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 Table of Contents  
Year : 2022  |  Volume : 13  |  Issue : 1  |  Page : 112

Internet of things (IoT) adoption model for early identification and monitoring of COVID-19 cases: A systematic review

1 Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
2 Department of Health Information Management, Student Research Committee, School of Health Management and Information Sciences Branch, Iran University of Medical Sciences, Tehran, Iran
3 Department of Health Information Technology; Department of Student Research Committee, Abadan University of Medical Sciences, Abadan, Iran

Date of Submission19-Nov-2020
Date of Acceptance21-Sep-2021
Date of Web Publication08-Aug-2022

Correspondence Address:
Hadi Kazemi-Arpanahi
Assistant professor of Health Information Management, Department of Health Information Technology, Abadan University of Medical Sciences, Abadan
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/ijpvm.IJPVM_667_20

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Background: The 2019 coronavirus disease (COVID-19) is a mysterious and highly infectious disease that was declared a pandemic by the World Health Organization. The virus poses a great threat to global health and the economy. Currently, in the absence of effective treatment or vaccine, leveraging advanced digital technologies is of great importance. In this respect, the Internet of Things (IoT) is useful for smart monitoring and tracing of COVID-19. Therefore, in this study, we have reviewed the literature available on the IoT-enabled solutions to tackle the current COVID-19 outbreak. Methods: This systematic literature review was conducted using an electronic search of articles in the PubMed, Google Scholar, ProQuest, Scopus, Science Direct, and Web of Science databases to formulate a complete view of the IoT-enabled solutions to monitoring and tracing of COVID-19 according to the FITT (Fit between Individual, Task, and Technology) model. Results: In the literature review, 28 articles were identified as eligible for analysis. This review provides an overview of technological adoption of IoT in COVID-19 to identify significant users, either primary or secondary, required technologies including technical platform, exchange, processing, storage and added-value technologies, and system tasks or applications at “on-body,” “in-clinic/hospital,” and even “in-community” levels. Conclusions: The use of IoT along with advanced intelligence and computing technologies for ubiquitous monitoring and tracking of patients in quarantine has made it a critical aspect in fighting the spread of the current COVID-19 and even future pandemics.

Keywords: Coronavirus, COVID-19, Internet of Things, systematic review

How to cite this article:
Shanbehzadeh M, Nopour R, Kazemi-Arpanahi H. Internet of things (IoT) adoption model for early identification and monitoring of COVID-19 cases: A systematic review. Int J Prev Med 2022;13:112

How to cite this URL:
Shanbehzadeh M, Nopour R, Kazemi-Arpanahi H. Internet of things (IoT) adoption model for early identification and monitoring of COVID-19 cases: A systematic review. Int J Prev Med [serial online] 2022 [cited 2023 Jun 2];13:112. Available from: https://www.ijpvmjournal.net/text.asp?2022/13/1/112/353573

  Context Top

The 2019 coronavirus disease (COVID-19) is a highly contagious disease that has affected a large portion of the population. It is caused by severe acute respiratory syndrome coronavirus-2. The World Health Organization has declared COVID-19 as a public health emergency.[1],[2],[3],[4] Currently, with the lack of approved pharmaceutical treatments or vaccines to cure this disease, there are many ongoing efforts to mitigate the spread of the virus (i.e., “flatten the curve”).[5],[6] Governments across the world are struggling to control the spread of the virus by issuing severe restrictions, such as implementing social distancing, contact tracing, and quarantine, and adhering to the guidelines of safety and precautions provided by the health officials. But implementing such restrictive and large-scale procedures is a huge challenge.[7] Moreover, during this pandemic, health care facilities are looking for practical and cost-effective solutions for early detection, monitoring, and tracking of infected persons in the population who can be contagious (symptomatic or asymptomatic carriers).[8],[9],[10] It is thus highly desirable to develop a smarter and integrated virtual surveillance system to ensure effective COVID-19 control and to reduce the spread across the community.[11],[12]

In recent years, Internet of Things (IoT) has received significant worldwide attention and has become ever more available for predicting, preventing, and monitoring infectious diseases.[6],[13] In this context, IoT technology has been shown to be a safe and efficient way of dealing with the COVID-19 pandemic due to its ubiquitous sensing ability and seamless connectivity.[14] The IoT is an advanced technology that refers to an interconnected web of real objects, sensors, and appliances for sensing, compiling, processing, monitoring, and managing a variety of information of our daily life.[15],[16] Besides, the reliable IoT networks provide timely crucial information that can help in taking timely decisions.[17],[18] Health care can be identified as one of the major application domains for the IoT. Internet of Health Things is a health care–specific version of IoT that aims to connect patients to health care settings for real-time monitoring and control of their clinical features such as health status, activities, and vital signs.[19],[20],[21]

In this pandemic, IoT has the potential to deal with huge amounts of data received from sensors used by a number of applications to battle against COVID-19. Our goal in this study is to review the IoT-based solutions combating this pandemic according to the “Fit between Individuals, Task, and Technology” (FITT) framework for determining its target users, technological requirements, system tasks, and processes.

  Evidence Acquisition Top

The FITT framework provides a theoretical foundation for this comprehensive systematic literature review (SLR), and data extraction was guided according this framework. FITT technology adoption model was presented by Ammenwerth et al.[22], which takes into account the system individuals, tasks, processes, and technologies. Our SLR is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standard guidelines, which describe the identification, screening, eligibility, and inclusion criteria of the articles that fall under the scope of review.[23]

Data sources and search strategy

An extensive SLR was performed using six scientific databases – PubMed, Google Scholar, Scopus, Web of Science, Science Direct, and ProQuest – from June 30 up to November 1, 2020. The following search terms were used (designed using English MeSH keywords and Emtree terms): [COVID-19 OR Novel coronavirus OR nCoV] AND [”Internet of Things” OR “Internet of Medical Things” OR “Internet of Health Things” OR “Medical Internet of Things” OR “IoT” OR “IoMT” OR “IoHT” OR “MIoT”]. After the adoption of advanced search option (search formula: combining key terms, search operators [AND and OR] and search fields: Title, Title/abstract and Topic) and applying the inclusion and exclusion criteria (via search filter or refine result), the titles and abstracts of potentially relevant studies were identified. Details of the search strategy are shown in [Table 1].
Table 1: Search syntax

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Study selection

Some inclusion and exclusion criteria were determined for screening articles. Full-text articles were obtained for detailed evaluation, and eligible studies that used IoT solutions or strategies based on the FITT framework during the COVID-19 pandemic were included in the systematic review. Editorials, commentaries, conferences papers, case reports, duplicates, non-English papers, letters to editor, commentary papers, book chapters, short briefs, technical reports, and those published before the year 2020 were excluded. As COVID-19 is a rapidly evolving field, we included preprint literature.

Data extraction and quality assessment

To minimize bias, two reviewers (H: K-A and M: SH) participated independently through each phase of review and screened the titles and abstracts of articles according to predefined criteria. The authors screened the full-text reports and decided whether they met the inclusion criteria. Any vagueness during the study selection process was resolved by further discussion. The studies that met our predefined inclusion criteria were screened, and the studies that completely fulfilled our inclusion criteria were extracted for deeper analysis and data extraction.

Summarizing results

The results were organized based on IoT adoption models (FITT framework).

  Results Top

Characteristics of included studies

The initial search in scientific database yielded 248 citations; 142 of which remained after omitting the duplicates through the emerged endnote library (reference tools bar > find duplicates) and 53 of which remained after removing non-English, those published before 2020, and the document-type ones (editorial, commentary, report, case study, and conference paper). In the last screening phase, 28 articles were identified as eligible studies that met our criteria [Figure 1].
Figure 1: PRISMA chart in the study selection process

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[Table 2] summarizes the general characteristics of the included studies based on author names, country, design and setting, and IoT digital services.
Table 2: Summary of reviewed articles

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All included studies are summarized in [Table 3] based on the FITT model in three classes: Individual, Technology, and Task.
Table 3: IoT-based FITT model to fight against COVID-19

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The analysis revealed that the IoT platforms that were used to fight against the COVID-19 pandemic were classified into three main sections as follows.

1. IoT individuals

Based on the findings of the “Individual” column in [Table 3], IoT stakeholders are categorized into three classes according the degree of their interactions. In this regard, the main stakeholders of IoT for managing COVID-19 classified as first level include the health care providers[6],[13],[18],[19],[24],[26],[29],[30],[31],[32],[33],[34],[35],[36],[37],[39],[42],[43],[44],[45],[46] and society.[6],[13],[18],[19],[24],[26],[29],[30],[31],[32],[33],[34],[35],[36],[37],[39],[42],[43],[44],[45],[46] The next level of IoT stakeholders include public health authorities,[6],[13],[24],[33],[38],[40],[43],[44],[45],[47] academicians, scientists, and researchers.[18],[25],[27],[29],[30],[31],[33],[34],[38],[40] Finally, the third level includes educational staff,[28] IT (information technology) vendors,[25],[31] economists,[18] third parties,[31] and engineering[25] [see [Figure 2]].
Figure 2: IoT-based COVID-19 stakeholders

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2. IoT technologies

Technological requirements that used to establish IoT for COVID-19 management are classified into five classes, which include technical platform,[13],[18],[19],[25],[30],[31],[34],[35],[40],[41],[42],[43],[47],[48],[49],[50] processing,[6],[25],[31],[35],[36],[47],[49],[50],[51] network,[6],[25],[35],[49],[50] storage,[6],[18],[19],[25],[31],[34],[36],[37],[46],[48] and added-value technologies[19],[27],[34],[36],[40],[41],[42],[47],[51] [see [Figure 3]].
Figure 3: IoT-based COVID-19 technologies

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Smart cell phones,[13],[18],[19],[25],[28],[30],[31],[35],[36],[37],[38],[40],[41],[42],[43],[44],[46],[47],[50],[52] portable digital tools (e.g., microcomputers such as palmtop, tablet, notebook),[27],[28],[34],[37],[39],[40],[43],[46],[47] and wearable devices[13],[18],[19],[25],[30],[31],[34],[35],[41],[42],[43],[40],[47],[48],[49],[50] are the most commonly used technological platforms in the IoT. But the application of nonportable technologies[25],[36],[39],[41],[42],[45] was insignificant.


Application of location-aware technologies (e.g., GPS [Global Positioning Systems], GIS [Geographic Information Systems], remote sensing, and geofencing),[6],[25],[31],[35],[36],[47],[49],[50],[51] intelligence systems (CDSS [clinical decision support system] and decision dashboards),[13],[28],[44],[52] artificial intelligence (AI) technologies (machine learning and deep learning),[18],[19],[36],[37],[39],[43],[44],[45],[51] and biosensor processors such as wearable or nonwearable technologies,[25],[41],[42],[45],[46] special sensor (voice, image, and facial processors)[47],[48],[49],[50] are the well-known techniques in providing added-value capabilities for real-time tracking, online telemonitoring, effective identification, and decision supports.

Exchange (Network)

According to network application in different geographical areas, we categorized Wireless Field Connectors into two classes as follows:

  1. Close Area Networks such as Bluetooth,[28],[30],[31],[37],[41],[42] RFID (radio-frequency identification),[6],[25],[35],[49],[50] WBAN (wireless body area networks)[1] and Zigbee[28],[46]
  2. Broad Area Networks such as mobile technology (GSM [Global System for Mobile Communications] or 4G and 5G networks),[6],[13],[19],[27],[28],[36],[43],[47],[52] Wi-Fi,[13],[28],[30],[35],[38],[47],[49],[50],[51] WSN (wireless sensor network),[39],[48] WiMAX (Worldwide Interoperability for Microwave Access),[12] and internet connection.[31],[36],[41],[42]


Using cloud storage area[6],[18],[19],[25],[31],[34],[36],[37],[46],[48] and shared health databases[18],[19],[30],[40],[41],[42],[45],[46],[47] for backup and permanent accessibility to data is compatible with IoT requirements during the COVID-19 pandemic.

Added-value technologies

The most prominent technologies to provide added value for IoT-based COVID-19 data processing, storage, and communication were cloud computing,[6],[13],[19],[25],[30],[31],[36],[38],[40],[41],[42],[43],[46],[48],[49],[50] telehealth,[18],[27],[28],[36],[37],[39],[43],[45],[47],[51] blockchain,[19],[27],[34],[36],[40],[41],[42],[47],[51] and big data mining.[13],[27],[35],[37],[43],[44],[45],[49],[50]

