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Statistics Professor Vladimir Minin recently organized a special section on infectious diseases in Statistical Science in collaboration with Theodore Kypraios, an associate professor of mathematics at the University of Nottingham. The section, written for the broader statistical community, covers the state-of-the-art of statistical inference for stochastic epidemic models for infectious disease data. It includes the following six articles:

  • “Approximate Bayesian Computation and Simulation-Based Inference for Complex Stochastic Epidemic Models,”
  • “Comparison and Assessment of Epidemic Models,”
  • “Evidence Synthesis for Stochastic Epidemic Models,”
  • “Bayesian Nonparametrics for Stochastic Epidemic Models,”
  • “Modeling and Inference for Infectious Disease Dynamics: A Likelihood-based Approach” and
  • “Estimating Transmission from Genetic and Epidemiological Data: A Metric to Compare Transmission Trees.”

According to Minin, “the purpose was to introduce and review important statistical problems and advances in infectious disease research that have been made in recent years.”

The section also includes an introduction by Minin and Kypraios that discusses how the “ability to quickly unravel the dynamics of the spread of infectious diseases is important for effective prevention of future outbreaks and for control of ongoing ones.” They also point to ongoing challenges, such as model scalability and assessment, use of genetic data and data integration.

— Shani Murray