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.
Download Paper Abstract Forecasts support decision making in a variety of applications. Statistical models can produce accurate forecasts given abundant training data, but when data is sparse or rapidly changing, statistical models may not be able to make accurate predictions. Expert judgmental forecasts—models that combine expert-generated predictions into a single forecast—can make predictions when training data is limited by relying on human intuition. Researchers have proposed a wide array of algorithms to combine expert predictions into a single forecast, but there is no consensus on an optimal aggregation model....
Compared to fixed effect models, random effects suffer lower bias and trade inflated type I errors for improved power. Contrasting hazard rates between trials prevent accurate estimates from both fixed and random effects models.
Crossing survival curves complicate how we interpret results from a clinical trial’s primary endpoint. We find the function to determine a crossing point’s location depends exponentially on individual survival curves. This exponential relationship between survival curves and the crossing point transforms small survival curve errors into large crossing point errors. In most cases, crossing points are sensitive to individual survival errors and may make accurately locating a crossing point unsuccessful. We argue more complicated analyses for mitigating crossing points should be reserved only after first exploring a crossing point’s variability, or hypothesis tests account for crossing point variability....
By modeling question sets as networks of interrelated questions, we introduce algorithms to help curtail the growth bias by efficiently distributing workers between exploring new questions and addressing current questions.
We find that causal statements have a number of significant lexical and grammatical differences compared to non-causal language
Accounting for spatial effects in both construction and robustness is important for designing efficient microgrids and other network infrastructure.