CVM Research
Permanent URI for this collection
Research continues to be one of the major missions of the College of Veterinary Medicine at Cornell University. As such, its faculty, students, and research scientists produce materials such as posters and datasets that describe or are related to their basic, applied and translational research activities and accomplishments.
This collection includes research outputs that have been submitted voluntarily by faculty, postdoctoral fellows, graduate students, veterinary students, technical personnel, and other research associates. Open access is provided under Creative Commons licenses.
Further information about the College’s research activities can be found at http://www.vet.cornell.edu/research/
Browse
Recent Submissions
Item Efficient Sample Size CalculatorHanley, Brenda J.; Booth, James G.; Hodel, Florian H.; Thompson, Noelle E.; Reeder, Ashley A.; Bloodgood, Jennifer C.; Dion, Jean-Philippe; Van de Berg, Sarah; Gonzalez-Crespo, Carlos; Huang, Yitong; Wang, Jue; Miller, Landon A.; Hollingshead, Nicholas A.; Peaslee, Jennifer L.; Schuler, Krysten L. (2025-06-06)Natural grouping behavior of hosts can reduce sample size requirements to estimate disease prevalence at a population scale. The Efficient Sample Size Calculator allows users to consider grouping tendencies of the host species to compute sample sizes needed to have 95% probability that disease prevalence in the population is at or below 1% or 2%. Allowable sampling schemes include simple random sampling, high-harvest sampling and two-stage cluster sampling. Examples cover a wide range of host species, diseases, and sampling schemes, and reveal that a well-designed sampling strategy may dramatically improve scientific efficiency over traditional sample size calculators without jeopardizing scientific rigor. Alternatively, an ill-designed sampling strategy may hamstring the ability for information from samples to reach the population scale. Novel statistical theory in Booth et al. (2024) and Booth et al. (2025).Item Data from: Synchronized seasonal excretion of multiple coronaviruses in Australian Pteropus spp is associated with co-infections in juvenile and subadult batsPeel, Alison J.; Ruiz-Aravena, Manuel; Kim, Karan; Scherting, Braden; Falvo, Caylee A.; Crowley, Dan; Munster, Vincent J.; Annand, Ed; Plain, Karren; Jones, Devin N.; Lunn, Tamika J.; Dale, Adrienne S.; Hoegh, Andrew; Eden, John-Sebastian; Plowright, Raina K. (2025-05-21)Item Data from: Periodic shifts in viral load increase risk of spillover from batsLunn, Tamika J.; Borremans, Benny; Jones, Devin N.; Kessler, Maureen K.; Dale, Adrienne, S.; Yinda, Kwe C.; Ruiz-Aravena, Manuel; Falvo, Caylee A.; Crowley, Dan; Lloyd-Smith, James O.; Munster, Vincent J.; Eby, Peggy; McCallum, Hamish; Hudson, Peter; Restif, Olivier; McGuire, Liam P.; Smith, Ina L.; Plowright, Raina K.; Peel, Alison J. (2025)This dataset and associated code files support all results reported in Lunn, et al., 2023 (https://doi.org/10.1101/2023.09.06.556454), where we found: Prediction and management of zoonotic pathogen spillover requires an understanding of infection dynamics within reservoir host populations. Transmission risk is often assessed using prevalence of infected hosts, with infection status based on the presence of genomic material. However, detection of viral genomic material alone does not necessarily indicate the presence of infectious virus, which could decouple prevalence from transmission risk. We undertook a multi-faceted investigation of Hendra virus shedding in Pteropus bats, combining insights from virus isolation, viral load proxies, viral prevalence, and longitudinal patterns of shedding, from 6,151 samples. In addition to seasonal and interannual fluctuation in prevalence, we found evidence for periodic shifts in the distribution of viral loads. The proportion of bats shedding high viral loads was higher during peak prevalence periods during which spillover events were observed, and lower during non-peak periods when there were no spillovers. We suggest that prolonged periods of low viral load and low