Global impact and surveillance strategies of omicron SARSCoV-2: exploring IoT applications and prevention approaches

This research highlights the importance of Internet of Things (IoT) technology in reducing the spread of the Omicron variant of COVID-19. In November 2021, the World Health Organization (WHO) reported the emergence of the SARS-CoV-2 Omicron variant (B.1.1.529) in South Africa. Despite ongoing preventive efforts, the pandemic continues to spread, and the rapid transmissibility of Omicron suggests that additional variants may emerge. This research discusses several SARS-CoV-2 variations, examines the dynamics and structure of omicron transmission, and offers insights into global immunization campaigns. Furthermore, it addresses several IoT applications, such as temperature sensors in crowded areas for quick identification and isolation of sick individuals, digital telehealth remote doctor-patient interactions, guidance for IoT-assisted ambulances by remote medical experts, virus spread prediction, and health monitoring devices. The deployment of biosensors, artificial intelligence, and the Internet of Medical Things (IoMT) for COVID-19 and Zika virus detection was also reviewed. Wearable sensor integration in pandemic healthcare has been comprehensively assessed for IoT-based detection and protection approaches. IoT applications include hotspot detection, real-time health analysis, remote administration of medication, medical device tracking, and ambulance monitoring was also analysed. This article analyses consumer-orientated IoT gadgets such as smartwatches, smart glasses, drones, and autonomous swab test robots to support public health initiatives. It also emphasizes their function in identifying, monitoring, and responding to the Omicron cases. The IoT plays a preventive role by improving real-time observation, supporting prompt action, and reducing face-to-face encounters, which helps contain the spread of Omicron. Moreover, IoT-based systems can track patients in home isolation, ensuring adherence to quarantine rules.

Coronavirus Disease (COVID-19, 2023) is caused by the SARS-CoV-2 virus. It was discovered in December 2019 in the Wuhan region of China (Gautam and Hens, 2022a). Later, coronavirus disease rapidly spread worldwide, and within a month, several people were affected by it (Gautam, 2020). SARS-CoV-2 has mutated into several variants. The WHO grouped these variances into three groups for observation and research purposes. Variants are classified as variants under monitoring (VUMs), variants of concern (VOCs), and variants of interest (VOIs). The variants of concern (VOCs) designated by the WHO are Beta (B.1.351), Delta (B.1.617.2), Alpha (B.1.1.7), and Gamma (P.1). Omicron (B.1.1.529) was designated as the fifth VOC by the WHO, exacerbating issues globally (Gautam and Hens, 2022b). The Internet of Things (IoT) is a new, cutting-edge technology that uses a network of sensors to send data and automate activities without human participation. The IoT provides real-time monitoring, early detection, quick response, and data-driven decision-making, all of which help limit and control the spread of the Omicron SARS-CoV-2 strain (Irkham et al., 2022).

The article examines the effects of the SARS-CoV-2 Omicron variant across different countries and highlights possible strategies for prevention.

Although measures are in place, the pandemic continues to expand, and Omicron’s swift spread raises concerns about future variants.

This article also highlights the crucial role of Internet of Things (IoT) technology in preventing the Omicron COVID-19 mutant from spreading.

IoT applications encompass tracking quarantined individuals, identifying hotspots, real-time health analysis, remote medication, medical device tracking, and ambulance monitoring.

Motivation

The primary motivation and novelty of this research are to address the urgent need for intelligent, real-time, and scalable surveillance systems that can handle highly transmissible SARS-CoV-2 variants, such as Omicron, for which conventional healthcare infrastructure is unsuitable. Sensor-equipped IoT devices can continually measure health indicators, such as blood oxygen levels (SpO 2), body temperature, and respiration rate, and send the data to healthcare systems for early infection diagnosis. Wearable biosensors and smart thermometers can immediately detect fever and other symptoms. Autonomous contact tracking is made possible by IoT technologies, particularly Bluetooth-capable gadgets and smartphone apps, which detect when two devices or, consequently, two individuals are brought into proximity to one another. Cloud and fog computing systems can combine data from numerous IoT devices to examine patterns and identify disease hotspots. Additionally, it aids public health decision-making and forecasts outbreaks. Patients with moderate symptoms or those who isolate themselves at home can be followed remotely through IoT-enabled telemedicine and remote patient monitoring. IoT is a potent tool for limiting the spread of COVID-19 variants, such as Omicron, because it improves public health responses by delivering immediate, real-time data and automating preventive interventions.

