|BONNEY, PETER - University Of Minnesota|
|MALLADI, SASIDHAR - University Of Minnesota|
|SSEMATIMBA, AMOS - University Of Minnesota|
|TORCHETTI, MIA KIM - Animal And Plant Health Inspection Service (APHIS)|
|CULHANE, MARIE - University Of Minnesota|
|CARDONA, CAROL - University Of Minnesota|
Submitted to: Scientific Reports
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/4/2020
Publication Date: 1/15/2021
Citation: Bonney, P., Malladi, S., Ssematimba, A., Spackman, E., Torchetti, M., Culhane, M., Cardona, C. 2021. Estimating epidemiological parameters using diagnostic testing data from low pathogenicity avian influenza infected turkey houses. Scientific Reports. 11:1-10. https://doi.org/10.1038/s41598-021-81254-z.
Interpretive Summary: Good control of low virulence avian influenza virus is critical to maintaining bird health and food security. Mathematical models based on real world and experimental data can be used to elucidate how low virulence avian influenza virus spreads among turkey flocks. Here a model is developed that estimates when turkeys could have been infected with the virus during an outbreak in 2018. By determining a time window for infection it may be possible to trace how the virus was introduced, which could lead to prevention of future outbreaks. Understanding virus spread also informs disease surveillance and detection programs that can be developed to more accurately and efficiently detect infected flocks, thus preventing spread in future outbreaks.
Technical Abstract: Limiting the spread of low pathogenicity avian influenza (LPAI) during an outbreak is critical to reduce the negative impact on poultry producers and local economies. Mathematical models of disease transmission can support outbreak control efforts by estimating relevant epidemiological parameters. In this article, diagnostic testing data from each house on a premises infected during a LPAI H5N2 outbreak in the state of Minnesota in the United States in 2018 was used to estimate the time of virus introduction and adequate contact rate, which determines the rate of disease spread. A well-defined most likely time of virus introduction, and upper and lower 95% credibility intervals were able to be estimated for each house. In some houses the contact rate estimates were also well-defined; however, the estimated upper 95% credibility interval bound for the contact rate was occasionally dependent on the upper bound of the prior distribution. These estimates can be improved with early detection, increased testing of monitored premises, and combining the results of multiple barns that possess similar production systems.