Addressing delayed case reporting in infectious disease forecast modeling.

ABSTRACT: Infectious disease forecasting is of great interest to the public health community and policymakers, since forecasts can provide insight into disease dynamics in the near future and inform interventions. Due to delays in case reporting, however, forecasting models may often underestimate the current and future disease burden. In this paper, we propose a general framework for addressing reporting delay in disease forecasting efforts with the goal of improving forecasts. We propose strategies for leveraging either historical data on case reporting or external internet-based data to estimate the amount of reporting error. We then describe several approaches for adapting general forecasting pipelines to account for under- or over-reporting of cases. We apply these methods to address reporting

SUBMITTER: Beesley LJ 

PROVIDER: S-EPMC9200328 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature