Adaptive metrics for an evolving pandemic A dynamic approach to area-level COVID-19 risk designations.
Alyssa M BilinskiJoshua A SalomonLaura A HatfieldPublished in: medRxiv : the preprint server for health sciences (2023)
In the rapidly-evolving COVID-19 pandemic, public health risk metrics often become less relevant over time. Risk metrics are designed to predict future severe disease and mortality based on currently-available surveillance data, such as cases and hospitalizations. However, the relationship between cases, hospitalizations, and mortality has varied considerably over the course of the pandemic, in the context of new variants and shifts in vaccine- and infection-induced immunity. We propose an adaptive approach that regularly updates metrics based on the relationship between surveillance inputs and future outcomes of policy interest. Our method captures changing pandemic dynamics, requires only hospitalization input data, and outperforms static risk metrics in predicting high-risk states and counties.
Keyphrases
- coronavirus disease
- sars cov
- health risk
- public health
- healthcare
- mental health
- electronic health record
- current status
- cardiovascular events
- risk factors
- emergency department
- type diabetes
- respiratory syndrome coronavirus
- early onset
- drug induced
- coronary artery disease
- metabolic syndrome
- machine learning
- cardiovascular disease
- dna methylation
- endothelial cells