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. 2021 Nov 1;32(6):820-828.
doi: 10.1097/EDE.0000000000001415.

Estimating Vaccine Efficacy Against Transmission via Effect on Viral Load

Lee Kennedy-Shaffer  1 , Rebecca Kahn  2   3 , Marc Lipsitch  2   3
Affiliations

Estimating Vaccine Efficacy Against Transmission via Effect on Viral Load

Lee Kennedy-Shaffer et al. Epidemiology. .

Abstract

Determining policies to end the SARS-CoV-2 pandemic will require an understanding of the efficacy and effectiveness (hereafter, efficacy) of vaccines. Beyond the efficacy against severe disease and symptomatic and asymptomatic infection, understanding vaccine efficacy against virus transmission, including efficacy against transmission of different viral variants, will help model epidemic trajectory and determine appropriate control measures. Recent studies have proposed using random virologic testing in individual randomized controlled trials to improve estimation of vaccine efficacy against infection. We propose to further use the viral load measures from these tests to estimate efficacy against transmission. This estimation requires a model of the relationship between viral load and transmissibility and assumptions about the vaccine effect on transmission and the progress of the epidemic. We describe these key assumptions, potential violations of them, and solutions that can be implemented to mitigate these violations. Assessing these assumptions and implementing this random sampling, with viral load measures, will enable better estimation of the crucial measure of vaccine efficacy against transmission.

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Conflict of interest statement

L.K.-S. reports no competing interests. M.L. reports consulting/honoraria from Bristol Myers Squibb, Sanofi Pasteur, Janssen, and Merck, as well as a grant through his institution, unrelated to COVID-19, from Pfizer. He has served as an unpaid advisor related to COVID-19 to Pfizer, One Day Sooner, Astra-Zeneca, Janssen, and COVAX (United Biomedical). R.K. discloses consulting fees from Partners in Health.

Figures

FIGURE 1.
FIGURE 1.
Directed acyclic graph for assumption 2. Vaccination can affect transmission through its effect on infection, its effect on duration of infectiousness, and its effect on the measured viral load, a proxy for its effect on infectiousness at any point in time through the infectiousness function g (dashed arrow). The rate of contact with susceptibles, however, is not affected by the vaccine (A). If the rate of contact with susceptibles is affected by the viral load of the infected individual (e.g., through changing contact patterns because of symptoms), the infectiousness function g (dashed arrows) must account for this relationship between measured viral load and transmission probability (B).
FIGURE 2.
FIGURE 2.
Directed acyclic graph for assumption 4. Vaccination can affect transmission only through the measured viral load and measured covariates (e.g., symptom status). Any effect modifiers of the relationship between the measured viral load at any point in time and transmission (e.g., age, comorbidities) are measured and adjusted for through the infectiousness function g (dashed arrows).
See this image and copyright information in PMC

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