Contributions from the silent majority dominate dengue virus transmission

• Type of Material: Article
• Authors: Quirine A. Ten Bosch, University of Notre Dame; Hannah E. Clapham, Johns Hopkins University; Louis Lambrechts, Institut Pasteur; Veasna Duong, Institut Pasteur du Cambodge; Philippe Buchy, Institut Pasteur du Cambodge; Benjamin M. Althouse, Institute for Disease Modeling; Alun L. Lloyd, North Carolina State University; Lance Waller, Emory University; Amy C. Morrison, University of California at Davis; Uriel Kitron, Emory University; Gonzalo Vazquez Prokopec, Emory University; Thomas W. Scott, University of California at Davis; T. Alex Perkins, University of Notre Dame
• Language: English
• Date: 2018-05
• Publisher: Public Library of Science
• Publication Version: Final Published Version
• Copyright Statement: © 2018 ten Bosch et al
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1371/journal.ppat.1006965
• Title of Journal or Parent Work: PLoS Pathogens
• ISSN: 1553-7366
• Volume: 14
• Issue: 5
• Start Page: e1006965
• End Page: e1006965
• Grant/Funding Information: Research leading to results on infectiousness in asymptomatic individuals received funding from the European Union Seventh Framework Programme (FP7/2007/2011) under Grant Agreement 282 378.; HEC is supported by National Institutes of Health (www.nih.gov/) under award no. 5R01AI102939-03.; LL is supported by the French Government’s Investissement d’Avenir program, Laboratoire d’Excellence Integrative Biology of Emerging Infectious Diseases (grant ANR-10-LABX-62-IBEID (http://www.agence-nationale-recherche.fr/)) and the City of Paris (http://www.paris.fr/) Emergence(s) program in Biomedical Research.; ALL is supported by grant R01-AI091980 from the National Institutes of Health (www.nih.gov/) and by the National Science Foundation (www.nsf.gov) (RTG/DMS - 1246991).; This research was funded by a grant from the US National Institutes of Health – National Institute of Allergy and Infectious Diseases (NIH/NIAID (https://www.niaid.nih.gov/)) award number P01AI098670 (to TWS).
• Research Categories: Health Sciences, Public Health; Biology, Biostatistics; Biology, Bioinformatics

Abstract

Despite estimates that, each year, as many as 300 million dengue virus (DENV) infections result in either no perceptible symptoms (asymptomatic) or symptoms that are sufficiently mild to go undetected by surveillance systems (inapparent), it has been assumed that these infections contribute little to onward transmission. However, recent blood-feeding experiments with Aedes aegypti mosquitoes showed that people with asymptomatic and pre-symptomatic DENV infections are capable of infecting mosquitoes. To place those findings into context, we used models of within-host viral dynamics and human demographic projections to (1) quantify the net infectiousness of individuals across the spectrum of DENV infection severity and (2) estimate the fraction of transmission attributable to people with different severities of disease. Our results indicate that net infectiousness of people with asymptomatic infections is 80% (median) that of people with apparent or inapparent symptomatic infections (95% credible interval (CI): 0-146%). Due to their numerical prominence in the infectious reservoir, clinically inapparent infections in total could account for 84% (CI: 82-86%) of DENV transmission. Of infections that ultimately result in any level of symptoms, we estimate that 24% (95% CI: 0-79%) of onward transmission results from mosquitoes biting individuals during the pre-symptomatic phase of their infection. Only 1% (95% CI: 0.8-1.1%) of DENV transmission is attributable to people with clinically detected infections after they have developed symptoms. These findings emphasize the need to (1) reorient current practices for outbreak response to adoption of pre-emptive strategies that account for contributions of undetected infections and (2) apply methodologies that account for undetected infections in surveillance programs, when assessing intervention impact, and when modeling mosquito-borne virus transmission.