sciences/Health care"},{"id":47045797,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2025-04-16T12:03:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-16 12:03:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6245899","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6245899","identity":"rs-6245899","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}