The primary objectives of this study are as follows: Analyse the Omicron SARS-CoV-2 variant’s structural characteristics and transmission dynamics with respect to its global spread.

The role of IoT technologies in monitoring and detecting Omicron infections in crowded and public settings was examined.

Explore IoT-enabled healthcare applications, such as telemedicine, remote patient monitoring, and ambulance services with IoT support.

Outline IoT-assisted global immunization and preventive strategies.

Prior work

Several researchers have used various approaches to create models for IoT in the medical field and disease forecasting. IoT-based healthcare reduces human error and makes disease diagnosis easier for clinicians. The accessibility of intelligent technology and the increasing risks of infectious diseases due to worldwide expansion make the use of IoT in the epidemiology of viral infections essential for the prediction, prevention, and control of infectious diseases. Viral illness modelling and pathogen identification have advanced as a result of the recent use of web-based monitoring tools in several countries to quickly diagnose infected patients and precisely forecast the potential incidence of viral infections (Aladdin et al., 2025). Almotiri et al. (2016) suggested an m-health system that uses wearable technology and mobile devices to gather patient data in real time for medical diagnosis. Barger et al. (2005) used sensor networks to assess a smart home prototype that tracks patient movements. Chiuchisan et al. (2014) presented a strategy for patient safety in intelligent intensive care units, warning caregivers of health disparities.

To enhance patient care and service accessibility, Tyagi et al. (2016) examined IoT components and suggested a cloud-based framework for safe medical data transfer. With an emphasis on monitoring multiple body parameters, Gupta et al. (2017) proposed a framework that utilizes GSM and Bluetooth technologies to expedite data transmission during ambulatory and hospital services. The increasing number of chronic illnesses and the growing demand for real-time health monitoring have led to significant research and development activities in IoT-based health monitoring systems. Although current remote patient monitoring solutions exhibit potential, they still need to be improved in crucial areas, including measurement accuracy, data security, system connectivity, and data storage capacity. To detect deviations, this article introduces a novel approach that combines customized baseline construction with various sensor fusions.

Contribution

This article systematically addresses the rapid global spread of the Omicron SARS-CoV-2 variant and the drawbacks of conventional pandemic control strategies. The pandemic persists despite continuous worldwide immunization campaigns, exposing the shortcomings of traditional healthcare and surveillance methods. This study presents IoT technology as a practical solution for large-scale surveillance and real-time monitoring to address these challenges. The vaccination status, COVID-19 active cases, recovery cases, and fatality cases for various nations through February 2025 are also reported in this article. Subsequently, IoT technology is proposed as a solution for remote healthcare, early detection, and real-time surveillance. Several IoT applications and smart devices have been developed for monitoring, prediction, and contactless responses. In contrast to earlier IoT–COVID surveys, this study focuses exclusively on the Omicron variant and its developing sub-lineages, avoiding a general discussion of COVID-19. It combines IoT-enabled surveillance techniques with variant-specific epidemiological findings, emphasizing that Omicron’s high transmission and immune evasion features require flexible digital health interventions. This study concludes that by enabling rapid response and reducing physical contact, IoT-based technologies improve preventive control.

SARS-CoV-2 variants

The Alpha version (B.1.1.7) is a SARS-CoV-2 variant of concern that was found in November 2020, mainly across the United Kingdom. This variant emerged as more transmissible and spread in mid-December 2020 at the same time as the surge of COVID-19. The alpha variant had many mutations and was predicted to be 30%–50% more contagious than other SARS-CoV-2 variants. In March 2021, the Alpha variant increased rapidly in many United States states and has been identified in 114 countries (Andre et al., 2023).

A SARS-CoV-2 variant of concern, referred to as the Beta variant (B.1.351), was found in South Africa in October 2020. This variant, 501Y.V2 acquired more mutations in South Africa and became contagious faster (Mwang et al., 2022). On 10 January 2021, Japanese health officials reported the discovery of the SARS-CoV-2 Gamma variant (P.1) from Brazil. This variant was widely spread in the northern city of Manaus, Brazil (Kirsebom et al., 2022; Dong and Yao, 2020).

