A preliminary study to estimate contact rates between free-roaming domestic dogs using novel miniature cameras

Abstract

Information on contacts between individuals within a population is crucial to inform disease control strategies, via parameterisation of disease spread models. In this study we investigated the use of dog-borne video cameras–in conjunction with global positioning systems (GPS) loggers–to both characterise dog-to-dog contacts and to estimate contact rates. We customized miniaturised video cameras, enclosed within 3D-printed plastic cases, and attached these to nylon dog collars. Using two 3400 mAh NCR lithium Li-ion batteries, cameras could record a maximum of 22 hr of continuous video footage. Together with a GPS logger, collars were attached to six free roaming domestic dogs (FRDDs) in two remote Indigenous communities in northern Australia. We recorded a total of 97 hr of video footage, ranging from 4.5 to 22 hr (mean 19.1) per dog, and observed a wide range of social behaviours. The majority (69%) of all observed interactions between community dogs involved direct physical contact. Direct contact behaviours included sniffing, licking, mouthing and play fighting. No contacts appeared to be aggressive, however multiple teeth baring incidents were observed during play fights. We identified a total of 153 contacts–equating to 8 to 147 contacts per dog per 24 hr–from the videos of the five dogs with camera data that could be analysed. These contacts were attributed to 42 unique dogs (range 1 to 19 per video) which could be identified (based on colour patterns and markings). Most dog activity was observed in urban (houses and roads) environments, but contacts were more common in bushland and beach environments. A variety of foraging behaviours were observed, included scavenging through rubbish and rolling on dead animal carcasses. Identified food consumed included chicken, raw bones, animal carcasses, rubbish, grass and cheese. For characterising contacts between FRDD, several benefits of analysing videos compared to GPS fixes alone were identified in this study, including visualisation of the nature of the contact between two dogs; and inclusion of a greater number of dogs in the study (which do not need to be wearing video or GPS collars). Some limitations identified included visualisation of contacts only during daylight hours; the camera lens being obscured on occasion by the dog’s mandible or the dog resting on the camera; an insufficiently wide viewing angle (36°); battery life and robustness of the deployments; high costs of the deployment; and analysis of large volumes of often unsteady video footage. This study demonstrates that dog-borne video cameras, are a feasible technology for estimating and characterising contacts between FRDDs. Modifying camera specifications and developing new analytical methods will improve applicability of this technology for monitoring FRDD populations, providing insights into dog-to-dog contacts and therefore how disease might spread within these populations. © 2017 Bombara et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Full text

RESEARCH ARTICLE
A preliminary study to estimate contact rates
between free-roaming domestic dogs using
novel miniature cameras
Courtenay B. Bombara
1
, Salome Du¨rr
2
, Gabriel E. Machovsky-Capuska
1,3
, Peter
W. Jones
4
, Michael P. Ward
1
*
1Sydney School of Veterinary Science, The University of Sydney, Camden, Australia, 2Veterinary Public
Health Institute, University of Bern, Liebefeld, Switzerland, 3The Charles Perkins Centre and School of Life
and Environmental Sciences, The University of Sydney, Sydney, Australia, 4School of Electrical and
Information Engineering, The University of Sydney, Sydney, Australia
*michael.ward@sydney.edu.au
Abstract
Information on contacts between individuals within a population is crucial to inform disease
control strategies, via parameterisation of disease spread models. In this study we investi-
gated the use of dog-borne video cameras–in conjunction with global positioning systems
(GPS) loggers–to both characterise dog-to-dog contacts and to estimate contact rates. We
customized miniaturised video cameras, enclosed within 3D-printed plastic cases, and
attached these to nylon dog collars. Using two 3400 mAh NCR lithium Li-ion batteries, cam-
eras could record a maximum of 22 hr of continuous video footage. Together with a GPS
logger, collars were attached to six free roaming domestic dogs (FRDDs) in two remote
Indigenous communities in northern Australia. We recorded a total of 97 hr of video footage,
ranging from 4.5 to 22 hr (mean 19.1) per dog, and observed a wide range of social behav-
iours. The majority (69%) of all observed interactions between community dogs involved
direct physical contact. Direct contact behaviours included sniffing, licking, mouthing and
