Zvonimir Poljak

DVM (Croatia), MSc, PhD (Guelph)

Profile

My research interests include studying the spread of infectious diseases in swine. Swine populations of interest include animals within herds, and herds within areas. To study spread, I use a variety of quantitative methods.

Research Interests

I am a university professor specializing in epidemiology and animal health, with a focus on infectious disease surveillance, emerging pathogens, and the application of data science in veterinary medicine and public health. My research spans multiple species, including swine, other livestock, and companion animals, with a strong emphasis on the epidemiology of respiratory infections, antimicrobial resistance, and health informatics.

A major focus of my work is influenza in animals, particularly its epidemiology, transmission, and factors influencing its spread. I study influenza A viruses in swine, poultry, and companion animals, using data-driven approaches and machine learning to assess host susceptibility and identify high-risk strains. My research on H3Nx and H5Nx influenza viruses provides insights into emerging threats and disease control strategies at the interface of veterinary and public health.

Beyond influenza, my research extends to other emerging and endemic infectious diseases in animal populations. This includes porcine reproductive and respiratory syndrome virus (PRRSV), canine infectious respiratory disease complex (CIRDC), and antimicrobial resistance in pathogens of pets and livestock. I apply machine learning, statistical modeling, and network analysis to study disease transmission patterns, risk factors, and clinical impacts, helping veterinarians and public health professionals make data-informed decisions.

A key aspect of my work is health informatics and digital epidemiology. I am involved in developing interactive disease surveillance dashboards to track trends in infectious diseases and antimicrobial resistance in animal populations. I also contributed to shaping veterinary health informatics competencies, preparing future veterinarians to integrate data science and digital tools into clinical practice and disease management. 

Through my research, I aim to improve disease surveillance, risk assessment, and data-driven decision-making in veterinary medicine, making it a dynamic field for students and professionals interested in epidemiology, data science, and One Health research. Potential graduate students with an interest in quantitative approaches to epidemiology, disease modeling, and health informatics can contact me to discuss potential opportunities for research.

Current Graduate Students

• Barr Hadar - PhD
• Kaushalya Kuruppu - PhD
• Dylan Melmer - PhD
• Famke Alberts - MSc

Teaching

In the past, I contributed to teaching Epidemiology I (POPM*6200), and Geographical Epidemiology (POPM*6950), Currently, I co-teach a graduate course in Swine Health Management (POPM*6700).

Selected Publications

1: Hadar BN, Poljak Z, Bonnett B, Coe J, Stone EA, Bernardo TM. Machine learning predicts selected cat diseases using insurance data amid challenges in interpretability. Am J Vet Res. 2025 Feb 7:1-11. 

2: Melmer DJ, O'Sullivan TL, Greer A, Ojkic D, Friendship R, Poljak Z. Machine learning models provide modest accuracy in predicting clinical impact of porcine reproductive and respiratory syndrome type 2 in Canadian sow herds. Am J Vet Res. 2025 Jan 9:1-12. 

3: Alberts F, Berke O, Rocha L, Keay S, Maboni G, Poljak Z. Predicting host species susceptibility to influenza viruses and coronaviruses using genome data and machine learning: a scoping review. Front Vet Sci. 2024 Sep 25;11:1358028.

4: Alberts F, Berke O, Maboni G, Petukhova T, Poljak Z. Utilizing machine learning and hemagglutinin sequences to identify likely hosts of influenza H3Nx viruses. Prev Vet Med. 2024 Dec;233:106351

5: Sobkowich K, Poljak Z, Weese JS, Plum A, Szlosek D, Bernardo TM. Prevalence and distribution of carbapenem-resistant Enterobacterales in companion animals: A nationwide study in the United States using commercial laboratory data. J Vet Intern Med. 2024 Sep-Oct;38(5):2642-2653. 

6: Henry M, McDonald W, Friendship RM, Greer AL, Poljak Z. Development and validation of a farm- and province-level swine flow simulation model using discrete events and Ontario swine farm and provincial input data. Can J Vet Res. 2024 Jan;88(1):3-11. 

