Disease Modelling

Our Disease Modelling Capabilities

Riskaware’s disease modelling consultancy services can be tailored for a variety of incidents and use cases. We have the expertise to develop novel models, enhance existing models, and empower your capabilities with innovative algorithms or robust data inputs in order to add greater insight.

Whatever your need, our solutions provide greater situational awareness about any given disease incident and allow for the planning of mitigation strategies.

From understanding human-to-human disease transfer or vector-borne diseases, to simulating mitigation techniques for any disease outbreak, we can provide the capabilities you need to retrieve actionable intelligence about disease incidents.

Bespoke disease models

Riskaware can build bespoke disease models for both naturally occurring outbreaks and biological incidents caused by humans, such as attacks or accidental releases. This includes bespoke parameters, compartments, and disease characteristics, such as the following:

• Deterministic and/or stochastic models
• Combined models e.g. disease and dispersion models
• Infectious or non-infectious disease models
• Human to human transmission
• Vector borne diseases e.g. malaria
• Spatial disease models

Coupling multiple disease models 

We can couple various disease models with one another, in a modelling chain, or with other related models to create more comprehensive situational awareness.  

One example is coupling disease models with dispersion models, to show the impacts of aerosol releases on a population. Our teams have extensive experience working with integrated indoor and outdoor dispersion models which provide an end-to-end view of a contaminant release. 

Models can be included and exchanged in a plug-and-play approach, so we can create completely bespoke combinations. This flexibility also allows model capabilities to be updated as the situation develops and enhanced by new, innovative technologies or models.  

State inference

Disease models show the spread of a disease through a population, estimating the number of people at each stage of infection – from susceptible to infected to recovered. We utilise data fusion techniques to improve the accuracy of this process, known as state inference, and produce more reliable predictions about population health and disease spread.

Parameter inference

Understanding the rate at which people transition between disease stages is also imperative for creating responsive mitigation and intervention strategies. It enables decision-makers to understand if and when intervention is needed, and helps inform effective resource deployment plans, such as for hospitals.

For example, parameter inference can be used to identify the rate at which infected people require medical care. Having estimates for this parameter allows more accurate predictions to be made for when healthcare facilities might begin to become overwhelmed, allowing mitigation strategies to be devised that prevent this scenario.

Optimisation

Optimising parameters and/or states allows user to understand what measures or properties are needed to achieve a specific goal. Our teams use this technique in disease modelling to help users identify what level of intervention is needed during swiftly changing scenarios, enabling them to respond ahead of time and achieve their objective – whether that’s to minimise the number of deaths, those hospitalised, or the monetary cost of an outbreak.