play fighting. No contacts appeared to be aggressive, however multiple teeth baring inci-
dents were observed during play fights. We identified a total of 153 contacts–equating to 8
to 147 contacts per dog per 24 hr–from the videos of the five dogs with camera data that
could be analysed. These contacts were attributed to 42 unique dogs (range 1 to 19 per
video) which could be identified (based on colour patterns and markings). Most dog activity
was observed in urban (houses and roads) environments, but contacts were more common
in bushland and beach environments. A variety of foraging behaviours were observed,
included scavenging through rubbish and rolling on dead animal carcasses. Identified food
consumed included chicken, raw bones, animal carcasses, rubbish, grass and cheese. For
characterising contacts between FRDD, several benefits of analysing videos compared to
GPS fixes alone were identified in this study, including visualisation of the nature of the con-
tact between two dogs; and inclusion of a greater number of dogs in the study (which do not
need to be wearing video or GPS collars). Some limitations identified included visualisation
of contacts only during daylight hours; the camera lens being obscured on occasion by the
dog’s mandible or the dog resting on the camera; an insufficiently wide viewing angle (36˚);
PLOS ONE | https://doi.org/10.1371/journal.pone.0181859 July 27, 2017 1 / 16
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OPEN ACCESS
Citation: Bombara CB, Du¨rr S, Machovsky-
Capuska GE, Jones PW, Ward MP (2017) A
preliminary study to estimate contact rates
between free-roaming domestic dogs using novel
miniature cameras. PLoS ONE 12(7): e0181859.
https://doi.org/10.1371/journal.pone.0181859
Editor: Javier Sanchez, Atlantic Veterinary College,
CANADA
Received: January 9, 2017
Accepted: July 7, 2017
Published: July 27, 2017
Copyright: ©2017 Bombara et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
files.
Funding: This study was funded by the Australian
Department of Agriculture’s Wildlife Exotic Disease
Preparedness Program. SD’s salary was funded by
the Swiss National Science Foundation (grant
number PASMP3 142737). GEM-C was supported
by the Loxton Research Fellowship from the
Faculty of Veterinary Science, The University of
Sydney. Cameras used in this research were

battery life and robustness of the deployments; high costs of the deployment; and analysis
of large volumes of often unsteady video footage. This study demonstrates that dog-borne
video cameras, are a feasible technology for estimating and characterising contacts
between FRDDs. Modifying camera specifications and developing new analytical methods
will improve applicability of this technology for monitoring FRDD populations, providing
insights into dog-to-dog contacts and therefore how disease might spread within these
populations.
Introduction
Estimation of contact rates between individuals is crucial to inform the spread of disease
within populations [1]. Contacts can be categorised as either direct (where physical contact
between two animals occurs) or indirect (where no physical contact between two animals
occurs), both of which can be effective in transmitting disease (depending on the nature of the
disease agent involved). In canines, a range of diseases–such as canine parvovirus and canine
distemper virus–can be spread via fomites [2–3]. For others–such as rabies–direct physical
contact (principally biting) between individuals is required for transmission [4]. Therefore, a
description of the nature of contacts and estimation of contact rates between con- and hetero-
specific individuals are needed to describe the spread of diseases within populations, via para-
meterisation of infectious disease models [1].
In the current study, our focus is on the potential spread of rabies in northern Australia, a
region currently free from canine rabies but which is under threat of an incursion due to the
eastern spread of the disease in Indonesia [5]. Rabies disease spread pathways in Papua New
Guinea have recently been characterised [6] and a rabies disease spread model to inform re-
sponse strategies in northern Australia has been developed [7]. The northern Australian region
is characterised by very low human population densities, mostly in discrete Indigenous com-
munities. Within these communities there are often large dog populations (one dog per five
residents, or greater), which are mostly free-roaming [8–10]. Disease transmission, in particu-
lar a potential rabies incursion, is likely to be spread via free-roaming domestic dogs (FRDD).
For this reason, information of the roaming behaviour of domestic dogs (Canis familiaris) and
the nature of their intra- and inter-specific contacts with wild dogs and dingoes (Canis lupus
dingo) that also inhabit this region, is critical for understanding potential disease spread and
for planning response strategies.
