Implementation of a Prospective Index-Cluster Sampling Strategy for the Detection of Presymptomatic Viral Respiratory Infection in Undergraduate Students.

Background

Index-cluster studies may help characterize the spread of communicable infections in the presymptomatic state. We describe a prospective index-cluster sampling strategy (ICSS) to detect presymptomatic respiratory viral illness and its implementation in a college population.

Methods

We enrolled an annual cohort of first-year undergraduates who completed daily electronic symptom diaries to identify index cases (ICs) with respiratory illness. Investigators then selected 5-10 potentially exposed, asymptomatic close contacts (CCs) who were geographically co-located to follow for infections. Symptoms and nasopharyngeal samples were collected for 5 days. Logistic regression model-based predictions for proportions of self-reported illness were compared graphically for the whole cohort sampling group and the CC group.

Results

We enrolled 1379 participants between 2009 and 2015, including 288 ICs and 882 CCs. The median number of CCs per IC was 6 (interquartile range, 3-8). Among the 882 CCs, 111 (13%) developed acute respiratory illnesses. Viral etiology testing in 246 ICs (85%) and 719 CCs (82%) identified a pathogen in 57% of ICs and 15% of CCs. Among those with detectable virus, rhinovirus was the most common (IC: 18%; CC: 6%) followed by coxsackievirus/echovirus (IC: 11%; CC: 4%). Among 106 CCs with a detected virus, only 18% had the same virus as their associated IC. Graphically, CCs did not have a higher frequency of self-reported illness relative to the whole cohort sampling group.

Conclusions

Establishing clusters by geographic proximity did not enrich for cases of viral transmission, suggesting that ICSS may be a less effective strategy to detect spread of respiratory infection.

Informations de copyright

© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America.

Déclaration de conflit d'intérêts

Potential conflicts of interest. M. T. M. reports grants from the Defense Advanced Research Projects Agency (DARPA) and the National Institutes of Health (NIH), and has a patent pending on “Methods to diagnose and treat acute respiratory infections.” T. W. B. reports grants from DARPA and NIH; reports owning equity in and serving as a consultant for Biomeme; and has a patent pending on Methods to diagnose and treat acute respiratory infections. E. L. T. reports consultancy fees and equity from Biomeme; has patents pending on Biomarkers for the molecular classification of bacterial infection and Methods to diagnose and treat acute respiratory infections; and is currently an employee of Danaher Diagnostics. C. W. W. and G. S. G. have patents pending on Molecular classification of bacterial infection and gene expression signatures useful to predict or diagnose sepsis and methods of using the same, and have patents issued on Methods to diagnose and treat acute respiratory disease and Methods of identifying infectious disease and assays for identifying infectious disease. C. W. W. reports owning equity in and consulting for Biomeme; reports grants from DARPA, NIH, Antibacterial Resistance Leadership Group, and Sanofi; and has received consultancy fees from bioMérieux, Roche, Biofire, Giner, and Biomeme. All other authors report no potential conflicts.