Forecasting

Model Selection Using Database Characteristics: Developing a Classification Tree for Longitudinal Incidence Data - DSA ADS Course 2023

DSA ADS Course 2023

This DSA ADS course is part of a series of courses that demonstrate how to use applied data science with high performance compute and high quality data to optimize decision making in real world scenarios.

Discuss model selection and developing classification trees in real world scenarios.

The Cost-Benefit Fallacy: Why Cost-Benefit Analysis Is Broken and How to Fix It

DSA ADS Course - 2022

Cost-benefit Analysis, Cost-benefit Fallacy, Public Investment Planning, Forecasting, Resource Allocation, Behavioral Science

Discuss cost-benefit analysis, cost-benefit fallacy, public investment planning, forecasting, resource allocation, welfare economics, behavioral science and behavioral economics.

What if scenario estimates are highly inaccurate and biased? What are potential costs of scenario inaccuracies seriously distorting effective resource allocation?

Forecasting for COVID19 has Failed

June, 2020

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.

Examination of Statistical Accuracy of COVID-19 Daily Death Count Predictions

May, 2020

Abstract

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).

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