Impact of Virus Strain Characteristics on Early Detection of Highly Pathogenic Avian Influenza Infection in Commercial Table-Egg Layer Flocks and Implications for Outbreak Control

Abstract

Early detection of highly pathogenic avian influenza (HPAI) infection in commercial poultry flocks is a critical component of outbreak control. Reducing the time to detect HPAI infection can reduce the risk of disease transmission to other flocks. The timeliness of different types of detection triggers could be dependent on clinical signs that are first observed in a flock, signs that might vary due to HPAI virus strain characteristics. We developed a stochastic disease transmission model to evaluate how transmission characteristics of various HPAI strains might effect the relative importance of increased mortality, drop in egg production, or daily real-time reverse transcriptase (RRT)-PCR testing, toward detecting HPAI infection in a commercial table-egg layer flock. On average, daily RRT-PCR testing resulted in the shortest time to detection (from 3.5 to 6.1 days) depending on the HPAI virus strain and was less variable over a range of transmission parameters compared with other triggers evaluated. Our results indicate that a trigger to detect a drop in egg production would be useful for HPAI virus strains with long infectious periods (6-8 days) and including an egg-drop detection trigger in emergency response plans would lead to earlier and consistent reporting in some cases. We discuss implications for outbreak control and risk of HPAI spread attributed to different HPAI strain characteristics where an increase in mortality or a drop in egg production or both would be among the first clinical signs observed in an infected flock.

Risk Reduction Modeling of High Pathogenicity Avian Influenza Virus Titers in Nonpasteurized Liquid Egg Obtained from Infected but Undetected Chicken Flocks

“…It is important to note that the RRT‐PCR disease detection trigger evaluated here is a form of targeted active surveillance intended to result in the earliest time to detection in order to reduce the spread of HPAI virus to another poultry premises . Efficient HPAI disease detection is conditional on the presence of chickens infected with HPAI virus in the dead bird pool, in combination with rapidly increasing mortality observed for most HPAI strains.…”

“…In order to estimate the number of contaminated eggs laid by HPAI‐infected chickens in a flock to be presented for breaking, a conservative assumption was made that the flock is in an infected, but undetected, disease state. A stochastic, within‐flock, disease spread simulation model was used to simulate the spread of three different strains of HPAI virus after the introduction of one infected chicken into the flock (house) of 100,000 table‐egg‐layer hens . The three HPAI virus strains were chosen to represent the natural variability inherent in the length of the infectious period, which impacts within‐flock virus transmission characteristics and, ultimately, the time to detection of HPAI infection in the flock.…”

“…The three strains were Guangdong lineage H5N1 HPAI virus (2.1 day [short time to death] estimated mean infectious period), the 1983 Pennsylvania H5N2 HPAI virus (4.1 day [medium time to death] estimated mean infectious period), and the 2003 Netherlands H7N7 HPAI virus (6.1 day [long time to death] estimated mean infectious period). Estimations of the approximate mean infectious period length for each virus were taken from previous work . The effective contact rate impacts the rate of within‐flock disease spread in simulation models.…”

“…Two mean effective contact rates were used in this study to consider the uncertainty in this parameter. A contact rate of 2 chickens/6 hours was chosen to represent faster spread considering estimates from published studies on HPAI transmission in caged layers in other countries . A description of the model variables used in this analysis is provided in Table and values for model input parameters and constants are provided in Table .…”

Using quantitative disease dynamics as a tool for guiding response to avian influenza in poultry in the United States of America

“…For an example of the latter, a within-flock model showed that vaccines conferring incomplete protection could counterintuitively increase the infectiousness of a flock, by delaying the time to detection and depopulation (Savill et al, 2006). Within-flock models have also been used to: (1) infer important events – such as the day of introduction – from mortality data (Bos et al, 2007), (2) estimate the risk of HPAIV transmission due to the movement of poultry-industry products from monitored flocks (i.e., flocks not known to be infected with HPAIV) into commerce (Malladi et al, 2012), (3) estimate important epidemiological parameters (Bouma et al, 2009) and (4) estimate mean time to detection for HPAIV strains under different surveillance-detection triggers (Weaver et al, 2012). …”