DSA ADS Course - 2021
Forecasting, COVID19, John Ioannidis, Public Policy, Health Policy, Causal Inference, Forensic Medicine, Causality, Intuitive Causation, Probabilistic Causation
Forecasting is usually impossible in high causal density environments. Scenario planning with applied probability and adaptation to near real-time data is optimal strategy. Epidemic forecasting is usually a fools errand yet appropriate analysis of experience and historical precedent is helpful.
OBJECTIVE: This paper provides a formal evaluation of the predictive performance of a model (and updates) developed by the Institute for Health Metrics and Evaluation (IHME) for predicting daily deaths attributed to COVID-19 for the United States.
STUDY DESIGN: To assess the accuracy of the IHME models, we examine both forecast accuracy, as well as the predictive performance of the 95% prediction intervals (PI).
This paper demonstrates that an operational forecast model can skillfully predict week 3-4 averages of temperature and precipitation over the contiguous U.S.
R. DAVID MCLEAN, JEFFREY PONTIFF
Campbell R. Harvey, Yan Liu, Heqing Zhu