The spatial spread of highly pathogenic avian influenza (HPAI) H5N2 during the 2015 outbreak in the U.S. state of Minnesota was analyzed through the estimation of a spatial transmission kernel, which quantifies the infection hazard an infectious premises poses to an uninfected premises some given distance away. Parameters were estimated using a maximum likelihood method for the entire outbreak as well as for two phases defined by the daily number of newly detected HPAI-positive premises. The results indicate both a strong dependence of the likelihood of transmission on distance and a significant distance-independent component of outbreak spread for the overall outbreak. The results further suggest that HPAI spread differed during the later phase of the outbreak. The estimated spatial transmission kernel was used to compare the Minnesota outbreak with previous HPAI outbreaks in the Netherlands and Italy to contextualize the Minnesota transmission kernel results and make additional inferences about HPAI transmission during the Minnesota outbreak. Lastly, the spatial transmission kernel was used to identify high risk areas for HPAI spread in Minnesota. Risk maps were also used to evaluate the potential impact of an early marketing strategy implemented by poultry producers in a county in Minnesota during the outbreak, with results providing evidence that the strategy was successful in reducing the potential for HPAI spread.

Timely diagnosis of influenza A virus infections is critical for outbreak control. Due to their rapidity and other logistical advantages, lateral flow immunoassays can support influenza A virus surveillance programs and here, their field performance was proactively assessed. The performance of real-time polymerase chain reaction and two lateral flow immunoassay kits (FluDETECT and VetScan) in detecting low pathogenicity influenza A virus in oropharyngeal swab samples from experimentally inoculated broiler chickens was evaluated and at a flock-level, different testing scenarios were analyzed. For real-time polymerase chain reaction positive individual-swabs, FluDETECT respectively detected 37% and 58% for the H5 and H7 LPAIV compared to 28% and 42% for VetScan. The mean virus titer in H7 samples was higher than for H5 samples. For real-time polymerase chain reaction positive pooled swabs (containing one positive), detections by FluDETECT were significantly higher in the combined 5- and 6-swab samples compared to 11-swab samples. FluDETECT detected 58%, 55.1% and 44.9% for the H7 subtype and 28.3%, 34.0% and 24.6% for the H5 in pools of 5, 6 and 11 respectively. In our testing scenario analysis, at low flock-level LPAIV infection prevalence, testing pools of 11 detected slightly more infections while at higher prevalence, testing pools of 5 or 6 performed better. For highly pathogenic avian influenza virus, testing pools of 11 (versus 5 or 6) detected up to 5% more infections under the assumption of similar sensitivity across pools and detected less by 3% when its sensitivity was assumed to be lower. Much as pooling a bigger number of swab samples increases the chances of having a positive swab included in the sample to be tested, this study’s outcomes indicate that this practice may actually reduce the chances of detecting the virus since it may result into lowering the virus titer of the pooled sample. Further analysis on whether having more than one positive swab in a pooled sample would result in increased sensitivity for low pathogenicity avian influenza virus is needed.

Limiting 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 estimated for each house. The length of the 95% credibility intervals ranged from 11 to 22 with a mean of 17 days. 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. The estimated modes ranged from 0.5 to 6.0 with a mean of 2.8 contacts per day. 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.

Avian influenza (AI) is an infectious viral disease that affects several species and has zoonotic potential. Due to its associated health and economic repercussions, minimizing AI outbreaks is important. However, most control measures are generic and mostly target pathways important for the conventional poultry farms producing chickens, turkeys, and eggs and may not target other pathways that may be specific to the upland game bird sector. The goal of this study is to provide evidence to support the development of novel strategies for sector-specific AI control by comparing and contrasting practices and potential pathways for spread in upland game bird farms with those for conventional poultry farms in the United States. Farm practices and processes, seasonality of activities, geographic location and inter-farm distance were analyzed across the sectors. All the identified differences were framed and discussed in the context of their associated pathways for virus introduction into the farm and subsequent between-farm spread. Differences stemming from production systems and seasonality, inter-farm distance and farm densities were evident and these could influence both fomite-mediated and local-area spread risks. Upland game bird farms operate under a single, independent owner rather than being contracted with or owned by a company with other farms as is the case with conventional poultry. The seasonal marketing of upland game birds, largely driven by hunting seasons, implies that movements are seasonal and customer-vendor dynamics vary between industry groups. Farm location analysis revealed that, on average, an upland game bird premises was 15.42 km away from the nearest neighboring premises with birds compared to 3.74 km for turkey premises. Compared to turkey premises, the average poultry farm density in a radius of 10 km of an upland game bird premises was less than a half, and turkey premises were 3.8 times (43.5% compared with 11.5%) more likely to fall within a control area during the 2015 Minnesota outbreak. We conclude that the existing differences in the seasonality of production, isolated geographic location and epidemiological seclusion of farms influence AI spread dynamics and therefore disease control measures should be informed by these and other factors to achieve success.

Movement restrictions are a critical component of response plans for an African swine fever (ASF) outbreak in the United States. These restrictions are likely to include requiring permits to move animals and products within, into, and out of 5‐km control areas (CAs) established around confirmed positive farms. For quarantined finishing farms located within a CA, diagnostic testing is an expected criterion for receival of a permit to move pigs to a harvest facility or removal of quarantine. A stochastic disease transmission and active surveillance model were used to evaluate premovement active surveillance protocols varying by the number of samples and timing of sample collection before movement. Surveillance protocol scenarios were evaluated for several different sampling prioritization schemes; virus strains of medium or high virulence; barn sizes of 1,200, 2,400, and 4,800 pigs; and farms with average to high mortality and morbidity during routine production. Surveillance protocols that included prioritization schemes targeting dead pigs and pigs with clinical signs resulted in the highest probabilities of detection and the lowest numbers of infectious pigs at the time of movement in barns that went undetected. There was some evidence that targeting sick pigs prior to dead pigs may be more effective for moderately virulent strains. However, in most scenarios, including all highly virulent strain scenarios and moderately virulent strain scenarios in barn sizes of 1,200 with average farm performance, prioritization of dead versus sick pigs first did not have a large impact on the predicted outcomes. Increasing sample sizes improved outcomes, though only marginal gains were achieved once the available dead and sick were sampled. Predicted outcomes may be further improved by sampling the available dead and sick pigs in a barn across multiple days, though the associated increase in the probability of detection was minor.

In 2022, highly pathogenic avian influenza virus (HPAIV) H5N1 clade 2.3.4.4b was detected in United States (US) poultry, quickly escalating into an outbreak that surpassed the 2014/15 HPAI US event in scale and impact. Unlike in 2014/15, the 2022/3/4/5 outbreak has included numerous HPAI detections in previously infrequently affected commodities such as broilers and commercially-raised upland game birds. Here, we describe H5 HPAI detections that occurred between December 2023 and January 2024 in a multipremises, commerical upland game bird system located in the Midwestern US. We used an approximate Bayesian computation algorithm and stochastic within-flock HPAI transmission model to estimate the following: (1) time of HPAI introduction onto each affected premises, (2) number of days between estimated times of introduction and times of detection, and (3) the adequate contact rates and basic reproduction numbers within individual barns. Clinical signs and mortality observed in the infected pheasant flocks were largely consistent with other pheasant H5 clade 2.3.4.4b outbreaks. Across the system, the estimated transmission metrics were noticeably lower than those calculated from outbreaks in other poultry species. However, times to detection were similar to HPAI outbreaks that have occurred in other commodities.