Interim estimates in null models of COVID-19 vaccine effectiveness

Abstract

Recently released interim numbers from advanced vaccine candidate clinical trials suggest that a COVID-19 vaccine effectiveness (VE) of >90% is achievable. However, SARS-CoV-2 transmission dynamics are highly heterogeneous and exhibit localized bursts of transmission, which may lead to sharp localized peaks in the number of new cases, often followed by longer periods of low incidence. Here we show that, for interim estimates of VE, these characteristic bursts in SARS-CoV-2 infection may introduce a strong positive bias in VE. Specifically, we generate null models of vaccine effectiveness, i.e., random models with bursts that over longer periods converge to zero VE but that for interim periods frequently produce apparent VE near 100%. As an example, by following the relevant clinical trial protocol, we can reproduce recently reported interim outcomes from an ongoing phase 3 clinical trial of an RNA-based vaccine candidate. Thus, to avoid potential random biases in VE, it is suggested that interim estimates on COVID-19 VE should control for the intrinsic inhomogeneity in both SARS-CoV-2 infection dynamics and reported cases.

Keywords

• SARS-CoV-2
• COVID-19
• Vaccine
• Vaccine candidate
• Vaccine effectiveness