In December 2020, the SARS-CoV-2 Delta variant (B.1.617.2) was first identified in India. This variant is highly contagious than the earlier variants, and it is predicted to be 50% more contagious. Figure 1 depicts the spread of several omicron strains worldwide since August 2024 (Covid-vaccinations, 2024). The distribution of main Omicron sub-variants by country, which is based on publicly accessible genomic surveillance data (Covid-vaccinations, 2024). Across several places, there is a steady change in dominance toward later lineages like XBB and, EG.5.1. The variation in prevalence between nations reveals variations in immunity, surveillance rigour, and transmission dynamics. In February 2021, the delta variant played a vital role in the second wave of the pandemic in India. It later spread as the third wave of the pandemic, mainly in China, the United Kingdom, and Russia.

The Omicron (B.1.1.529) variant is a highly transmissible virus that the WHO has classified as a variant of concern. The biological characteristics, such as lineage of Pango, GISAID clade, originating country, spike mutations, rapidity of spread, and various blood analyses of SARS-COV-2 variants, are shown in Table 1. The most common symptoms of SARS-COV-2 variations include fatigue, headache, body ache, fever, muscle ache, cold, difficulty breathing, lack of smell, chills, and stomach problems (Meo et al., 2021). The SARS-CoV2 genome of the omicron variant contains 18,261 mutations, with 97% occurring within the coding area and 558 occurring in the extragenic genome region (Bansal and Kumar, 2022).

Omicron structure

The omicron variant is more contagious than the beta and delta variants. The omicron variant has many non-synonymous mutations in the omicron variant. In addition, the omicron variant contains many spike mutations that affect immune evasion, disease severity and transmission. SARS-CoV-2-related viruses, known as “variants of concern,” are those in which the receptor-binding domain (RBD) spike protein has undergone changes that significantly increase the binding affinity in the RBDhACE2 complex and are associated with rapid transmission within human societies. Figure 6 in Ren et al. (2022) shows the Receptor-Binding Domain (RBD) for the Omicron variant and the spike protein structure of the Omicron variant. According to various alignments, the Omicron variant has 30 mutations, most of which are in the spike protein, with 50% coming from the RBD.

Approximately 60 substitutions, omissions, and insertions of omicron variants have already been identified, and the SARS-CoV-2 variation with the most mutated areas has been examined. The RBD region of omicron has 15 mutations, which is a critical target for neutralizing antibodies. The six mutations in ORF1a are (K856R, I3758V, L2084I, P3395H, A2710T, and T3255I). Moreover, the omicron variant has two omissions totalling four amino acids, whereas ORF1b has two substitutions (I1566V and P314L). The envelope (E) protein contains a single variation (T9I), and the membrane (M) protein includes three changes (Q19E, A63T, and D3G). Moreover, the nucleocapsid (N) protein has three variations and three residue deletions. The total number of Omicron mutations that have been found was collected in a spike, which included 30 mutations, namely P681, A67V, Y145D, T95I, G339D, S371L, L212I, K417N, S375F, S373P, G446S, N440K, E484A, S477N, G496S, Q493R, T478K, N679K, Y505H, T547K, H655Y, Q498R, D614G, N501Y, and G142/V143/Y144, H69/V70, N211 has three deletions (Gupta et al., 2017). The results of a novel cryo-electron microscopy (Cryo-EM) structural study of the SARS-CoV-2 omicron spike protein associated with human ACE2 were fascinating. The beta, alpha, and gamma variants share the N501Y mutation. The N501Y variant has been considered to enhance transmissibility and improve spike-angiotensin converting enzyme 2 (ACE2) binding. Omicron spike mutations, such as P681H, N501Y, D614G, K417N, and E484 residue alterations, have been identified in more VOC variants. These alterations are associated with increased pathogenicity, enhanced transmissibility, and reduced ability to evade neutralizing monoclonal antibodies. Additionally, they are associated with increased ACE2 binding affinity.