7: Keay S, Poljak Z, Alberts F, O'Connor A, Friendship R, O'Sullivan TL, Sargeant JM. Does Vaccine-Induced Maternally-Derived Immunity Protect Swine Offspring against Influenza a Viruses? A Systematic Review and Meta-Analysis of Challenge Trials from 1990 to May 2021. Animals (Basel). 2023 Oct 3;13(19):3085.

8: Petukhova T, Spinato M, Rossi T, Guerin MT, Kelton D, Nelson-Smikle P, Barham M, Ojkic D, Poljak Z. Development of interactive dashboards for monitoring endemic animal pathogens in Ontario, Canada: Ontario interactive animal pathogen dashboards. J Vet Diagn Invest. 2023 Nov;35(6):727-736. 

9: Sobkowich KE, Weese JS, Poljak Z, Plum A, Szlosek D, Bernardo TM. Epidemiology of companion animal AMR in the United States of America: filling a gap in the one health approach. Front Public Health. 2023 Jun 16;11:1161950.

10: Chadha A, Dara R, Pearl DL, Gillis D, Rosendal T, Poljak Z. Classification of porcine reproductive and respiratory syndrome clinical impact in Ontario sow herds using machine learning approaches. Front Vet Sci. 2023 Jun 7;10:1175569.

11: Tran H, Friendship R, Poljak Z. Classification of group A rotavirus VP7 and VP4 genotypes using random forest. Front Genet. 2023 May 30;14:1029185. 

12: Chadha A, Dara R, Pearl DL, Sharif S, Poljak Z. Predictive analysis for pathogenicity classification of H5Nx avian influenza strains using machine learning techniques. Prev Vet Med. 2023 Jul;216:105924.

13: Hadar BN, Bonnett BN, Poljak Z, Bernardo TM. Morbidity of insured Swedish cats between 2011 and 2016: Comparing disease risk in domestic crosses and purebreds. Vet Rec. 2023 Jun-Jan 17;192(12):e2778.

14: Brown J, Physick-Sheard P, Greer A, Poljak Z. Network analysis of Standardbred horse movements between racetracks in Canada and the United States in 2019: Implications for disease spread and control. Prev Vet Med. 2022 Jul;204:105643. 

15: Petukhova T, Pearl DL, Spinato M, Fairles J, Hazlett M, Poljak Z. The impact of the initial public health response to COVID-19 on swine health surveillance in Ontario. One Health. 2021 Dec;13:100338. 

16: Hadar BN, Lambrecht KJ, Poljak Z, Coe JB, Stone EA, Verbrugghe A, Bernardo TM. Technology-enhanced weight-loss program in multiple-cat households: a randomized controlled trial. J Feline Med Surg. 2022 Aug;24(8):726-738. 

17: Ouyang ZB, Hodgson JL, Robson E, Havas K, Stone E, Poljak Z, Bernardo TM. Day-1 Competencies for Veterinarians Specific to Health Informatics. Front Vet Sci. 2021 Jun 11;8:651238. 

18: Ferreira JB, Poljak Z, Friendship R, Nagy É, Wideman G, Grgić H. Assessment of exposure to influenza A viruses in pigs between weaning and market age. Vet Res. 2021 Apr 21;52(1):60. 

19: Grgić H, Gallant J, Poljak Z. Virological Surveillance of Influenza A Subtypes Isolated in 2014 from Clinical Outbreaks in Canadian Swine. Viruses. 2017 Mar 21;9(3):55. 

20: Arruda AG, Friendship R, Carpenter J, Greer A, Poljak Z. Evaluation of Control Strategies for Porcine Reproductive and Respiratory Syndrome (PRRS) in Swine Breeding Herds Using a Discrete Event Agent-Based Model. PLoS One. 2016 Nov 22;11(11):e0166596. 

21: Arruda AG, Poljak Z, Knowles D, McLean A. Development of a stochastic agent-based model to evaluate surveillance strategies for detection of emergent porcine reproductive and respiratory syndrome strains. BMC Vet Res. 2017 Jun 12;13(1):171.