Bio-logging technologies have made significant contributions to understand how animals
utilize their environments and interact among each other [11]. Miniaturize data loggers can
collect and store information from multiple sensors–such as global positioning systems (GPS),
time depth recorders, accelerometers and temperature thermistors [12]. GPS technology is
widely used to observe the roaming behaviour of animals, even over long periods of time [13].
When a substantial proportion of individuals in a population are monitored via GPS and loca-
tions are recorded at a sufficiently high frequency, contact rates within the population can be
estimated [7]. However, the main disadvantage of only employing GPS loggers is the lack of
visual confirmation of the nature of these contacts. Therefore, direct (involving physical inter-
action) versus indirect contacts (where two dogs are within close proximity but no physical
contact occurs) cannot be distinguished. Particularly for contagious diseases driven by direct
contact between susceptible and infectious hosts (such as rabies), knowledge and characterisa-
tion of the type of contact is essential to provide accurate estimates of potential disease spread.
Estimating contact rates between free-roaming domestic dogs using novel miniature cameras
PLOS ONE | https://doi.org/10.1371/journal.pone.0181859 July 27, 2017 2 / 16
funded by Faculty of Veterinary Science DVC
Compact Research fund (The University of
Sydney). The funders had no role in study design,
data collection and analysis, decision to publish, or
preparation of the manuscript.
Competing interests: The authors have declared
that no competing interests exist.

Cameras have previously been identified as useful tools to collect data on the frequency and
characteristics of contacts [14–16]. In a study of deer in Texas, USA [15], deer-borne cameras
were used to collect behavioural data from an animal’s perspective. Deer were identified based
on distinguishing features and contacts were recorded. A more recent study by Lavelle et al.
[16] compared methods of contact estimation using deer-borne detection systems involving a
GPS logger, a proximity logger and a video camera on a sample of white-tailed deer. In this
study it was reported that contact estimated using GPS data could be an underrepresentation
of actual contacts [16]. Recent developments in animal-borne video cameras have provided
partial glimpses of fine-scale, detailed behaviours in different animal species, including interac-
tions with their environment [14,15,17], social interactions [16,18,19], and foraging behav-
iour [20–22]. This has presented new opportunities for developing the optimal device that
would enable researchers to collect their desired data. However, there are also several chal-
lenges in implementing this novel technology; these include battery and data storage capaci-
ties, weight, packaging and cost-effectiveness (that is, cost of the device and amount of labour
required versus the value of the data collected for the specified study goal) [21,23].
Here, we deployed contact identification systems (a miniaturised video camera combined
with a GPS logger) to characterise contacts in FRDD populations in Aboriginal and Torres
Strait Islander communities in northern Australia. In addition, we discussed the potential util-
ity of the information generated for disease control via parameterization of models.
Material and methods
The deployments: Contact identification system
We customized a miniaturised camera as previously reported [21]. In the present study, the
camera (U10 AU USB Flash Drive DRV Camera, DV Taiwan) was enclosed in a 90 x 30 x 20
mm (L x W x H) 3D-printed plastic case and attached to a nylon dog collar specifically secured
to reduce collar movement. To protect the camera against sea water and rain, we used an
empty saline solution bag (Fig 1A). The camera had a sensor resolution of 720 x 480 HD at
30 frames per second and a 36˚ lens angle and a storage capability of a MicroSD 64 GB (for
more details see [21]). To maximize data collection, the unit was powered by two 3400 mAh
Panasonic NCR lithium Li-ion batteries, enabling 20 hr of continuous video recording. A GPS
logger (CatLog1;http://mr-lee.com/catlog.htm) which has been used in previous studies in
these populations [8–10] was also attached to each collar (Fig 1B). The GPS loggers were set
to record locations (latitude and longitude, called GPS fix) every minute. The final contact
Fig 1. Video-camera collar on a community dog (A) in Galiwin’ku, the Northern Territory, Australia, October 2014. The
collar (B) also included a GPS (Global Positioning System; 1) logger in addition to its lens (2) and battery (3).
https://doi.org/10.1371/journal.pone.0181859.g001
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identification system–including the batteries, camera and GPS logger–weighed 313 g, which is
below the 3% threshold (system weight animal weight) beyond which behavioural disrup-
tions are likely to occur in animals [20].
