Evaluation of shelter dog activity levels before and during COVID-19 using automated analysis

Sarah Elizabeth Byosiere, Marcelo Feighelstein, Kristiina Wilson, Jennifer Abrams, Guy Elad, Nareed Farhat, Dirk van der Linden, Dmitrii Kaplun, Aleksandr Sinitca, Anna Zamansky

Research output: Contribution to journalArticlepeer-review

Abstract

Animal shelters have been found to represent stressful environments for pet dogs, both affecting behavior and influencing welfare. The current COVID-19 pandemic has brought to light new uncertainties in animal sheltering practices which may affect shelter dog behavior in unexpected ways. To evaluate this, we analyzed changes in dog activity levels before COVID-19 and during COVID-19 using an automated video analysis within a large, open-admission animal shelter in New York City, USA. Shelter dog activity was analyzed during two two-week long time periods: (i) just before COVID-19 safety measures were put in place (Feb 26-Mar 17, 2020) and (ii) during the COVID-19 quarantine (July 10–23, 2020). During these two periods, video clips of 15.3 second, on average, were taken of participating kennels every hour from approximately 8 am to 8 pm. Using a two-step filtering approach, a matched sample (based on the number of days of observation) of 34 dogs was defined, consisting of 17 dogs in each group (N1/N2 = 17). An automated video analysis of active/non-active behaviors was conducted and compared to manual coding of activity. The automated analysis validated by comparison to manual coding reaching above 79% accuracy. Significant differences in the patterns of shelter dog activity were observed: less activity was observed in the afternoons before COVID-19 restrictions, while during COVID-19, activity remained at a constant average. Together, these findings suggest that 1) COVID-19 lockdown altered shelter dog in-kennel activity, likely due to changes in the shelter environment and 2) automated analysis can be used as a hands-off tool to monitor activity. While this method of analysis presents immense opportunity for future research, we discuss the limitations of automated analysis and guidelines in the context of shelter dogs that can increase accuracy of detection, as well as reflect on policy changes that might be helpful in mediating canine stress in changing shelter environments.

Original languageEnglish
Article number105614
JournalApplied Animal Behaviour Science
Volume250
DOIs
StatePublished - May 2022

Bibliographical note

Funding Information:
The research was partially supported by the grant from the Ministry of Science and Technology of Israel and RFBR according to the research project no. 19-57-06007 and the Animal Behavior and Conservation MA Program’s Thesis Research Grants . The authors would specifically like to thank Animal Care Centers of NYC for their willingness to be involved in this project. Finally, this work would not have been possible without the amazing team of manual video coders who contributed to this project. Thus, the authors wish to thank Sasha Montero, Sasha Prasad, Aiman Haider, Bryan Perez, Lorraine DiSano, Caitlyn Thai, Rachel Rodin, and Anne Castiglioni for their efforts.

Funding Information:
The research was partially supported by the grant from the Ministry of Science and Technology of Israel and RFBR according to the research project no. 19-57-06007 and the Animal Behavior and Conservation MA Program's Thesis Research Grants. The authors would specifically like to thank Animal Care Centers of NYC for their willingness to be involved in this project. Finally, this work would not have been possible without the amazing team of manual video coders who contributed to this project. Thus, the authors wish to thank Sasha Montero, Sasha Prasad, Aiman Haider, Bryan Perez, Lorraine DiSano, Caitlyn Thai, Rachel Rodin, and Anne Castiglioni for their efforts.

Publisher Copyright:
© 2022 Elsevier B.V.

Keywords

  • Applied behavior
  • COVID-19
  • Computer vision
  • Dog behavior
  • Machine learning
  • Shelter research

ASJC Scopus subject areas

  • Food Animals
  • Animal Science and Zoology

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