期刊
AMERICAN JOURNAL OF EPIDEMIOLOGY
卷 188, 期 2, 页码 467-474出版社
OXFORD UNIV PRESS INC
DOI: 10.1093/aje/kwy239
关键词
asymptomatic infection; epidemics; infectious diseases; interval censoring; modeling; vaccine trials
资金
- National Institute of General Medical Sciences [U54GM088558]
- National Institute of Allergy and Infectious Diseases [K01AI125830, R37 AI051164]
Vaccine efficacy against susceptibility to infection (VES), 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 VES is resource-intensive. We aimed to identify approaches for accurately estimating VES 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 VES, and we also estimated vaccine efficacy against progression to symptoms (VEP). A corrected relative risk and an interval-censored Cox model accurately estimate VES 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 VES estimates across values of the basic reproduction number (R-0) and accurate estimates of VEP for higher R-0 values. Identifying resource-preserving methods for accurately estimating VES and VEP is important in designing trials for diseases with a high proportion of asymptomatic infection.
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