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Latent likelihood ratio tests for assessing spatial kernels in epidemic models.

David Thong1, George Streftaris2, Gavin J Gibson2

  • 1Maxwell Institute for Mathematical Sciences, Heriot-Watt University, Riccarton, Edinburgh, EH14 4AS, UK. david.y.w.t@gmail.com.

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Summary

Selecting the right transmission kernel is crucial for epidemic models. New latent likelihood ratio tests offer greater power than infection-link residuals for detecting spatial kernel mis-specification.

Keywords:
Bayesian inferenceLatent likelihood ratio testsLatent processesSpatio-temporal epidemic models

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Spatial Statistics

Background:

  • Accurate spatio-temporal epidemic models depend on appropriate transmission kernel selection.
  • Kernel choice significantly impacts control strategy design by influencing estimated transmission distances.
  • Existing model criticism methods require further development for spatial epidemic models.

Purpose of the Study:

  • Introduce and evaluate latent likelihood ratio tests for assessing spatial kernel validity in epidemic models.
  • Compare the power of latent likelihood ratio tests against infection-link residuals for detecting kernel mis-specification.
  • Provide a computationally efficient alternative to Bayesian methods for spatial kernel assessment.

Main Methods:

  • Developed latent likelihood ratio tests using likelihood-based discrepancy variables.
  • Compared the performance of latent likelihood ratio tests with infection-link residuals.
  • Utilized simulated epidemic data to assess model mis-specification detection capabilities.

Main Results:

  • Latent likelihood ratio tests demonstrate higher power in detecting kernel mis-specification compared to infection-link residuals.
  • The new tests are particularly effective when mis-specification is modest.
  • The approach avoids the computational complexity and prior sensitivity issues associated with fully Bayesian methods.

Conclusions:

  • Latent likelihood ratio tests provide a powerful and practical tool for scrutinizing spatial kernels in epidemic models.
  • This method enhances the reliability of epidemic models used for public health interventions.
  • The findings support the use of latent likelihood ratio tests for improving the accuracy of epidemiological predictions.