United States Government Publishing Office publisher pbl distributor dst United States Commerce Department National Institute of Standards and Technology (NIST) author aut Government Organization Moayeri, Nader. author aut text government publication eng GOVPUB Executive Agency Publications executive 2023-10-27 Commerce Department 2022-11-29 monographic deposited born digital 52 digital object pages C 13. https://www.govinfo.gov/app/details/GOVPUB-C13-f9ec02753397ddbd67b0ece5137d3206 P0b002ee1c1bb2c4f on1389890529 (OCoLC)1389890529 DGPO 2023-10-27 2026-06-27 GOVPUB-C13-f9ec02753397ddbd67b0ece5137d3206 machine generated eng fdlp C13 GOVPUB-C13-f9ec02753397ddbd67b0ece5137d3206 f9ec02753397ddbd67b0ece5137d3206 GOVPUB NIST Interagency or Internal Reports NOTE: THE “DATE ISSUED” ABOVE MAY DEFAULT TO JANUARY 1ST OF A GIVEN YEAR. TO THE VIEW THE MOST ACCURATE DATE OF ISSUE, REVIEW THE TITLE PAGE OF THE PUBLICATION. Interim or final reports on work performed by NIST for outside sponsors (both government and non-government). May also report results of NIST projects of transitory or limited interest, including those that will be published subsequently in more comprehensive form. National Institute of Standards and Technology Commerce Department. National Institute of Standards and Technology. 2022 2022-11-29 https://www.govinfo.gov/app/details/GOVPUB-C13-f9ec02753397ddbd67b0ece5137d3206 https://www.govinfo.gov/content/pkg/GOVPUB-C13-f9ec02753397ddbd67b0ece5137d3206/pdf/GOVPUB-C13-f9ec02753397ddbd67b0ece5137d3206.pdf Bluetooth low energy (BLE) Bluetooth technology Communicable diseases Contact tracing (Epidemiology) COVID-19 (Disease) Distance estimation Electronic contact tracing Exposure notification Infection Pandemics Path loss models Proximity detection Received signal strength indicator (RSSI) Receiver operating characteristic curves Smartphones Viterbi algorithm Wearable technology Moayeri, Nader. creator Moayeri, Nader. Li, Chang. Shi, Lu. National Institute of Standards and Technology (U.S.) Material Measurement Laboratory mdu U.S. Dept. of Commerce, National Institute of Standards and Technology 2022-11-29. monographic 1 online resource (52 pages) : illustrations (color) text technical report eng The Corona VIrus Disease 2019 (COVID-19) pandemic has had a profound effect on the entire world. With the onset of the pandemic in 2020, also started various efforts around the world to automate the contact tracing process to increase its efficacy. Most of these efforts and the smartphone apps that were developed used the Bluetooth Low Energy (BLE) Received Signal Strength Indicator (RSSI) to detect proximity between people. This was facilitated by the development of the Google-Apple Exposure Notification (GAEN) system that made it possible for Android phones and iPhones to exchange standardized BLE messages seamlessly so that RSSI could be measured by both types of phones. In the summer of 2020, we carried out a five-week long BLE RSSI data collection campaign using wearable devices developed at NIST and running on a Raspberry Pi platform. The data collection was comprehensive, because it included a wide variety of operational scenarios including situations where the two devices were not in line-of-site of each other. To the best of our knowledge, there are no publicly reported repositories of BLE RSSI data that includes non-line-of-site (NLOS) scenarios. Such scenarios are important, because they can lead to false alarms in detecting situations where virus transmission between two people is possible. The paper presents a classical analytical framework for proximity detection in the context of electronic contact tracing for COVID-19. We make a distinction between instantaneous and after the fact proximity detection. In the former case, the purpose is to instantaneously warn people that they are violating social distancing rules. In the latter case, the purpose is to notify people after the fact that they had a close contact of certain duration with an individual who has tested positive for COVID-19 and was capable of transmitting the virus at the time of the contact. We evaluate the performance of various methods for instantaneous proximity detection. In after the fact proximity detection, we are interested in identifying periods of time where two people were too close to each other with no barriers between them. We propose a method that applies the Viterbi Algorithm to a time series of BLE RSSI data to solve this problem. The method exploits the fact that humans do not move faster than certain speed. In both cases of instantaneous and after the fact proximity detection, we show that methods based on BLE RSSI data leave a lot of room for improvement. Therefore, the problem of proximity detection for electronic contact tracing and blunting the spread of highly infectious diseases is far from solved. We also know that the next pandemic is not a matter of if but when. Nader Moayeri; Chang Li; Lu Shi. November 2022. Title from PDF title page (viewed January 4, 2023). Includes bibliographical references. Approved by the NIST Editorial Review Board on 2022-09-20 Mode of access: World Wide Web. Systems requirements: Adobe Acrobat PDF reader. Bluetooth technology COVID-19 (Disease) Contact tracing (Epidemiology) Infection Communicable diseases Pandemics Receiver operating characteristic curves Smartphones Wearable technology Bluetooth low energy (BLE) Distance estimation Electronic contact tracing Exposure notification Path loss models Proximity detection Received signal strength indicator (RSSI) Viterbi algorithm https://doi.org/10.6028/NIST.IR.8437