Skip to main content

Visualisation of epidemiological map using an Internet of Things infectious disease surveillance platform

Dear Editor,

We read with the interest Editorial by Verdonk et al. on how machine learning could be used in clinical practice during an epidemic such as the coronavirus disease 2019 (COVID-19) [1]. The COVID-19 pandemic has resulted in a global public health emergency. Border control measures such as symptom screening and health questionnaires have been enforced at many international airports [2]. However, recent research and experiences show that many infected travellers can slip through fever-based screening which employs thermography, due to false-negative results [3].

Vital signs, especially the body temperature, are the most frequent symptoms for infectious diseases. We have developed a novel non-contact vital sign measurement system for screening infectious diseases; this system can detect suspected infections based on contactless multiple vital sign monitoring, thereby outperforming fever screening [4]. The most promising approach towards improving the performance involves connecting multiple systems to an Internet of Things (IoT) infectious disease surveillance platform. This could enable operators to predict the outbreaks of infectious diseases earlier than is currently possible.

The concept of IoT infectious disease surveillance platform is presented in Fig. 1. The targeted vital signs are heart rate, respiration rate, and body temperature, which can be measured without contact by using the infectious diseases screening radar system designed for airport quarantine counter and onboard screening (Fig. 1a, b). Moreover, data on ambient temperature, humidity, and global positioning system (GPS) can be simultaneously monitored. The server displays these values according to the IP and GPS information of each system and records these values in addition to the original vital signals and thermal images in its big database, in association with the patient’s ID for future analyses (Fig. 1c). We are investigating methods of improvement and potential novel applications towards the visualisation of the epidemiological map (Fig. 1e, f) via the combination of big data analysis and artificial neural network. For instance, standalone infection screening systems can be placed in airport quarantines, outpatient units, and other places across Japan where mass gatherings are likely to occur. The IoT infectious disease surveillance platform interconnects all infection screening systems, facilitating the collection and transmission of big data via internet. These data can be comprehensively analysed to conduct real-time surveillance and visualisation of the epidemiological map and thereby capture hot-spots or clusters of cases. This technology will enable timely tracking of epidemic outbreaks and facilitate faster decision-making which can delay exponential increase in the number of infected cases (Fig. 1g).

Fig. 1
figure 1

Potential network structure of a vital signs-based infection screening system used for the early detection and prediction of pandemic outbreak of infectious diseases

Advancements in innovative technologies, such as IoT, AI, 5G, and big data are leading to the emergence of a new field in the fight against COVID-19 and other pandemics. Integration of such technologies can help to generate solutions within the healthcare sector for the screening, prediction, and prevention of emerging infectious diseases.

Availability of data and materials

Not applicable.


  1. Verdonk C, Verdonk F, Dreyfus G. How machine learning could be used in clinical practice during an epidemic. Crit Care. 2020;24:265.

    Article  Google Scholar 

  2. Wells CR, Sah P, Moghadas SM, Pandey A, Shoukat A, Wang Y, et al. Impact of international travel and border controls measures on the global spread of the novel 2019 coronavirus outbreak. Proc Natl Acad Sci U S A. 2020;117(13):7504–9.

    Article  CAS  Google Scholar 

  3. Normile D. Airport screening is largely futile, research shows. Science. 2020;367(6483):1177–8.

    Article  CAS  Google Scholar 

  4. Sun G, Hakozaki Y, Abe S, Vinh NQ, Matsui T. A novel infection screening method using a neural network and k-means clustering algorithm which can be applied for screening of unknown or unexpected infectious diseases. J Inf Secur. 2012;65(6):591–2.

    Google Scholar 

Download references




JSPS KAKENHI Grant-in-Aid for Scientific Research (B) Grant No. 19H02385.

Author information

Authors and Affiliations



Study concept and design: GS, NVT, TM. Analysis and interpretation: GS, NVT, PTH, LTH, KI, TM. Drafting of the manuscript: GS, NVT. All authors final approval of the version to be submitted.

Corresponding author

Correspondence to Guanghao Sun.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests


Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, G., Trung, N.V., Hoi, L.T. et al. Visualisation of epidemiological map using an Internet of Things infectious disease surveillance platform. Crit Care 24, 400 (2020).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: