Open-Source Intelligence for Detection of Radiological Events and Syndromes Following the Invasion of Ukraine in 2022: Observational Study

Haley Stone; David Heslop; Samsung Lim; Ines Sarmiento; Mohana Kunasekaran; C Raina MacIntyre

doi:10.2196/39895

Open-Source Intelligence for Detection of Radiological Events and Syndromes Following the Invasion of Ukraine in 2022: Observational Study

JMIR Infodemiology. 2023 Jun 28:3:e39895. doi: 10.2196/39895.

Authors

Haley Stone¹, David Heslop², Samsung Lim³, Ines Sarmiento¹, Mohana Kunasekaran¹, C Raina MacIntyre^{1

4}

Affiliations

¹ Biosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Kensington, Australia.
² School of Population Health, Faculty of Medicine, University of New South Wales, Sydney, Australia.
³ School of Civil & Environmental Engineering, University of New South Wales, Sydney, Australia.
⁴ College of Public Service & Community Solutions, Arizona State University, Tempe, AZ, United States.

PMID: 37379069
PMCID: PMC10365590
DOI: 10.2196/39895

Abstract

Background: On February 25, 2022, Russian forces took control of the Chernobyl power plant after continuous fighting within the Chernobyl exclusion zone. Continual events occurred in the month of March, which raised the risk of potential contamination of previously uncontaminated areas and the potential for impacts on human and environmental health. The disruption of war has caused interruptions to normal preventive activities, and radiation monitoring sensors have been nonfunctional. Open-source intelligence can be informative when formal reporting and data are unavailable.

Objective: This paper aimed to demonstrate the value of open-source intelligence in Ukraine to identify signals of potential radiological events of health significance during the Ukrainian conflict.

Methods: Data were collected from search terminology for radiobiological events and acute radiation syndrome detection between February 1 and March 20, 2022, using 2 open-source intelligence (OSINT) systems, EPIWATCH and Epitweetr.

Results: Both EPIWATCH and Epitweetr identified signals of potential radiobiological events throughout Ukraine, particularly on March 4 in Kyiv, Bucha, and Chernobyl.

Conclusions: Open-source data can provide valuable intelligence and early warning about potential radiation hazards in conditions of war, where formal reporting and mitigation may be lacking, to enable timely emergency and public health responses.

Keywords: OSINT; Ukraine; artificial intelligence; contamination; data source; early warning; emergency response; environmental health; open source; open-source intelligence; power plant; public health; radiation; radiobiological events; radiological; sensor.

©Haley Stone, David Heslop, Samsung Lim, Ines Sarmiento, Mohana Kunasekaran, C Raina MacIntyre. Originally published in JMIR Infodemiology (https://infodemiology.jmir.org), 28.06.2023.