A framework for classifying disease trends applied to influenza-associated hospital admissions in the United States
Mathis, S. M.; Biggerstaff, M.; Budd, A.; O'Halloran, A.; Bozio, C.; Borchering, R. K.
Show abstract
We built a framework for categorizing week-to-week increases or decreases in seasonal influenza hospitalizations aiding data interpretation in the context of past seasons. Using Influenza Hospitalization Surveillance Network (FluSurv-NET) data, we established thresholds for weekly hospitalization rate differences and applied them to the 2022/23 and 2023/24 influenza seasons from the National Healthcare Safety Network. While the number of weeks categorized as stable was consistent across seasons, more large increases and decreases were observed in 2022/23. This metric captures hospitalization rate changes and contextualizes the current season relative to past influenza trends, facilitating trend assessment.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Using Capture-Recapture Methods to Estimate Influenza Hospitalization Incidence Rates 95%
- Detection of novel influenza viruses through community and healthcare testing: Implications for surveillance efforts in the United States 94%
- Interactions among common non-SARS-CoV-2 respiratory viruses and influence of the COVID-19 pandemic on their circulation in New York City 94%
Similar papers in this journal
- Understanding spatiotemporal clustering of seasonal influenza in the United States 96%
- Estimates of epidemiological parameters for H5N1 influenza in humans: a rapid review 91%
- Association between SARS-CoV-2 Infection and Select Symptoms and Conditions 31 to 150 Days After Testing among Children and Adults 90%
Similar papers in this journal
- Assessing the utility of COVID-19 case reports as a leading indicator for hospitalization forecasting in the United States 92%
- A prospective real-time transfer learning approach to estimate Influenza hospitalizations with limited data 92%
- Characterizing Potential Interaction Between Respiratory Syncytial Virus and Seasonal Influenza in the U.S. 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.