Estimating within-flock transmission rate parameter for H5N2 highly pathogenic avian influenza virus in Minnesota turkey flocks during the 2015 epizootic

Abstract

Better control of highly pathogenic avian influenza (HPAI) outbreaks requires deeper understanding of within-flock virus transmission dynamics. For such fatal diseases, daily mortality provides a proxy for disease incidence. We used the daily mortality data collected during the 2015 H5N2 HPAI outbreak in Minnesota turkey flocks to estimate the within-flock transmission rate parameter (β). The number of birds in Susceptible, Exposed, Infectious and Recovered compartments was inferred from the data and used in a generalised linear mixed model (GLMM) to estimate the parameters. Novel here was the correction of these data for normal mortality before use in the fitting process. We also used mortality threshold to determine HPAI-like mortality to improve the accuracy of estimates from the back-calculation approach. The estimated β was 3.2 (95% confidence interval (CI) 2.3–4.3) per day with a basic reproduction number of 12.8 (95% CI 9.2–17.2). Although flock-level estimates varied, the overall estimate was comparable to those from other studies. Sensitivity analyses demonstrated that the estimated β was highly sensitive to the bird-level latent period, emphasizing the need for its precise estimation. In all, for fatal poultry diseases, the back-calculation approach provides a computationally efficient means to obtain reasonable transmission parameter estimates from mortality data.

Estimating the introduction time of highly pathogenic avian influenza into poultry flocks

“…An increase of the latent period by 2 days could increase the transmission rate up to 14.1 times. This sensitivity to changes in the latent period was also observed in other studies 20 , 24 , 30 and is probably caused by the effect of the latent period on the time interval from the moment an individual becomes infected until the first secondary case (generation time). Assuming that the length of the infectious period and the transmission rate remain constant, an increase in the latent period will decrease the growth rate of the number of infectious individuals, since the same amount of secondary infections is produced over a longer period.…”

“…A detailed description of the deterministic model is given in Supplementary Methods S3 . Similar models consisting of ordinary differential equations (ODEs) with gamma distributed disease stages have previously been applied to describe the dynamics of LPAI 15 and HPAI 24 in poultry flocks. We numerically integrated the ODEs using a method which applies a variable step size in order to balance computational efficiency with a sufficient level of accuracy (function lsoda in R package deSolve).…”

Mathematical modeling of COVID-19 transmission dynamics in Uganda: Implications of complacency and early easing of lockdown

“…The homogeneous-mixing approximation—translating into the fact that every individual in the population having an equal chance of interacting with any other individual)- is motivated by the observed transmission patterns in which there seems to be no geographic clusters whereas the frequency-dependence in the transmission term is deemed suitable since the population size is large. Moreover, if the population size remains more or less constant as an epidemic passes through, then density- and frequency-dependent models are equivalent [ 24 – 29 ]. The underlying compartmental model is of the SEIHR ( S ) format with the population split into compartments determined by their disease status.…”

Estimating epidemiological parameters using diagnostic testing data from low pathogenicity avian influenza infected turkey houses

“…The prior distributions for the contact rate and time of virus introduction were assumed to be uniform distributed. The distribution for the contact rate had a minimum of 0.1 and maximum of 6.0 contacts per day, which was set based on LPAI and HPAI contact rate estimates for turkeys in the literature 3,18,[26][27][28][29] . The latest time of virus introduction that was evaluated was the time of sampling for the first positive diagnostic test result observed in the data.…”