Estimation of free-roaming dog populations using Google Street View: A Validation Study

Abstract

Free-roaming dogs play a central role in carrying zoonotic pathogens such as rabies virus, Echinococcus granulosus, and Leishmania spp. The control and elimination of these pathogens require quantitative knowledge of dog populations. Thus, estimating the dog population is fundamental for planning, implementing, and evaluating public health programs. However, dog population estimation is time-consuming, requires many field personnel, may be inaccurate and unreliable, and is not without danger. Our objective was to validate a remote methodology for estimating the population of free-roaming dogs using Google Street View (GSV). Our target populations were free-roaming dogs from Arequipa, Peru, a rabies-affected area. Adopting a citizen science approach, and using social media, we recruited online citizen scientists from Arequipa and other regions and trained them to use GSV to identify and count free-roaming dogs in 26 urban and periurban communities. We used correlation metrics and negative binomial models to compare the counts of dogs identified in the GSV imagery with accurate counts of free-roaming owned dogs estimated via door-to-door surveys. In total, citizen scientists detected 868 dogs using GSV and using door-to-door surveys we estimated 909 free-roaming dogs across those 26 communities (Pearsons coefficient was r=0.73, p < 0.001). Our model predicted that for each free-roaming dog detected with GSV in urban areas, there were 1.03 owned dogs with free access to the street (p < 0.001). The type of community, urban versus periurban, did not have an important effect on the model, but fitting the models in periurban communities was difficult because of the sparsity of high-resolution GSV images in those areas. Using GSV imagery for estimating dog populations is a promising tool in urban areas. Citizen scientists can help to generate information for disease control programs in places with insufficient resources.