Ruminant-dense environments increase risk of reported Shiga toxin-producing infections independently of ruminant contact.

Cattle and other domestic ruminants are the primary reservoirs of O157 and non-O157 Shiga toxin-producing (STEC). Living in areas with high ruminant density has been associated with excess risk of infection, which could be due to both direct ruminant contact and residual environmental risk, but the role of each is unclear. We investigated whether there is any meaningful risk to individuals living in ruminant-dense areas if they do not have direct contact with ruminants.

Human Health Surveillance During Animal Disease Emergencies: Minnesota Department of Health Response to Highly Pathogenic Avian Influenza Outbreaks, 2015 and 2022-2023.

Objectives: Highly pathogenic avian influenza (HPAI) poses an occupational risk for poultry workers, responders, and others in contact with infected birds. The objective of this analysis was to describe HPAI surveillance methods and outcomes, and highlight the challenges, successes, and lessons learned during the Minnesota Department of Health's (MDH's) public health response to HPAI outbreaks in Minnesota poultry flocks in the years 2015 and 2022-2023.

Methods: During both outbreaks, MDH staff attempted to contact all potentially exposed people and conduct a standardized interview.

Ruminant-dense environments increase risk of Shiga toxin-producing independently of ruminant contact.

Cattle and other domestic ruminants are the primary reservoirs of O157 and non-O157 Shiga toxin-producing (STEC). Living in areas with high ruminant density has been associated with excess risk of infection, which could be due to both direct ruminant contact and residual environmental risk, but the role of each is unclear. We investigated whether there is any meaningful risk to individuals living in ruminant-dense areas if they do not have direct contact with ruminants.

Improving and Extending STERGM Approximations Based on Cross-Sectional Data and Tie Durations.

Temporal exponential-family random graph models (TERGMs) are a flexible class of models for network ties that change over time. Separable TERGMs (STERGMs) are a subclass of TERGMs in which the dynamics of tie formation and dissolution can be separated within each discrete time step and may depend on different factors. The Carnegie et al.