Duncan T. Wilson

Agent-based simulation for large-scale emergency response: A survey of usage and implementation

When attempting to determine how to respond optimally to a large-scale emergency, the ability to predict the consequences of certain courses of action in silico is of great utility. Agent-based simulations (ABSs) have become the de facto tool for this purpose; however, they may be used and implemented in a variety of ways. This article reviews existing implementations of ABSs for large-scale emergency response, and presents a taxonomy classifying them by usage. Opportunities for improving ABS for large-scale emergency response are identified.

A multi-objective combinatorial model of casualty processing in major incident response

During the emergency response to mass casualty incidents decisions relating to the extrication, treatment and transporting of casualties are made in a real-time, sequential manner. In this paper we describe a novel combinatorial optimization model of this problem which acknowledges its temporal nature by employing a scheduling approach. The model is of a multi-objective nature, utilizing a lexicographic view to combine objectives in a manner which capitalizes on their natural ordering of priority. The model includes pertinent details regarding the stochastic nature of casualty health, the spatial nature of multi-site emergencies and the dynamic capacity of hospitals. A Variable Neighborhood Descent metaheuristic is employed in order to solve the model. The model is evaluated over a range of potential problems, with results confirming its effective and robust nature.

Agent-based simulation of emergency response to plan the allocation of resources for a hypothetical two-site major incident

During a major incident, the emergency services work together to ensure that those casualties who are critically injured are identified and transported to an appropriate hospital as fast as possible. If the incident is multi-site and resources are limited, the efficiency of this process is compromised as the finite resources must be shared among the multiple sites. In this paper, agent-based simulation is used to determine the allocation of resources for a two-site incident which minimizes the latest hospital arrival times for critically injured casualties. Further, how the optimal resource allocation depends on the distribution of casualties across the two sites is investigated. Such application supports the use of agent-based simulation as a tool to aid emergency response.

Statistical challenges in assessing potential efficacy of complex interventions in pilot or feasibility studies

Early phase trials of complex interventions currently focus on assessing the feasibility of a large randomised control trial and on conducting pilot work. Assessing the efficacy of the proposed intervention is generally discouraged, due to concerns of underpowered hypothesis testing. In contrast, early assessment of efficacy is common for drug therapies, where phase II trials are often used as a screening mechanism to identify promising treatments. In this paper, we outline the challenges encountered in extending ideas developed in the phase II drug trial literature to the complex intervention setting. The prevalence of multiple endpoints and clustering of outcome data are identified as important considerations, having implications for timely and robust determination of optimal trial design parameters. The potential for Bayesian methods to help to identify robust trial designs and optimal decision rules is also explored.

Investigating the effect of overtriage on hospital arrival times of critically injured casualties during a major incident using agent-based simulation

This paper uses agent-based simulation to simulate the prehospital response to a hypothetical major incident in the UK. The rate of overtriage by the operational-level secondary triage officer is varied, and its effect on the latest arrival time of a critically injured casualty to hospital is modelled as a function of the number of ambulances involved in the response. Modelling such relationships could aid strategic-level planning for emergencies, by providing insight into how to compensate for the effect of overtriage.

Online optimization of casualty processing in major incident response: An experimental analysis

When designing an optimization model for use in mass casualty incident (MCI) response, the dynamic and uncertain nature of the problem environment poses a significant challenge. Many key problem parameters, such as the number of casualties to be processed, will typically change as the response operation progresses. Other parameters, such as the time required to complete key response tasks, must be estimated and are therefore prone to errors. In this work we extend a multi-objective combinatorial optimization model for MCI response to improve performance in dynamic and uncertain environments. The model is developed to allow for use in real time, with continuous communication between the optimization model and problem environment. A simulation of this problem environment is described, allowing for a series of computational experiments evaluating how model utility is influenced by a range of key dynamic or uncertain problem and model characteristics. It is demonstrated that the move to an online system mitigates against poor communication speed, while errors in the estimation of task duration parameters are shown to significantly reduce model utility.

The STORMI Scenario Designer: A program to facilitate setting up agent-based simulations of major incident emergency response

Compared to live exercises, agent-based simulation is an inexpensive method to perform ‘what-if’ style experiments of emergency response. However, the lack of a user-friendly interface often prevents such software being widely adopted by practitioners. This paper describes the STORMI Scenario Designer, a program designed specifically to facilitate practitioners in setting up agent-based simulations of emergency scenarios.

Simulating the spatial organization of the UK Ambulance Service at major incident sites

Documentation such as generic major incident plans and action cards describe how the UK Ambulance Service is to respond to major incidents, including how paramedics should organize themselves at the incident site. Towards the aim of testing existing procedures to hypothetical events in-silico, this paper proposes a way to simulate the on-site spatial organization of the UK Ambulance Service as part of an agent-based simulation of emergency response. The 2001 Selby train crash is used as a case study to demonstrate the approach.

Phase II trial designs for complex interventions: a single-arm single-stage design for clustered continuous outcomes

To incorporate formal assessments of potential efficacy into feasibility and pilot studies of complex interventions, appropriate methods for sample size determination are required. In particular, the implications of clustered patient outcomes resulting from cluster randomisation or treatment provision must be addressed. In this paper we will show how small-sample methods used to design phase II drug trials can be extended to allow for this clustering. In particular, we will propose a single-arm single-stage design for continuous data clustered in a two-level hierarchical structure. A number of alternative methods for determining the choice of sample size will be described. We will report the results of a simulation study comparing these methods in terms of the resulting error rates, suggested sample size parameters, and computational burden. A sensitivity analysis will demonstrate the implications of nuisance parameter misspecification at the design stage. Particular attention will be given to the scenario where the number of cluster-level units is severely limited, as will often be the case in the early phases of complex intervention development and evaluation.

Modeling Uncertain and Dynamic Casualty Health in Optimization-Based Decision Support for Mass Casualty Incident Response

When designing a decision support program for use in coordinating the response to Mass Casualty Incidents, the modelling of the health of casualties presents a significant challenge. In this paper we propose one such health model, capable of acknowledging both the uncertain and dynamic nature of casualty health. Incorporating this into a larger optimisation model capable of use in real-time and in an online manner, computational experiments examining the effect of errors in health assessment, regular updates of health and delays in communication are reported. Results demonstrate the often significant impact of these factors.