
When Data Disappear: Public Health Pays As Policy Strays
Illustrating the importamce of public health data
Illustrating the importamce of public health data
We presented 3000 humans with simulated surveillance data about the number of incident hospitalizations from a current and two past seasons, and asked that they predict the peak time and intensity of the underlying epidemic
We collected – between May 19, 2022 and July 31, 2022 – 1275 forecasts from 442 individuals of six questions about the mpox outbreak where ground truth data are now available. Individual human judgment forecasts and an equally weighted ensemble were evaluated, as well as compared to a random walk, autoregressive, and doubling time model.
The Watermelon Meow Meow (WMM) outbreak is a cumulative experience that teaches upper-level undergraduate/graduate students about infectious disease dynamics by asking students to: participate in a fictitious outbreak
We propose a novel Cluster-Aggregate-Pool or `CAP’ ensemble algorithm that first clusters together individual forecasts, aggregates individual models that belong to the same cluster into a single forecast (called a cluster forecast), and then pools together cluster forecasts via a linear pool.
These results suggest that a human judgment forecasting platform can quickly generate probabilistic predictions for targets of public health importance
Our work shows that aggregated human judgment forecasts for infectious agents are timely, accurate, and adaptable, and can be used as a tool to aid public health decision making during outbreaks.
To support public health decision making, we solicited trained forecasters and experts in vaccinology and infectious disease to provide monthly probabilistic predictions from July to September of 2020 of the efficacy, safety, timing, and delivery of a COVID-19 vaccine.
Needing no data at the beginning of an epidemic, an adaptive ensemble can quickly train and forecast an outbreak, providing a practical tool to public health officials looking for a forecast to conform to unique features of a specific season.
This work highlights the importance that an expert linear pool could play in flexibly assessing a wide array of risks early in future emerging outbreaks, especially in settings where available data cannot yet support data-driven computational modeling.