Transmissibility of omicron variant

The Omicron variant spread globally in a short period, and the number of cases rapidly increased. There is a rising concern regarding its transmissibility. However, an early South African report revealed that the Omicron strain might transmit much faster from one individual to another. Scientists can only reach conclusions in a short time. As shown in Figure 2, as of 12 August 2024, the omicron variant spread mainly to France, Italy, the United Kingdom, the United States, Spain, Japan, and China (Covid-vaccinations, 2024). In early 2022, Omicron rapidly took the lead globally and has been very prevalent ever since, with only minor variations caused by new sub-lineages. International discrepancies reflect variations in immunity levels, sequencing coverage, and transmission dynamics.

According to the Centre’s for Disease Control and Prevention (CDC), the omicron variant capacity in the United States grew by 2.5% in less than 2 weeks. In New York, the infection rate increased by 13%, and omicron cases doubled every 2–3 days in Britain. In December 2021, the infection rate in South Africa rose from 10.3% to 24.3%, which was faster than that in other countries. Smart wearables have been designed for multiple uses in diverse areas, including fitness, healthcare, and relaxation, to avert the COVID-19 pandemic (Govindaraj et al., 2023; Dhanasekar and Subash Nageshwar, 2022).

Omicron infection cures most people by causing extreme fever, physical fatigue, and body aches for several days. (Araf et al., 2022). As of 01 June 2022, the United Kingdom had more cases, that is, approximately 1.2 million patients (COVID-19, 2023). Globally, the coronavirus has been detected in 220 nations and territories. This respiratory virus has spread across six continents, killing approximately 6.87 million people. The virus has infected over 687 million people worldwide, of whom 660 million have recovered. France, Italy, and the United States are among the countries that have been heavily affected.

Omicron sub variants

The World Health Organization predicts that Omicron has three descendant sub-lineages: BA.1, BA.2, and BA.3. The omicron BA.1 subvariant began spreading on 10 January 2022, and gradually replaced the Delta variation in Maharashtra, India. Despite multiple defining variants of the omicron variant, the predominant variant nationwide was the BA.1 subvariant omicron. Stealth omicron also refers to a new BA.2 subvariant. It was detected in Sweden, India, and Norway in January 2022, and has spread to more than 40 countries. Compared to BA.1, the BA.2 subvariant is more contagious and has been claimed to not even be detected in the RTPCR test. Two subvariants of BA.2 Omicron, BA2.10 and BA2.12, were found in Bangalore, India, in April 2022. These new variants spread to Haryana and Uttar Pradesh in India in April 2022. BA.2.12.1 variant has been rapidly spreading, and by June 2022, it will most undoubtedly be the dominant virus strain, primarily across the United States This form descended from BA.2. However, the cause of this variant remains undetermined. The omicron BA.2 lineage spread quicker than the initial BA.1 mutation. Unvaccinated people, booster-dose-vaccinated people, and double-vaccinated individuals were more vulnerable to BA.2 infection than BA.1 infection. A high transmissibility rate of BA.2 among unvaccinated people suggests that it has other characteristics besides immune evasion.

Currently, Omicron BA.5 and BA.4 variants are being investigated. The antibodies produced in non-vaccinated people were eight times lower than those of Omicron BA.5 and BA.4. Antibodies are high in vaccinated people. Vaccinated people have five times more immunity than unvaccinated people to destroy the coronavirus. Only 30% of the South African population is vaccinated. Researchers have warned that Corona will enter the 5th wave sooner than South Africa expected. According to researchers, Omicron variants BA.5 and BA.4 will cause the next waves.

The three types of hybrid versions are XD, XE, and XF, in addition to the above-mentioned Omicron variants. XD combines Delta and Omicron BA.1. It was detected in France, Denmark, and Belgium and was widely identified in the United Kingdom in April 2022. The WHO also issued a warning about a new variant called XE. Until recently, the Omicron BA.2 variant was the fastest spreading variant. XE is a hybrid variant of the two mutated variants, BA.1 and BA.2. This XE omicron variant could spread approximately ten times faster than the Omicron BA.2 variant. However, this mutated variant has only been observed in a few individuals worldwide. According to the WHO, XE mutant corona (BA.1 & BA.2) was initially found mainly in the United Kingdom around 19 January 2022. However, only 600 people have been diagnosed with this new variant. The third type, XF, combines Delta and Omicron BA.2. It has spread to some parts of the United Kingdom.

