CBRN: How modelling supports incident response - Riskaware

CBRN: How modelling supports incident response

By Robert Gordon & Murray Purves

Optimised responses to CBRN incidents are crucial to the safeguarding of public health and critical infrastructure. These can only be achieved if key decision makers have access to reliable, timely and actionable intelligence and situational awareness data, which allows them to strategically deploy resources and provide tailored information to first responders as an incident develops.

Modelling that encompasses both indoor and outdoor dispersion plays a crucial part in fulfilling this requirement. Throughout every stage of incident response, modelling supports key stakeholders by taking complex information from a variety of sources and making it accessible, understandable and actionable. This means responders can make informed decisions at pace and thus more effectively mitigate CBRN incidents.

The role of modelling in CBRN incident response strategies

Like a number of other incident types, responses to a release of CBRN material often rely on the provision of scientific modelling data to give estimates of the hazard posed by the release. This data is typically required to be generated within very short timescales to facilitate effective decision making.

Historically, this has placed limits on the fidelity of modelling that can be achieved during a response. Organisations either required large and expensive compute resources to carry out the simulations or had to accept a lower quality of data.

Many organisations rely on the use of “templates” to provide these hazard predictions. Templates are a very basic kind of modelling, with limited information used to produce simple shapes representing the broad area of hazard that may be expected. While such templates are useful to provide a near-instant estimate, they are often very conservative in nature. This can prompt response actions – and their associated costs – far in excess of what is truly required.

High-fidelity modelling can provide a more precise and accurate estimate of the true extent and severity of the CBRN hazard, enabling responders to target their resources more efficiently. This comes at the cost of requiring the equipment, human expertise and crucially, the time required to operate these models.

UrbanAware aims to solve this challenge by reducing the entry barriers associated with high-fidelity CBRN hazard prediction. For the first time, research-grade models have been packaged within an accessible interface and backed by cloud computing resources. This reduces the tradeoffs required, enabling complex modelling to be carried out without the typical requirement for domain expertise and computer resource commitment.

Learn more by downloading the UrbanAware brochure

Advanced modelling concepts

Enhancing model predictions with data fusion

“All models are wrong, but some are useful” – George Box

In the early stages of most CBRN events, decision makers may be aware of some basic information; however, it’s likely they’ll know very little about the hazardous airborne material released or its quantities. With limited intelligence, even the highest-fidelity modelling systems will not be able to exactly match the situation on the ground.

As the situation evolves, the best CBRN information management systems pull in live data as it is gathered, and use it to augment and improve its own predictions using data fusion. Data fusion combines multiple sources of data to build a more accurate picture, using sources such as:

  • CBRN detectors
  • Casualty data
  • CCTV evidence
  • Eyewitness reports

Data fusion allows incident commanders and other key stakeholders to base response decisions on the best available information from both simulated and real-world sources. It reduces the cognitive overhead required to process data from a wide range of disparate sources, allowing people to maximise the value of their data and discover more information about the type of hazardous material facing them.

Improved modelling within the urban environment

An urban dispersion model – such as that integrated within UrbanAware – accounts for material interactions with urban infrastructure.

By considering those effects specific to the urban environment, such as wake entrainment, channelling, and the introduction of additional turbulence, the model can provide more accurate information about the direction and extent of the predicted hazard. This allows decision makers to better estimate potential impacts on areas in which data is unavailable, and more effectively manage the incident.

Incorporating indoor effects

Indoor CBRN dispersion modelling simulates the evolution of the hazard inside a building when a release occurs within it, or when an outdoor release infiltrates into a building.

The Urban Sub-System (USS) available in UrbanAware enables users to simulate outdoor and indoor dispersion in concert and model the interaction between the two. This allows decision makers to specifically determine their responses to key infrastructure, including schools and hospitals. It also helps develop better informed sheltering and evacuation strategies.

Modelling the effects of a CBRN hazard on people and infrastructure 

CBRN effects modelling pulls model prediction data together with toxicology, population and other datasets to predict the effects of a CBRN release on people and infrastructure.

Access to accurate population data is a key challenge for modelling experts to overcome, as it directly impacts estimates of the number of people affected by a CBRN incident. Riskaware is working to develop a dynamic population database to facilitate better estimates of the effects of a CBRN release through more accurate population counts.

Communicating uncertainty

All models are uncertain. However, decisions informed by them can’t be. That means model uncertainties must be combined and presented to decision makers in a clear and concise way so they can account for them. Riskaware’s UrbanAware solution achieves this, presenting a simple and consistent Common Operational Picture to stakeholders through an intuitive user interface.

Other applications of CBRN modelling

Supporting the training of CBRN first responders

Realistically simulating CBRN incidents is crucial to the effective delivery of CBRN training exercises and the optimisation of a response. This can be achieved through the generation of synthetic “ground truth” data as the basis for an exercise, providing a realistic depiction of a potential incident and the hazards caused by it.

This ground truth is also utilised to generate exercise inject data, providing players with realistic and consistent information as they progress through the exercise. This data can be predetermined (such as a simulated report from an external agency), or reactive to player action (such as synthetic readings from a hand-held detector).

By leveraging modelling data to its fullest capacity, first responders are able to “train how they play” by being fully immersed in the exercise scenario. Integration with equipment can enable realistic hardware displaying synthetic data to be utilised, such as augmented reality-enabled scene assessment and simulated detector/sensor readings.

The effective use of modelling supports the delivery of effective training exercises at scale. Applying consistent data throughout all phases of an exercise enables organisations to extract maximum value from a limited training budget.

The effective use of modelling in exercise development and delivery enables organisations to extract maximum value from a limited training budget. Applying consistent inject data throughout all phases of an exercise enables key learning objectives to be effectively achieved, from a simple table-top scenario all the way to a full end-to-end multi-agency field exercise.

Read our guide to CBRN incidents, solutions and response

Supporting “what-if” incident response planning

“What-if” analysis using dispersion and effects modelling helps organisations plan effective response strategies for a range of potential incidents in a cost- and time-efficient manner. By combining intelligence and analysis with a statistical approach – for example using climatology and historic meteorological data -a variety of simulations can be conducted. They assess how an incident could impact an area under a wide range of conditions. For example:

  •   If the wind direction is easterly, which key infrastructure needs to be evacuated?
  •   If the attack occurs in the morning rather than the evening, how many casualties are there likely to be?
  •   If X amount of a hazardous airborne material is released, what are the best and worst case scenarios?

Considering all of these factors, decision makers can prepare robust and responsive mitigation strategies and pre-plan evacuation routes and the positioning of first responders. All of this helps to optimise incident responses and better protect public health and key infrastructure.

For more in-depth information about UrbanAware, download our product sheet

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