prevalence reflect prolonged shedding of non-infectious RNA, or viral loads that are insufficient or unlikely to overcome dose barriers to spillover infection. These findings show that incorporating viral load (or proxies of viral load), into longitudinal studies of virus excretion will better inform predictions of spillover risk than prevalence alone.Item Code from: Periodic shifts in viral load increase risk of spillover from batsLunn, Tamika; Borremans, Benny (2025)This code, and the associated dataset, support the findings of the results reported in Lunn, et al, 2023 (https://doi.org/10.1101/2023.09.06.556454), where we found: Prediction and management of zoonotic pathogen spillover requires an understanding of infection dynamics within reservoir host populations. Transmission risk is often assessed using prevalence of infected hosts, with infection status based on the presence of genomic material. However, detection of viral genomic material alone does not necessarily indicate the presence of infectious virus, which could decouple prevalence from transmission risk. We undertook a multi-faceted investigation of Hendra virus shedding in Pteropus bats, combining insights from virus isolation, viral load proxies, viral prevalence, and longitudinal patterns of shedding, from 6,151 samples. In addition to seasonal and interannual fluctuation in prevalence, we found evidence for periodic shifts in the distribution of viral loads. The proportion of bats shedding high viral loads was higher during peak prevalence periods during which spillover events were observed, and lower during non-peak periods when there were no spillovers. We suggest that prolonged periods of low viral load and low prevalence reflect prolonged shedding of non-infectious RNA, or viral loads that are insufficient or unlikely to overcome dose barriers to spillover infection. These findings show that incorporating viral load (or proxies of viral load), into longitudinal studies of virus excretion will better inform predictions of spillover risk than prevalence alone.Item Evaluating a selective therapy approach to antimicrobial treatment of high-risk calves at arrival to a male dairy calf rearing facility on future health, growth and antimicrobial use: A group randomized controlled trial [Supplement]von Konigslow, Taika E.; Renaud, David L.; Duffield, Todd F.; Winder, Charlotte B.; Kelton, David F. (CVM Population Medicine and Diagnostic Sciences, 2025-01-03)Supplemental materials for Bovine Practitioner articleItem Sample size calculator for declaring a population free of infectious disease (Version 1)Hanley, Brenda J.; Booth, James G.; Hodel, Florian H.; Thompson, Noelle E.; Bloodgood, Jennifer, C. G.; Dion, Jean-Philippe; Van de Berg, Sarah; Gonzalez-Crespo, Carlos; Huang, Yitong; Wang, Jue; Miller, Landon A.; Hollingshead, Nicholas A.; Peaslee, Jennifer L.; Schuler, Krysten L. (2024)Scientists can leverage natural biological groupings of free-ranging wildlife to measure the prevalence of an infectious pathogen or disease. Specifically, correlation in disease status among individuals within natural groupings may be leveraged to conduct more efficient disease investigations. Unlike traditional sample size calculators, this calculator considers the natural grouping behavior of wild animals on the landscape and its effects on infectious disease transmission. Side-by-side output plots show potential sample savings afforded when correlation is considered relative to the same population where correlation is ignored. The statistical theory is depicted in Booth et al. (2023). This software contains only simple random sampling (SRS) although Booth et al. (2023) shows additional sampling schemes and remarks that scheme matters in sample size computations. We provide tutorials that show a variety of ways that this software can be used within a simple random sampling paradigm to plan real life wildlife health investigations. Tutorials include various diseases and pathogens in cervid species, mammals, herpetofauna, avians, and aquatic species. Later versions of this software will contain additional sampling