Using real-time data to guide decision-making during an influenza pandemic: A modelling analysis
Fig 5
Proposed decision framework for triggering preemptive non-pharmaceutical interventions (NPIs), in advance of the fall wave.
Shown, for illustration, is the example of California, and a proposed scenario in which school opening is postponed by 10 weeks. These plots can be interpreted as cumulative probability distributions, for the total hospitalisations projected in the fall wave. As described in the main text, we define a ‘probabilistic risk score’ (PRS) as the probability that fall wave hospitalisations will increase a given threshold, h. We assume that preemptive interventions would be triggered if PRS exceeds some threshold probability P, with both H and P determined by a policymaker. The figure shows an illustrative scenario where h = 1, 500 cumulative hospitalisations, and P = 0.1 (‘reference point’, shown as a black dot). Any model-based projections can be represented as a downward-sloping curve on this plot: preemptive interventions would be triggered if the curve intersects the vertical, dashed line at any point above the reference point. As examples, the blue curve shows model projections for a 2009-pandemic-like virus in California (i.e. corresponding to Fig 3A), a scenario that would not trigger preemptive interventions. The solid red curve shows an alternative scenario, of a virus that is equally infectious, but twice as severe (i.e. having twice the risk of hospitalisation given infection). Such a virus would trigger preemptive interventions; the dashed red curve shows the reduction in hospitalisation risk that would occur, in a scenario where school opening is postponed for 10 weeks until vaccine rollout is underway (assuming the same vaccine introduction and rollout scenario as occurred in 2009–2010, in response to the pandemic).