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Prof. Ruben Vescovo (Tohoku University): Modeling Japan’s DMAT Framework: an agent-based model for disaster medical mobility at scale
November 14 @ 1:00 pm - 2:00 pm
For this week’s IGS seminar, we’ll have Prof. Ruben Vescovo, a visiting collaborator from Tohoku University to present his work.
Time & Location:
Friday Nov 14, 2025, 1:00 PM – 2:00 PM
Donald Bren Hall 3011, ICS, UC Irvine
Donald Bren Hall 3011, ICS, UC Irvine
Lunch will be provided.
Title:
Modeling Japan’s DMAT Framework: an agent-based model for disaster medical
mobility at scale
Abstract:
Disasters provide us with a unique set of conditions: they are both incredibly destructive
and very infrequent. Statistically speaking, when we consider disasters, we are thinking
about distribution outliers with significant magnitude, which are often not in distribution
of one another. Hence, studying mobility for a disaster is non-deterministic, causal
system that is highly coupled with the co-disaster or post-disaster condition.
and very infrequent. Statistically speaking, when we consider disasters, we are thinking
about distribution outliers with significant magnitude, which are often not in distribution
of one another. Hence, studying mobility for a disaster is non-deterministic, causal
system that is highly coupled with the co-disaster or post-disaster condition.
Disaster management frameworks that deal with the co-disaster and post-disaster
logistics are often devised in the mitigation and preparedness stages of the disaster-
cycle, but remain unproven until the next disaster, which inevitably prompts a review of
the framework resulting from unaccounted-for disaster-driven circumstances.
One such framework is the Japanese Disaster Medical Assistance Team (DMAT), a
government taskforce comprised of medical professionals (doctors, nurses, logistics
personnel) which is prescribed to operate in the post disaster window (0 to 72 hours)
after a disaster event. DMAT is a centrally managed, strictly regulated, command &
control organization which is tasked with assisting hospital operations by facilitating
patient processing, transfers, and transport.
logistics are often devised in the mitigation and preparedness stages of the disaster-
cycle, but remain unproven until the next disaster, which inevitably prompts a review of
the framework resulting from unaccounted-for disaster-driven circumstances.
One such framework is the Japanese Disaster Medical Assistance Team (DMAT), a
government taskforce comprised of medical professionals (doctors, nurses, logistics
personnel) which is prescribed to operate in the post disaster window (0 to 72 hours)
after a disaster event. DMAT is a centrally managed, strictly regulated, command &
control organization which is tasked with assisting hospital operations by facilitating
patient processing, transfers, and transport.
To better understand the limitations of the DMAT system in context, IRIDeS is
collaborating with the Japanese Government to reproduce the DMAT framework as an
Agent-Based “Digital Shadow” to test various post-disaster conditions, infrastructure
arrangements, and DMAT supply & support flows. Due to the scale of operations, the
model is being developed as a high-performance parallel system designed to operate
on vector processing units and wide CPU throughput configurations.
collaborating with the Japanese Government to reproduce the DMAT framework as an
Agent-Based “Digital Shadow” to test various post-disaster conditions, infrastructure
arrangements, and DMAT supply & support flows. Due to the scale of operations, the
model is being developed as a high-performance parallel system designed to operate
on vector processing units and wide CPU throughput configurations.
Bio:
Ruben is Assistant Professor at the International Research Institute of Disaster Science
(IRIDeS) at Tohoku University. Currently his research focus is Agent Based Model applications to disaster management and disaster science, with an emphasis on decision making agents under uncertain conditions. Ruben’s previous work centered on statistical and probabilistic machine learning applications to uncertainty-aware predictive hazard-to-vulnerability models for
(IRIDeS) at Tohoku University. Currently his research focus is Agent Based Model applications to disaster management and disaster science, with an emphasis on decision making agents under uncertain conditions. Ruben’s previous work centered on statistical and probabilistic machine learning applications to uncertainty-aware predictive hazard-to-vulnerability models for
infrastructure in disasters.