Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) is the most important endemic pathogen in the U.S. swine industry. Despite control efforts involving improved biosecurity and different vaccination protocols, the virus continues to circulate and evolve. One of the foremost challenges in its control is high levels of genetic and antigenic diversity. Here, we quantify the co-circulation, emergence and sequential turnover of multiple PRRSV lineages in a single swine-producing region in the United States over a span of 9 years (2009–2017). By classifying over 4,000 PRRSV sequences (open-reading frame 5) into phylogenetic lineages and sub-lineages, we document the ongoing diversification and temporal dynamics of the PRRSV population, including the rapid emergence of a novel sub-lineage that appeared to be absent globally pre-2008. In addition, lineage 9 was the most prevalent lineage from 2009 to 2010, but its occurrence fell to 0.5% of all sequences identified per year after 2014, coinciding with the emergence or re-emergence of lineage 1 as the dominant lineage. The sequential dominance of different lineages, as well as three different sub-lineages within lineage 1, is consistent with the immune-mediated selection hypothesis for the sequential turnover in the dominant lineage. As host populations build immunity through natural infection or vaccination toward the most common variant, this dominant (sub-) lineage may be replaced by an emerging variant to which the population is more susceptible. An analysis of patterns of non- synonymous and synonymous mutations revealed evidence of positive selection on immunologically important regions of the genome, further supporting the potential that immune-mediated selection shapes the evolutionary and epidemiological dynamics for this virus. This has important implications for patterns of emergence and re-emergence of genetic variants of PRRSV that have negative impacts on the swine industry. Constant surveillance on PRRSV occurrence is crucial to a better understanding of the epidemiological and evolutionary dynamics of co-circulating viral lineages. Further studies utilizing whole genome sequencing and exploring the extent of cross-immunity between heterologous PRRS viruses could shed further light on PRRSV immunological response and aid in developing strategies that might be able to diminish disease impact.

The spread of pathogens in swine populations is in part determined by movements of animals between farms. However, understanding additional characteristics that predict disease outbreaks and uncovering landscape factors related to between-farm spread are crucial steps toward risk mitigation. This study integrates animal movements with environmental risk factors to identify the occurrence of porcine epidemic diarrhea virus (PEDV) outbreaks. Using weekly farm-level incidence data from 332 sow farms, we applied machine-learning algorithms to quantify associations between risk factors and PEDV outbreaks with the ultimate goal of training predictive models and to identify the most important factors associated with PEDV occurrence. Our best algorithm was able to correctly predict whether an outbreak occurred during one-week periods with >80% accuracy. The most important predictors included pig movements into neighboring farms. Other important neighborhood attributes included hog density, environmental and weather factors such as vegetation, wind speed, temperature, and precipitation, and topographical features such as slope. Our neighborhood-based approach allowed us to simultaneously capture disease risks associated with long-distance animal movement as well as local spatial dynamics. The model presented here forms the foundation for near real-time disease mapping and will advance disease surveillance and control for endemic swine pathogens in the United States.

In human and veterinary medicine, there have been multiple reports of pathogens being airborne under experimental and field conditions, highlighting the importance of this transmission route. These studies shed light on different aspects related to airborne transmission such as the capability of pathogens becoming airborne, the ability of pathogens to remain infectious while airborne, the role played by environmental conditions in pathogen dissemination, and pathogen strain as an interfering factor in airborne transmission. Data showing that airborne pathogens originating from an infectious individual or population can infect susceptible hosts are scarce, especially under field conditions. Furthermore, even though disease outbreak investigations have generated important information identifying potential ports of entry of pathogens into populations, these investigations do not necessarily yield clear answers on mechanisms by which pathogens have been introduced into populations. In swine, the aerosol transmission route gained popularity during the late 1990’s as suspicions of airborne transmission of porcine reproductive and respiratory syndrome virus (PRRSV) were growing. Several studies were conducted within the last 15 years contributing to the understanding of this transmission route; however, questions still remain. This paper reviews the current knowledge and identifies knowledge gaps related to PRRSV airborne transmission.

The increasing availability and complexity of data has led to new opportunities and challenges in veterinary epidemiology around how to translate abundant, diverse, and rapidly growing ‘big’ data into meaningful insights for animal health. Big data analytics are used to understand health risks and minimize the impact of adverse animal health issues through identifying high-risk populations, combining data or processes acting at multiple scales through epidemiological modeling approaches, and harnessing high velocity data to monitor animal health trends and detect emerging health threats. The advent of big data requires the incorporation of new skills into veterinary epidemiology training, including, for example, machine learning and coding, in order to prepare a new generation of scientists and practitioners to engage with big data. Establishing pipelines to analyze big data in near real-time is the next step for progressing from simply having ‘big data’ to creating ‘smart data’, with the objective of improving understanding of health risks, effectiveness of management and policy decisions, and ultimately preventing or at least minimizing the impact of adverse animal health issues.

Porcine reproductive and respiratory syndrome virus genotype 2 (PRRSV-2) genetic diversity in the U.S. was assessed using a database comprising 10 years’ worth of sequence data obtained from swine production systems routine monitoring and outbreak investigations. A total of 26,831 ORF5 PRRSV-2 sequences from 34 production systems were included in this analysis. Within group mean genetic distance (i.e. mean proportion of nucleotide differences within ORF5) per year according to herd type was calculated for all PRRSV-2 sequences. The percent nucleotide difference between each sequence and the ORF5 sequences from four commercially available PRRSV-2 vaccines (Ingelvac PRRS MLV, Ingelvac PRRS ATP, Fostera PRRS, and Prevacent PRRS) within the same lineage over time was used to classify sequences in wild-type or vaccine-like. The mean ORF5 genetic distance fluctuated from 0.09 to 0.13, being generally smaller in years in which there was a relative higher frequency of dominant lineage. Vaccine-like sequences comprised about one fourth of sequences obtained through routine monitoring of PRRS. We found that lineage 5 sequences were mostly Ingelvac PRRS MLV-like. Lineage 8 sequences up to 2011 were 62.9% Ingelvac PRRS ATP-like while the remaining were wild-type viruses. From 2012 onwards, 51.9% of lineage 8 sequences were Ingelvac PRRS ATP-like, 45.0% were Fostera PRRS-like, and only 3.2% were wild-type. For lineage 1 sequences, 0.1% and 1.7% of the sequences were Prevacent PRRS-like in 2009–2018 and 2019, respectively. These results suggest that repeated introductions of vaccine-like viruses through use of modified live vaccines might decrease within-lineage viral diversity as vaccine-like strains become more prevalent. Overall, this compilation of private data from routine monitoring provides valuable information on PRRSV viral diversity.