Assessment of replicate numbers for titrating avian influenza virus using dose-response models

Erica Spackman, Sasidhar Malladi, Amos Ssematimba, Christopher B. Stephens

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Embryonating chicken eggs (ECEs) are among the most sensitive laboratory host systems for avian influenza virus (AIV) titration, but ECEs are expensive and require space for storage and incubation. Therefore, reducing ECE use would conserve resources. We utilized statistical modeling to evaluate the accuracy and precision of AIV titration with 3 instead of 5 ECEs for each dilution by the Reed–Muench method for 50% endpoint calculation. Beta-Poisson and exponential dose-response models were used in a simulation study to evaluate observations from actual titration data from 18 AIV isolates. The reproducibility among replicates of a titration was evaluated with one AIV isolate titrated in 3 replicates with the beta-Poisson, exponential, and Weibull dose-response models. The standard deviation (SD) of the error between input and estimated virus titers was estimated with Monte Carlo simulations using the fitted dose-response models. Good fit was observed with all models that were utilized. Reducing the number of ECEs per dilution from 5 to 3 resulted in the width of the 95% confidence interval increasing from ±0.64 to ±0.75 log10 50% ECE infectious doses (EID50) and the SD of the error increased by 0.03 log10 EID50. Our study suggests that using fewer ECEs per dilution is a viable approach that will allow laboratories to reduce costs and improve efficiency.

Original languageEnglish (US)
Pages (from-to)616-619
Number of pages4
JournalJournal of Veterinary Diagnostic Investigation
Issue number4
StatePublished - Jul 1 2019

Bibliographical note

Funding Information:
This research was supported by U.S. Department of Agriculture, ARS CRIS Project 6040-32000-066-00D.

Publisher Copyright:
© 2019 The Author(s).


  • avian influenza virus
  • embryonating chicken egg titration
  • virus quantification


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