Aaron Gerding

I build, evaluate, and combine (mainly probabilistic) forecasts of the incidence and outcomes of infectious diseases.

My work is anchored in the goal of understanding and formalizing how forecasts can be structured and presented to best support adaptive public health policy. In order for epidemiogical forecasting to lead to policy that is both just and effective, I see it as crucial that operational statistical methodologies retain a special focus on those populations with the lowest social and political visibility.

Previously I studied the differential geometry of surfaces immersed in space forms from the perspective of integrable systems theory and explored information geometry while transitioning to data science.

selected publications

Evaluating infectious disease forecasts with allocation scoring rules

Aaron Gerding, Nicholas G Reich, Benjamin Rogers, and Evan L Ray

Journal of the Royal Statistical Society Series A: Statistics in Society, Dec 2024

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Recent years have seen increasing efforts to forecast infectious disease burdens, with a primary goal being to help public health workers make informed policy decisions. However, there has been only limited discussion of how predominant forecast evaluation metrics might indicate the success of policies based in part on those forecasts. We explore one possible tether between forecasts and policy: the allocation of limited medical resources so as to minimize unmet need. We use probabilistic forecasts of disease burden in each of several regions to determine optimal resource allocations, and then we score forecasts according to how much unmet need their associated allocations would have allowed. We illustrate with forecasts of COVID-19 hospitalizations in the U.S., and we find that the forecast skill ranking given by this allocation scoring rule can vary substantially from the ranking given by the weighted interval score. We see this as evidence that the allocation scoring rule detects forecast value that is missed by traditional accuracy measures and that the general strategy of designing scoring rules that are directly linked to policy performance is a promising direction for epidemic forecast evaluation.