Integrating epidemiological and genetic data with different sampling intensities into a dynamic model of respiratory syncytial virus transmission
Kombe, Ivy K;
Agoti, Charles N;
Munywoki, Patrick K;
Nokes, D James;
Medley, Graham F;
(2020)
Integrating epidemiological and genetic data with different sampling intensities into a dynamic model of respiratory syncytial virus transmission.
MedRxiv.
DOI: https://doi.org/10.1101/2020.03.08.20030742
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<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>Respiratory syncytial virus (RSV) is responsible for a significant burden of acute respiratory illness in children under 5 years old. Prior to rolling out any vaccination program, identification of the source of infant infections could further guide vaccination strategies.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>We extended a dynamic model calibrated at the individual host level initially fit to social-temporal data on shedding patterns to include whole genome sequencing data available at a lower sampling intensity.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>In this study population of 493 individuals with 55 infants under the age of 1 year distributed across 47 households, we found that 52% of RSV-B and 60% of RSV-A cases arose from infection within the household. Forty-five percent of infant infections appeared to occur in the household, of which 68% were a result of transmission from a child aged between 2 and 13 years living in the same household as the infant.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>These results further highlight the importance of pre-school and school-aged children in RSV transmission, particularly the role they play in directly infecting the household infant. These age groups are a potential RSV vaccination target group.</jats:p></jats:sec>