Estimation of the Generation Interval Using Pair-Wise Relative Transmission Probabilities

• Activity Number: 319 - Innovative Approaches to the Study of an Epidemic
• Type: Contributed
• Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 2:00 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Sarah Leavitt* and Helen E Jenkins and Paola Sebastiani and Robyn S Lee and C. Robert Horsburg and Andrew M Tibbs and Laura F. White
• Companies: Boston University and Boston University and Boston University and University of Toronto and Boston University and Massachusetts Department of Public Health and Boston University
• Keywords: tuberculosis; hierarchical clustering; kernel density estimation; reproductive number; noise reduction; serial interval

Abstract

The generation interval (the time between infection of primary and secondary cases) and its often used proxy, the serial interval (the time between symptom onset of primary and secondary cases) are critical parameters in understanding infectious disease dynamics. Because it is difficult to determine who infected whom, these important outbreak characteristics are not well understood for many diseases. We present a novel method for estimating the generation or serial interval using surveillance or outbreak investigation data that, unlike existing methods, does not require a contact tracing data or pathogen whole genome sequence data on all cases. We start with an estimation maximization algorithm developed by Hens et al. (2012) and incorporate relative transmission probabilities with noise reduction. We use simulations to show that our method accurately estimates the mean of the generation interval distribution for diseases with different reproductive numbers, generation intervals, and mutation rates. We then apply our method to routinely collected surveillance data from Massachusetts (2010-2016) to estimate the serial interval of tuberculosis in this setting.