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, because such infections may contribute to onward transmission and long-term sequelae, such as congenital Zika syndrome. However, estimating VE(S) is resource-intensive. We aimed to identify approaches for accurately estimating VE(S) when limited information is available and resources are constrained. We modeled an individually randomized vaccine trial by generating a network of individuals and simulating an epidemic. The disease natural history followed a “susceptible-exposed-infectious/symptomatic (or infectious/asymptomatic)-recovered” model. We then used 7 approaches to estimate VE(S), and we also estimated 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 serological 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 the basic reproduction number (R(0)) and accurate estimates of VE(P) for higher R(0) 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.
Analyzing Vaccine Trials in Epidemics With Mild and Asymptomatic Infection