Preventions measures of omicron variants

The omicron variant’s specific properties of the omicron variant have yet to be discovered. Although the Omicron variant has developed a greater ability for human spread, it is becoming less resistant to conventional antibody treatments. This highlights the need to improve public health precautions, including the use of masks, regular ventilation, isolation, and handwashing. These approaches will help prevent the transmissibility of the omicron and other dangerous variants. Furthermore, during pandemics, initial diagnosis and timely quarantine are essential for avoiding virus transmission (He et al., 2021). A large part of the population must be immune to the disease to stop the epidemic. Vaccination is the most effective method for preventing pandemics.

Vaccines have been used to help reduce the number of people dying from infectious diseases. Several research groups have developed vaccines to protect people against SARS-CoV-2. Figure 3 shows the number of people fully and partially vaccinated against COVID-19 worldwide. In India, 951.99 million people were fully vaccinated as of 12 August 2024 (Covid-vaccinations, 2024). Most countries showed an increase in vaccination rates in 2021–2022, but there were still discernible regional differences. These differences indicate variations in public acceptance, vaccine accessibility, and distribution effectiveness.

In Estonia, only 2.11 million people were fully vaccinated. Vaccination coverage in this country is substantially lower than the global average of 58%. This highlights the urgent need for improved vaccination coverage in Estonia, which may accelerate the transmission of omicron across the country. In numerous studies on SARS-CoV-2, serum-neutralizing antibodies began to decline 6 months after immunization, indicating the need for booster shots to maintain immunity levels. Figure 4 shows that the initial and booster vaccine statuses improved globally on 12 August 2024 (Covid-vaccinations, 2024). It uses public datasets, such as vaccines delivered, population coverage, full vaccination, and booster uptake, to compare the progress of COVID-19 vaccination between nations. In 2021, there was a rapid expansion, followed by stabilization, with developing nations having greater coverage than emerging ones. These discrepancies reflect variations in immunization strategies, healthcare capabilities, and vaccine access.

Almost 72.5% of people in India have completed their initial vaccination doses, and 17% have received booster doses as of 12 August 2024. In Africa, 6.9% of people have been vaccinated with booster doses as of 12 August 2024. COVID-19 booster vaccines and other countries have started using booster doses. Consequently, the administration of a booster vaccine reduced omicron prevalence of Omicron. Figure 5 shows the human morality count due to SAR-Cov-2 globally on 23 February 2025 (Covid-vaccinations, 2024). The temporal patterns of COVID-19 new cases and fatalities per million across nations based on public epidemiology datasets (Covid-vaccinations, 2024). There are multiple waves of illness, and over time, incidence increases typically exceed related mortality peaks. The divergence points to the effects of increased population immunity, better treatment, and immunization coverage.

On 23 August 2021, the Food and Drug Administration (FDA) in the United States approved the Pfizer-BioNTech vaccine for individuals aged 16 years and above. The Pfizer-BioNTech vaccine was promoted and named COMIRNATY after the FDA approval. Figure 6 shows the efficiency of the COVID-19 vaccine Pfizer-BioNTech towards the omicron variant following two doses (Accorsi et al., 2022). When comparing the delta variant, immunization effectiveness for persons vaccinated with the Pfizer-BioNTech dose for the second time was good. Vaccination effectiveness against acute illness of BA.2 and BA.1 following 2 weeks after obtaining the double amount was 63.6% (58.8%–67.8%) and 67.1% (54.2%–76.3%), respectively, as shown in Figure 7 as on 24 March 2022 (Accorsi et al., 2022). After 25 weeks, this decreased to 17.4% (15.2%–19.4%) and 24.3% (20.3%–28.0%) of BA.1 as well as BA.2, respectively. After 1 week, BA.1 and BA.2 vaccination effectiveness increased to 71.3% (69.6%–72.9%) and 72.2% (67.0%–76.5%), respectively. After receiving the booster dose within a minimum of 15 weeks, this decreased to 45.5% (43.8%–47.2%) and 48.4% (45.2%–51.4%), respectively. People vaccinated with all three doses of the mRNA vaccine had good protection against Omicron and Delta variants compared to 2nd dose vaccinated and unvaccinated individuals.

