A range of models exist for reverse engineering the source term from a deliberate or accidental release of pathogenic agent [1,2,3] and for learning the transmissibility of infectious diseases [4,5,6]. However, a key step from such a hazard “footprint” is then to identify the operational delivery of pharmaceutical interventions such as antivirals (e.g., for influenza) and antibiotics (e.g. for bacterial agents) or decontamination for chemical exposures given uncertainty and time pressure. The Countermeasure Delivery Optimisation Toolbox (developed by Hall when employed in UKHSA) needs updating, whilst it is unclear how to deploy antivirals to boost their effectiveness. The benefit gained from an intervention reduces as the time since exposure increases and so ‘inhost’ models are needed to predict benefit structured and parameterised with relevant data [i.e. 7,8,9]. These advances can be adapted to learn impact of both antivirals and antibiotics to inform response models.

In the geography of a release there maybe sub-populations at greater risk of harm (care home residents, school children, economically disadvantaged) that require specific consideration. There will be a need to engage with senior medical advisors in UKSA to communicate results and create visualisations that can be interpreted at pace.

Working with UKHSA and other engaged partners (potential interest from Dept. of Health and Social Care) this project will:

1. Define a set of scenarios/high risk agents for modelling

2. Simulate exposures to those pathogens

3. Identify the best dose response model for agents to calculate casualty assessments

4. Develop in-host models for disease progression in individuals with mechanistic assumptions for role of interventions

5. Use these models to optimise advice for pre-deployment or usage.

6. Extend the optimisation to include health economic arguments and wider impacts.

7. Develop advisory notes on potential policy options, in tandem with likely users.