Computational modeling of first responders’ willingness to accept radiation exposure during radiological or nuclear events




first responder, willingness to work, radiological, nuclear


Since the events of 9/11, a concerted interagency effort has been undertaken to create comprehensive emergency planning and preparedness strategies for the management of a radiological or nuclear event in the US. These planning guides include protective action guidelines, medical countermeasure recommendations, and systems for diagnosing and triaging radiation injury. Yet, key areas such as perception of risk from radiation exposure by first responders have not been addressed. In this study, we identify the need to model and develop new strategies for medical management of large-scale population exposures to radiation and examine the phenomena of radiation dread and its role in emergency response using an agent-based modeling approach. Using the computational modeling platform NetLogo, we developed a series of models examining factors affecting first responders’ willingness to work (WTW) in the context of entering areas where radioactive contamination is present or triaging individuals potentially contaminated with radioactive materials. In these models, the presence of radiation subject matter experts (SMEs) was found to increase WTW. Degree of communication was found to be a dynamic variable with either positive or negative effects on WTW dependent on the initial WTW demographics of the test population. Our findings illustrate that radiation dread is a significant confounder for emergency response to radiological or nuclear events and that increasing the presence of radiation SME in the field and communication among first responders when such radiation SMEs are present will help mitigate the effect of radiation dread and improve first responder WTW during future radiological or nuclear events.

Author Biographies

Mary Sproull, PhD

George Mason University, Fairfax, Virginia

Terri Rebmann, PhD

Saint Louis University, St. Louis, Missouri

Austin Turner, MS

Saint Louis University, St. Louis, Missouri

Rachel Charney, MD

Saint Louis University, St. Louis, Missouri

Emmanuel Petricoin, PhD

George Mason University, Fairfax, Virginia

Gregory D. Koblentz, PhD

George Mason University, Fairfax, Virginia

William G. Kennedy, PhD

George Mason University, Fairfax, Virginia


US Department of Homeland Security: Radiological dispersal device (RDD) response guidance: Planning for the first 100 minutes. 2017. Available at Accessed September 21, 2022.

US Department of Homeland Security: Nuclear/radiological incident annex to the response and recovery federal interagency operational plans. 2016. Available at Accessed September 21, 2022.

Buddemeier B, Dillon M: Key Response Planning Factors for the Aftermath of Nuclear Terrorism. Livermore, CA: Lawrence Livermore National Laboratory, 2009 (LLNL-TR-410067).

National Security Staff Interagency Policy Coordination Subcommittee for Preparedness & Response to Radiological and Nuclear Threats: Planning Guidance for Response to a Nuclear Detonation. 2010. Available at Accessed September 21, 2022.

Sproull M, Koizumi N, Petricoin E, et al.: The impact of radiation dread on mass casualty medical management during a radiological or nuclear event. Am J Disaster Med. 2021; 16(2): 147-162.

Rebmann T, Charney RL, Loux TM, et al.: Firefighters’ and emergency medical service personnel’s knowledge and training on radiation exposures and safety: Results from a survey. Health Secur. 2019; 17(5): 393-402.

Turner JA, Rebmann T, Loux TM, et al.: Willingness to respond to radiological disasters among first responders in St. Louis, Missouri. Health Secur. 2020; 18(4): 318-328.

Veenema TG, Lavin RP, Bender A, et al.: National nurse readiness for radiation emergencies and nuclear events: A systematic review of the literature. Nurs Outlook. 2019; 67(1): 54-88.

Jackson JC, Rand D, Lewis K, et al.: Agent-based modeling: A guide for social psychologists. Soc Psychol Personal Sci. 2017; 8(4): 387-395.

DeAngelis DL, Diaz SG: Decision-making in agent-based modeling: A current review and future prospectus. Front Ecol Evol. 2019; 6: 237.

Tisue S, Wilensky U: NetLogo: A Simple Environment for Modeling Complexity. Boston, MA: International Conference on Complex Systems, 2004.

Niazi M, Hussain A: Agent-based tools for modeling and simulation of self-organization in peer-to-peer, ad hoc, and other complex networks. IEEE Commun Mag. 2009; 47(3): 166-173.

Wilensky U, Rand W: An introduction to agent-based modeling: Modeling natural. In Social, and Engineered Complex Systems with NetLogo. USA: MIT Press, 2015.

Kennedy W, Bassett J: Implementing a “fast and frugal” cognitive model within a computational social simulation. In Proceedings of the Second Annual Meeting of the Computational Social Science Society of the Americas. 2011.

Oregon State University: Radiological Operations Support Specialist (ROSS). 2018. Available at Accessed November 15, 2021.

National Association of County & City Health Officials: NACCHO: Preparedness Brief Available at Accessed November 15, 2021.

National Alliance for Radiation Readiness (NARR): 2018. Available at Accessed November 15, 2021.

US Department of Health & Human Services: Radiation emergency medical management (REMM). 2018. Available at Accessed November 15, 2021.

Sproull M: Modeling of Mass Casualty Management during a Radiological or Nuclear Event. Fairfax, VA: George Mason University ProQuest Dissertations Publishing, 2022.






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