Role of IoT in mitigating omicron variant of COVID-19

The advent of the SARS-CoV-2 omicron variant, which causes COVID-19, was reported in November 2021 and originated between South Africa and Botswana. It rapidly attained global dominance as the prevailing variant. This variant is recognized for its heightened transmissibility compared to SARS-CoV-2 variants, albeit typically leading to less severe illness (Alam, 2023). The role of IoT in mitigating the omicron variant is crucial because fast detection and isolation of the affected person is necessary. A solution for this was proposed in figure 27 of Kumar et al. (2020). Temperature data from different sensors are collected from other crowded locations and transferred to a big data warehouse through a cloud gateway, where meaningful data about the infected person are extracted and further isolation actions can be taken.

Digital telehealth played a pivotal role in the containment of the omicron variant. Establishing a portal enables patients to remotely consult doctors for appropriate treatment, thereby minimizing direct doctor-patient contact and reducing omicron transmission. IoT-enabled ambulances (Figure 8) allow remote medical experts to advise on patient care, ensuring swift responses and effective treatment. Furthermore, IoT can assist in predicting potential virus spread using Artificial Intelligence, issuing warnings to unaffected individuals about areas susceptible to future outbreaks (Kamal et al., 2020).

An IoT-based health monitoring device can also be developed to monitor temperature and SpO2 data and send them to the cloud for further processing. These data and critical situations can be viewed using a customized application. A flowchart for an IoT-based health monitoring device is depicted in Figure 9 (Bhardwaj et al., 2022).

The COVICT framework, as proposed in (Wahid et al., 2023), addresses contact tracing and identification of probable COVID-19 cases to prevent virus transmission. This framework encompasses several vital components, such as data gathering and uploading, a symptom analysis data centre, a cloud database, a healthcare centre, an isolation and quarantine centre, a contact tracing centre for positive cases, a national command and control centre, and the industry. The sensor layer collects data from various sources, including biosensors embedded in wearable devices and onboard smartphone sensors, such as cameras, microphones, and inertial sensors. These sensors can detect symptoms such as fever, shortness of breath, sore throat, and headache. The collected data were then transmitted to the Internet via an Internet gateway.

Wahid et al. (2023) described in figure 3 an IoT-based architecture for COVID detection. The movement of COVID-19 patient data between various networks with a cloud database is controlled by an edge device. Its responsibilities include the optimization, buffering, and processing of patient data in real time. Subsequently, the data are stored in a cloud database, accessible to a data analysis centre for further processing. The processed results are then communicated to remote healthcare physicians at healthcare centres for clinical evaluation. If a positive case is confirmed, healthcare officials use the patient’s travel history, which has been transmitted to the Data Centre, to follow the patient’s location and take measures to mitigate the spread of the virus.

The applications of IoT to mitigate the omicron variant of COVID-19 can be summarized as follows (Kumar et al., 2021): The locations of quarantined individuals can be tracked. This will warn the health department and government about the potential spread of the virus. The IoT can help identify future and current hotspots using machine learning algorithms. Real-time health analysis of patients using IoT can help in better and faster recovery. Remote medication administration prevents the spread of viruses among doctors. The location of medical devices is tracked to make better decisions about the places that need those devices. For real-time monitoring of ambulance locations to effectively utilize the infrastructure in the event of an outbreak. Severe COVID-19 symptoms of acute respiratory distress and pneumonia may be more likely in people with blood disorders and long-term heart and lung problems (Malin Bruntha et al., 2023; Jubair Ahmed et al., 2022). Table 2 compares the mainstream sensors used for COVID-19 monitoring.

Consumer-centric IoT devices are also a potential solution for mitigating the omicron Variant of COVID-19 and as described in (Castiglione et al., 2021). The smartwatch-type thermometer is worn on the wrist and continuously monitors the temperature. Similar to any other health tracking device, it can be paired with the consumer’s mobile phone and show the data using a customized application. The government can then use the abnormal temperature data to track potential omicron patients and control the spread. Furthermore, smart glasses equipped with IR and optical cameras capture the body temperature of individuals in a crowd and use face recognition to facilitate tracking. The recorded data can be managed on a smartphone and sent to health officers for further analysis. These glasses are beneficial for identifying and avoiding certain people to minimize the risk of being affected by the omicron variant. Another device is intelligent drones. These drones are equipped with medical supplies and can record temperature data from crowds.

These drones can provide first aid quickly in any emergency and help health officers identify infected persons, enabling rapid isolation. Autonomous Swab Test Robots enable testing patients for COVID without human interaction, preventing the spread of the virus. These robots can assist health workers, reducing their stress and risk of infection.

Strengths and limitations

IoT-enabled sensors and applications can quickly identify and notify those who may have come into contact with infected people, and the ongoing monitoring of vital health indicators, such as heart rate, temperature, and oxygen saturation, enables the early detection of symptoms. AI and machine learning models can be used to anticipate possible epidemics, optimize resource utilization, and inform efficient policy decisions using data collected from IoT devices. Furthermore, through cold-chain monitoring, IoT is essential for preserving vaccination effectiveness and guaranteeing accurate medical supply tracing. IoT networks offer an effective and scalable method for addressing public health issues, providing widespread coverage with minimal human involvement. In healthcare, IoT solutions struggle to protect sensitive data from privacy and security threats, and the expense of setting up devices and infrastructure remains a significant barrier. Due to restricted access and inadequate connectivity, their influence is reduced in rural or low-income areas. Misdiagnosis or panic may result from inaccurate sensor data, and platform interoperability issues further impede smooth operation. Biosensors are used in wearable technology, such as fitness bands and smartwatches, allowing for ongoing, informal vital sign monitoring and early identification of aberrant physiological patterns linked to COVID-19 symptoms; however, their accuracy is limited by sensor noise and variation among individuals.

Temperature and environmental sensors in public areas allow for quick widespread screening without physical contact and support the development of automatic alerts for prompt isolation (Hayat et al., 2019). Nevertheless, using symptom-driven sensors such as temperature, SpO 2, cough or symptom-based screening devices, we are unable to detect pre-symptomatic or asymptomatic individuals (Kumar et al., 2020; Mukhtar et al., 2021). Systems for remote consultation and telehealth mitigate the danger of physical contact and transmission and reduce the strain on hospitals during infection outbreaks; however, they necessitate reliable Internet access and digital knowledge. IoT-Assisted Ambulance and Emergency Response Systems include high implementation and maintenance costs, interoperability problems between ambulance and hospital systems, and real-time patient assessment during transit, which helps hospitals prepare for new patients (Tarique et al., 2025). Furthermore, widespread acceptance is challenging because of the dependence on user compliance and the requirement for ongoing maintenance and scalability.

Data privacy, security, and encryption in IoT-Based health monitoring systems

To ensure the confidentiality, availability, and integrity of sensitive healthcare data collected by IoT devices, a secure surveillance system for IoT healthcare data in the cloud must be developed. The widespread use of IoT devices in healthcare creates serious issues with data privacy, safe transmission, and safeguarding sensitive medical information, especially when tracking and managing the spread of highly contagious variations such as Omicron SARS-CoV-2 (Gupta et al., 2017).

Data privacy challenges

IoT-based health systems are appealing targets for hackers because they constantly gather, share, and process personal information, such as oxygen saturation, body temperature, location data, heart rate, and medical records. Ensuring security, integrity, and accessibility is crucial because these data can be categorized as personal health information (PHI). The lack of patient permission, data usage transparency, and data leakage during wireless transmission are the main privacy concerns (Stergiou et al., 2023). IoT healthcare frameworks should include privacy-preserving techniques such as pseudonymization, data anonymization, and access control measures to reduce these threats.

Encryption technologies for secure data transmission

Encryption is essential for safeguarding IoT health data during transmission and storage. Asymmetric encryption has been employed for reliable key exchange and identification among IoT devices and cloud servers, whereas symmetric encryption is used for securing real-time sensor data because of its low computational overhead and compatibility with IoT devices with limited resources (Ullah et al., 2021). Furthermore, End-to-End Encryption (E2EE) prevents unauthorized parties from intercepting health data by ensuring that it is encrypted from the IoT device to the appropriate medical professional.

