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Found 5 papers, 1 papers with code
Outlier Ranking in Large-Scale Public Health Streams
Disease control experts inspect public health data streams daily for outliers worth investigating, like those corresponding to data quality issues or disease outbreaks.
Correcting for heterogeneity in real-time epidemiological indicators
We describe the problem of spatial and temporal heterogeneity in these signals derived from these data sources, where spatial and/or temporal biases are present.
Federated Epidemic Surveillance
The surveillance of a pandemic is a challenging task, especially when crucial data is distributed and stakeholders cannot or are unwilling to share.
Computationally Assisted Quality Control for Public Health Data Streams
However, existing outlier detection frameworks perform poorly on this task because they do not account for the data volume or for the statistical properties of public health streams.
Recalibrating probabilistic forecasts of epidemics
We apply this recalibration method to the 27 influenza forecasters in the FluSight Network and show that recalibration reliably improves forecast accuracy and calibration.
