The Data Science of COVID-19 Spread: Some Troubling Current and Future Trends

Abstract

One of the main ways we try to understand the COVID-19 pandemic is through time series cross section counts of cases and deaths. Observational studies based on these kinds of data have concrete and well known methodological issues that suggest significant caution for both consumers and produces of COVID-19 knowledge. We briefly enumerate some of these issues in the areas of measurement, inference, and interpretation.

Document Details

Document Type
Pub Defense Publication
Publication Date
Sep 01, 2020
Source ID
10.1515/peps-2020-0053

Entities

People

  • Erik Gartzke
  • Rex W. Douglass
  • Thomas Leo Scherer

Organizations

  • Charles Koch Foundation
  • Office of Naval Research
  • University of California, San Diego

Tags

Readers

  • Infectious Disease/Epidemiology
  • Regression Analysis.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - DoD AI Strategy