Secure data storage and access control

As cloud-based infrastructures commonly store IoT healthcare data, secure storage methods are essential. Effective strategies to secure data storage and access control include multi-factor authentication (MFA) for medical personnel, encrypted cloud storage with role-based access control (RBAC), and blockchain-based data storage for transparent and impenetrable record management (Kamble et al., 2025).

IoT technologies have greatly improved Omicron prevention and surveillance; however, their safe implementation depends on strong data security measures. The long-term viability, patient confidence, and data privacy of IoT-based healthcare systems are ensured by integrating authentication, encryption, secure cloud storage, and adherence to regulations.

Challenges and future perspectives in IoT-Based health monitoring systems

Despite anticipated advancements in COVID-19 research and smart applications, further research is needed to develop autonomous and quick notification systems for virus epidemics (Gendy and Yuce, 2022). IoT-based COVID-19 remote monitoring techniques, including drone technology, wearable sensor nodes, IoT buttons, and several frameworks and architectures, have been suggested to manage and control the COVID-19 outbreak.

Challenges

High latency is one of the biggest issues in the real-time collection of data on COVID-19 symptoms. Several heterogeneous devices must be connected to collect data on COVID-19-associated metrics using IoT architecture. Deploying cloud services near the consumer or at one hop is the most practical way to solve latency issues. This is referred to as Mobile Edge Computing (MEC). MEC is a unique instance of cloud computing in which customers have access to processing and storage resources. Additionally, big data analytics offers a promising catalyst for IoT in helping to battle worldwide pandemics (Ndiaye et al., 2020). A wide range of IoT solutions supported by AI and big data have been proposed; however, it is acknowledged that they might not yet have achieved their full potential. IoT-based frameworks raise privacy concerns because these low-end devices are highly susceptible to denial-of-service attacks and are readily exploited by hackers. This issue can be resolved by creating privacy-preserving protocols for video conversations and contact tracing. Figure 10 shows a conceptual block design of an IoT health monitoring ecosystem tailored for pandemic detection and response, including its framework, data flow, and essential components (Mohapatra et al., 2025).

Future perspectives

The following are future suggestions to enhance the COVICT architecture for contact tracing and the real-time detection of COVID-19: To solve latency, jittering, and network dysconnectivity issues, it is recommended to use cloud services near the user, that is, at one hop (Wahid et al., 2023).

Advances in entomology and info surveillance will enhance the IoT’s ability to analyze health-related searches, public communication, and Internet behaviour (Dhanasek and ar, 2025).

IoT-enabled online patient rooms, remote diagnostics, and video consultations enhance ongoing clinical workflows by lowering the risk of in-person exposure to medical personnel (Dehghani et al., 2023).

A customized risk profile will be supported by wearables and biosensors, allowing for early care intensification for high-risk patients and tailored quarantine guidelines.

In this article, the impacts of the SARS-CoV-2 omicron variant on several nations are discussed, along with potential prevention measures. The transmissibility of the omicron variant and its different forms of SARS-CoV-2 have been analyzed. This article also reports the vaccination status, COVID-19 active cases, recovery cases, and mortality cases for different countries up to February 2025. According to the WHO, new conditions increased by 20% globally in February 2025, with approximately 687 million cases reported. This study used IoT for numerous applications, emphasizing its importance in combating COVID-19 Omicron. IoT technology offers multifaceted solutions for immediate and long-term pandemic control, from the quick identification and isolation of affected individuals to remote healthcare delivery, predictive analytics, and advanced monitoring systems. The COVICT framework, wearable biosensors, and consumer-centric IoT devices further exemplify the adaptability and innovation of IoT in bolstering public health efforts. This article integrates consumer devices, IoMT, AI, biosensors, telemedicine, and smart city monitoring into a single preventative framework, making it unique in that it offers a surveillance-oriented, Omicron-specific study of IoT applications. This study emphasizes community-level deployment, real-time variant containment strategies, and remote healthcare operations adapted to the high transmissibility of the Omicron variant, in contrast to prior IoT–COVID surveys that concentrated on general pandemic management and hospital settings. As we navigate the challenges posed by emerging variants, IoT integration remains a critical tool in our arsenal for safeguarding communities and mitigating the impact of infectious diseases.