Analyzing Vaccine Trials in Epidemics with Mild and Asymptomatic Infection

Abstract

Vaccine efficacy against susceptibility to infection (VE_S), regardless of symptoms, is an important endpoint of vaccine trials for pathogens with a high proportion of asymptomatic infection, as such infections may contribute to onward transmission and long-term sequelae such as Congenital Zika Syndrome. However, estimating VE_S is resource-intensive. We aim to identify approaches to accurately estimate VE_S when limited information is available and resources are constrained. We model an individually randomized vaccine trial by generating a network of individuals and simulating an epidemic. The disease natural history follows a Susceptible, Exposed, Infectious/Symptomatic or Infectious/Asymptomatic, Recovered model. We then use seven approaches to estimate VE_S, and we also estimate vaccine efficacy against progression to symptoms (VE_P). A corrected relative risk and an interval censored Cox model accurately estimate VE_S and only require serologic testing of participants once, while a Cox model using only symptomatic infections returns biased estimates. Only acquiring serological endpoints in a 10% sample and imputing the remaining infection statuses yields unbiased VE_S estimates across values of R₀ and accurate estimates of VE_P for higher R₀ values. Identifying resource-preserving methods for accurately estimating VE_S and VE_P is important in designing trials for diseases with a high proportion of asymptomatic infection.