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Activity Number: 442 - Disease Prediction, Statistical Methods for Genetic Epidemiology and Mis
Type: Contributed
Date/Time: Thursday, August 12, 2021 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #318039
Title: Selection Bias Adjustment in Infectious Disease Outbreak Data
Author(s): Mingjin Liu* and Yang Yang
Companies: University of Florida and University of Florida
Keywords: Surveillance Data; Selection Bias; Final Size Model

Analyzing outbreak surveillance data can help us assess temporal and spatial variation in transmissibility of various infectious diseases (e.g. influenza subtypes) at individual level and can also help us assess the effectiveness of intervention of programs. But outbreak surveillance data often has selection bias, where outbreaks were reported to and investigated only when the number of identified cases exceeding predefined surveillance threshold (e.g. 10 or 30 cases). Selection bias can lead to biased estimates of transmissibility if no adjustment being applied or simply treating first 10 or 30 (surveillance threshold) cases as index cases. Here we propose selection bias adjustment by reformulated final-size models, which has good and efficient estimation of transmission probabilities.

Authors who are presenting talks have a * after their name.

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