CorCast: A Distributed Architecture for Bayesian Epidemic Nowcasting and its Application to District-Level SARS-CoV-2 Infection Numbers in Germany

Abstract

Timely information on current infection numbers during an epidemic is of crucial importance for decision makers in politics, medicine, and businesses. As information about local infection risk can guide public policy as well as individual behavior, such as the wearing of personal protective equipment or voluntary social distancing, statistical models providing such insights should be transparent and reproducible as well as accurate. Fulfilling these requirements is drastically complicated by the large amounts of data generated during exponential growth of infection numbers, and by the complexity of common inference pipelines. Here, we present CorCast -- a stable and scalable distributed architecture for the reproducible estimation of nowcasts suitable for pandemic scenarios -- and its application to the inference of district-level SARS-CoV-2 infection numbers in Germany.

Nowcasting COVID-19 Deaths in England by Age and Region

“…However, it uses the more restrictive multinomial distribution for case reports, in place of S&E's (and our) GDM distribution. Hildebrandt et al (2021) described an implementation of Günther et al's method to nowcast cases in Germany at the level of district or state. Bergström et al (2022) extended Günther et al's method to incorporate additional data on a second event that reflects an earlier stage in disease progression than the event being nowcasted.…”

Addressing delayed case reporting in infectious disease forecast modeling

“…Classical modeling strategies for event time outcomes can be applied to estimate a delay distribution as a function of person-level predictors, which can then be aggregated to obtain estimates of π ts ( d ) overall. Approaches for handling settings where baseline disease diagnosis or symptom onset dates are unknown for patients in the historical dataset have been explored elsewhere [ 13 , 14 , 20 ].…”