schemes.Item Ecotoxicology Data Package: Contaminants in Migratory Waterfowl in the Northeast United StatesDayan, David; Hanley, Brenda J.; Schuler, Krysten L. (2024-06-10)This data package constitutes the ecotoxicology evidence used to assess contaminant loads in the breast muscle tissues of hunter-harvested migratory waterfowl collected in the Northeast Atlantic Flyway during the 2021-2022 hunting season. This package contains three folders: (1) Waterfowl Sample (Field) Collection Metadata Folder, (2) Analytical Chemistry Data Folder, and (3) Final Contaminant Data Folder. Content in the Waterfowl Sample (Field) Collection Metadata Folder was produced while obtaining samples of wild waterfowl from hunters across the study area. Content in the Analytical Chemistry Data Folder was originally compiled, curated, and provided by contracted analytical facilities to the Wildlife Health Laboratory at Cornell University (CWHL), after which CWHL did minimal data cleaning (converted formats, removed redundant columns). Specifically, analytical chemistry data on mercury (Hg) originated from raw data provided by the New York State Department of Environmental Conservation Analytical Services Unit at the Hale Creek Field Station (Gloversville, New York); polychlorinated dibenzo-p-dioxins and furans (PCDD/Fs) originated from raw data provided by Pace Analytical (Minneapolis, Minnesota); and polychlorinated biphenyls (PCBs), E1 and E2 organochlorine pesticides (OCPs), and per- and polyfluoroalkyl substances (PFAS) originated from raw data provided by SGS AXYS (Sidney, British Columbia). Content in the Final Contaminant Data Folder was processed according to our specific project decisions regarding concentrations (see below and annotations in code for details) for publication in Dayan et al. (in preparation).Item Data from: Evolutionary genomic analyses of canine E. coli infections identifies a relic capsular locus associated with resistance to multiple classes of antimicrobial drugsCeres, Kristina; Zehr, Jordan D.; Murrell, Chloe; Millet, Jean K.; Sun, Qi; McQueary, Holly C.; Horton, Alanna; Cazer, Casey; Sams, Kelly; Reboul, Guillaume; Andreopoulos, William B.; Mitchell, Patrick K.; Anderson Renee; Franklin-Guild, Rebecca; Cronk, Brittany D.; Stanhope, Bryce J.; Burbick, Claire R.; Wolking, Rebecca; Peak, Laura; Zhang, Yan; McDowall, Rebeccah; Krishnamurthy, Aparna; Slavic, Durda; Sekhon, Prabhjot Kaur; Tyson, Gregory H.; Ceric, Olgica; Stanhope, Michael J.; Goodman, Laura B. (2024)These files contain data that support hypotheses presented in Ceres et. al. Evolutionary genomic analyses of canine E. coli infections identifies a relic capsular locus associated with resistance to multiple classes of antimicrobial drugs. In Ceres et. al. we found: Escherichia coli is the leading cause of death attributed to antimicrobial resistance (AMR) worldwide, and the known AMR mechanisms involve a range of functional proteins. Here we employed a pan-GWAS approach on over 1,000 E. coli isolates from sick dogs collected across the US and Canada and identified a strong statistical association of AMR, involving a range of antibiotics, to a group 1 capsular (CPS) gene cluster. This cluster included genes under relaxed selection pressure, had several loci missing, and pseudogenes for other key loci. It is widespread in E. coli and Klebsiella infections across multiple host species. Earlier studies demonstrated that the octameric CPS polysaccharide export protein Wza can transmit macrolide antibiotics into the E. coli periplasm. We suggest the CPS in question, and its highly divergent Wza, functions as an antibiotic trap, preventing drug penetration. We also highlight the high diversity of lineages circulating in dogs across all regions studied, overlap with human lineages, and regional prevalence of resistance to multiple drug classes.Item North American Wildlife Agency CWD Testing and Ancillary Data (2000 – 2022)Schuler, Krysten L.; Hanley, Brenda J.; Abbott, Rachel C.; Dayan, David B.; Hollingshead, Nicholas A.; Ballard, Jennifer R.; Middaugh, Christopher