Simple simulation based reconstruction of incidence rates from death data

Daily deaths from an infectious disease provide a means for retrospectively inferring daily incidence, given knowledge of the infection-to-death interval distribution. Existing methods for doing so rely either on fitting simplified non-linear epidemic models to the deaths data or on spline based deconvolution approaches. The former runs the risk of introducing unintended artefacts via the model formulation, while the latter may be viewed as technically obscure, impeding uptake by practitioners. This note proposes a simple simulation based approach to inferring fatal incidence from deaths that requires minimal assumptions, is easy to understand, and allows testing of alternative hypothesized incidence trajectories. The aim is that in any future situation similar to the COVID pandemic, the method can be easily, rapidly, transparently, and uncontroversially deployed as an input to management.