3. IoT tasks

The most important IoT tasks and use cases related to COVID-19 management in multiple “on-body,” “in-clinic/hospital,” and even “in-community” levels include real-time telemonitoring,[6],[18],[19],[24],[25],[29],[30],[35],[36],[38],[39],[40],[41],[44],[45] effective screening and surveillance programs,[13],[19],[26],[31] case tracking or mobility tracing,[6],[19],[29],[30],[32],[33],[39],[40],[46] timely case identification,[31],[43],[44],[45],[46] self-quarantine and lockdown policy,[39],[42],[47] smart social distancing,[32],[34] and smart contact tracing.[32],[47] Other applications include remote education,[28] knowledge sharing,[27] and decision supports[41],[46] [see [Figure 4]].
Figure 4: IoT-based COVID-19 tasks

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The IoT adoption framework for COVID-19 management is shown in [Figure 5].
Figure 5: IoT adoption framework for COVID-19 management

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  Discussion Top

Currently, given the lack of definitive and effective treatment, as well as the increase in the number of infected cases and mortalities, social isolation and containment strategy have been the best preventive interventions to limit the disease transmission.[13],[18],[48] Therefore, there has been an increasing demand for adoption of innovative technologies. Accordingly, many governments and policy-making agencies have emphasized on adopting innovative solutions to tackle the current health crisis, which is leading to health care's digital revolution.[29],[31],[34],[39],[46],[49] In this sense, “the smart public health surveillance” is a novel concept derived from adopting advanced technologies such as IoT, which uses an internet-like structure for the integration of heterogeneous “medical objects” to address real-time screening, epidemic tracing, case identification, quarantine management, decision support, and accurate predictions.[50],[51],[52] Furthermore, IoT, by providing digital contact tracing and case tracking capabilities, plays a crucial role in maintaining social distancing and implementing lockdown policies.[24],[42]

In the present study, we review current literature about IoT-enabled platforms from technical requirements, expected system tasks, and target user aspects during the COVID-19 outbreak according to the FITT model. Integration of such interrelated technologies can help generate solutions within the health care sector for smart screening, prediction, and prevention of diseases.

Use of portable microcomputers, cell phones, and wearable devices equipped with wireless networks (e.g., Wi-Fi, Bluetooth, and GSM), along with added-value technologies such as new AI algorithms (e.g., machine learning and deep learning), cloud computing, and big data mining, leads to better adoption of IoT in the current and even future pandemics.[25],[31] This study showed, applied to the current crisis, that IoT can be leveraged to help patients receive at-home timely monitoring and smart surveillance through mobile communications, internet connectivity, and other wireless technologies. It can also provide a comprehensive database and flexible data exchange infrastructure for governments, public health authorities, health care settings, and so on.[6],[53] In addition, with regard to big data mining, there are various applications, including machine learning, deep learning, and geospatial processing, in which intelligent algorithms are used for decision making based on the data generated from IoT-enabled devices.[17],[18],[19],[20] Moreover, this technology is also suitable to simultaneously capture the necessary physiological or geospatial data of the confirmed or suspected cases from various locations and manage the collected data in combination with GPS, AI algorithms, cloud computing, and intelligent and virtual management systems.[29],[54],[55]

This study also opens opportunities for health care industry in designing customized IoT-based solutions for real-time monitoring and tracking of current and probable future pandemics. However, our study also has some limitations. In future studies, with the increased usage of IoT-based smart surveillance systems, it is important to analyze the actual use of such systems in the form of longitudinal studies rather than as review or cross-sectional study. In addition, because of our search inclusion/exclusion criteria (IoT-based COVID-19 studies conducted in 2020), we may have missed some valuable studies in this field.

  Conclusion Top

In this article, we reviewed the IoT-enabled solutions according to the FITT framework to better understand the IoT applications to tackle the COVID-19 pandemic.

As a concluding remark, the IoT-based portable devices equipped with wireless broadband networks that take advantage of added-value and innovative complementary technologies such as new AI algorithms, big data mining, geospatial processors, and cloud computing have been broadly used in public health surveillance. Future researches should focus on novel computational and telematics approaches especially in the field of public health big data mining, deep learning, 5G/6G networks, smart implants, robotics, blockchain, and so on. These innovation technologies could be applied to diminish the destructive effects of probable future pandemics.


This article is the result of a research project approved by the research committee at Abadan University of Medical Sciences, Iran (Ethic code number: IR.ABADANUMS.REC.1399.095).

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

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  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]

  [Table 1], [Table 2], [Table 3]


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