R.; Cunningham, Mark; Clemons, Bambi; Sayler, Katherine; Kelly, James D.; Killmaster, Charlie H.; Harms, Tyler M.; Ruden, Rachel M.; Caudell, Joe; Westrich, Michelle Benavidez; McCallen, Emily; Casey, Christine; O’Brien, Lindsey M.; Trudeau, Jonathan K.; Straka, Kelly; Stewart, Chad; Carstensen, Michelle; McKinley, William T.; Hynes, Kevin P.; Ableman, Ashley; Miller, Landon A.; Cook, Merril; Myers, Ryan; Shaw, Jonathan; Van de Berg, Sarah; Tonkovich, Michael J.; Grove, Daniel M.; Storm, Daniel J. (2024-05-06)The North American Wildlife Agency CWD Testing and Ancillary Data (2000-2022) dataset (“Dataset”) represents epidemiological, population, ecological, and anthropogenic data related to chronic wasting disease (CWD) surveillance in white-tailed deer (Odocoileus virginianus) in 16 US states (“Administrative Areas”) in North America. The data are summarized at the “Sub-Administrative Area” level. For most state wildlife agencies, counties or equivalent units within Administrative Areas serve as sub-administrative area. While the overall time-period represented by the Dataset spans the years 2000 through 2022, data availability varies by wildlife agency. Administrative Areas and associated wildlife agencies represented in the Dataset include Arkansas (Arkansas Game and Fish Commission), Florida (Florida Fish and Wildlife Conservation Commission), Georgia (Georgia Department of Natural Resources), Indiana (Indiana Department of Natural Resources), Iowa (Iowa Department of Natural Resources), Kentucky (Kentucky Department of Fish and Wildlife Resources), Maryland (Maryland Department of Natural Resources), Michigan (Michigan Department of Natural Resources), Minnesota (Minnesota Department of Natural Resources), Mississippi (Mississippi Department of Wildlife, Fisheries, and Parks), New York (New York State Department of Environmental Conservation), North Carolina (North Carolina Wildlife Resources Commission), Ohio (Ohio Department of Natural Resources), Tennessee (Tennessee Wildlife Resources Agency), Virginia (Virginia Department of Wildlife Resources), and Wisconsin (Wisconsin Department of Natural Resources). This dataset is intended for use in regional models to determine risk factors that can be used to predict locations of CWD incidence in white-tailed deer at the sub-administrative unit level. Version 2 (the current version) of the Dataset corrects errors present in Version 1 of the Dataset (Them et al. 2023). For Version 2, the original data sources were re-examined and re-processed to ensure the highest level of fidelity and accuracy possible. Data processing scripts were revised for improved precision and data quality and are available in a related repository (Hollingshead et al. 2024). All data and scripts were assessed for accuracy and consistency using a thorough QAQC process completed by internal and external collaborators.Item North American Wildlife Agency CWD Testing and Ancillary Data (2000 – 2022) ScriptsHollingshead, Nicholas A.; Dayan, David B. (2024-05-03)These scripts have been used to generate the North American Wildlife Agency CWD Testing and Ancillary Data (2000 – 2022) (Version 2) (“Dataset”), representing epidemiological, population, ecological, and anthropogenic data related to chronic wasting disease (CWD) surveillance in white-tailed deer (Odocoileus virginianus) in 16 US states (“Administrative Areas”) in North America. The data are summarized by “Management Areas,” the geographic unit within the administrative area of a wildlife agency used for wildlife management, typically aligning with county or county-equivalent boundaries. The resulting dataset is intended for use in regional models to determine risk factors that can be used to predict locations of CWD incidence in white-tailed deer at the sub-administrative unit level. Version 2 of the dataset corrects errors present in Version 1 (https://doi.org/10.7298/7txw-2681). For Version 2, the original data sources were re-examined and re-processed to ensure the highest level of fidelity and accuracy possible. All data and scripts were assessed for accuracy and consistency by internal and external collaborators.