To assess any potential adverse reaction of the dogs carrying the collars, a dummy camera
was constructed and tested on two farm dogs from Kirkham NSW and two farm dogs from
Jindabyne NSW, Australia. Our initial trials revealed no negative effects of the use of collars in
dogs. The study was approved by the Animal Ethics Committee of The University of Sydney (#
N00/7-2013/2/6015).
Study area and study animals
As a pilot study, we deployed these miniaturised cameras and GPS data loggers on six commu-
nity dogs, two in Seisia in the Northern Peninsula Area (10.883˚ S, 142.383˚ E) of Cape York,
Queensland, Australia and four in Galiwin’ku on Elcho Island (12.024˚S, 135.572˚E), Northern
Territory, Australia, in September and October 2014, respectively (Table 1 and Fig 2).
Households participating in this study had been selected opportunistically in an earlier
study [8]. In conjunction with the local animal management worker, researchers drove around
the communities searching for dogs and owners at home who were willing to participate. The
study methods were explained to the owners and following verbal consent the dogs were man-
ually restrained and cameras were attached.
Data analysis
Video footage. Contact rates were analysed (S1 Data) using a dog-to-dog contact defini-
tion of being sighted within one five-minute interval of video footage. No spatial limit was set
to define a contact via video–as soon as another dog was visible this was counted as a contact.
Contacted dogs were identified individually where possible enabling calculation of repeated
contacts between the same individuals. The contacts were classified as ‘direct’ if physical con-
tact between two dogs occurred and ‘indirect’ if another dog was visible in the camera field of
view but no physical contact occurred. The environment (inside house, surrounding bush,
urban environment [houses and other infrastructure visible] and beach; Fig 3) and the time
period during which a dog remained in each particular environment were recorded over the
entire period of the video and for each contact. The video footage was reviewed using the pro-
gram Avidemux 2.6.6 (Multi-platform Video Platform Editor, Boston MA). The videos were
edited to exclude unusable footage–when the view was obstructed or when it was too dark to
observe contacts. The recording of contact data was restricted to daylight hours using the dog-
borne video cameras. To estimate daily contact rates to enable comparisons between dogs, the
number of contacts were extrapolated to a 24 hr period, assuming constant contact rates dur-
ing this period.
Table 1. Description of six dogs fitted with video cameras in a study of interactions and contact rates between free-roaming dogs in Indigenous
communities in northern Australia. All dogs include were classified as “camp dog” breed.
Camera on Camera off
ID Sex Colour Community Age Dog household Date Time Date Time
42 male, neutered black, tan & white Galiwin’ku adult single 21/10/14 16:23 22/10/14 11:30
14 male, neutered black & tan Galiwin’ku adult unknown 22/10/14 15:51 23/10/14 15:16
23 male, entire brindle Galiwin’ku young multiple 21/10/14 14:00 22/10/14 11:39
115 male, neutered black & tan Galiwin’ku young multiple 22/10/14 16:06 23/10/14 15:16
130 male, neutered tan Seisia, NPA young multiple 3/09/14 10:28 4/09/14 10:00
33 male, neutered tan Umagico, NPA adult multiple 3/09/14 12:15 3/09/14 16:15
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Fig 2. Location of two sites in northern Australia where video-camera collars for visualising and estimating dog-to-dog contacts were trialled on
community dogs.
https://doi.org/10.1371/journal.pone.0181859.g002
Fig 3. Habitat use identified from dog ID130, with attached video cameras in Seisia, the Northern Peninsula Area (NPA) of Cape York, Queensland.
The study was conducted during September 2014. Images include: (a) beach (b) surrounding bush land, (c) urban (road) environment.
https://doi.org/10.1371/journal.pone.0181859.g003
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GPS data. GPS fixes were used as a comparative non-visual procedure to calculate contact
rates. GPS fixes that were obviously in error or caused by a dog being transported in a vehicle
were identified based on the assumption that it is implausible for a community dog to run
faster than 20 km/h within a full one-minute period [8]. Thus both GPS fixes at the beginning
and end of a one-minute period in which the calculated speed of movement was >20 km/h
were excluded from the dataset before further analysis. Contacts between dogs using the GPS
loggers can only occur between dogs that both wear a logger. In addition to the six dogs fitted
with contact identification systems, six dogs in Seisia and 24 dogs in Galiwin’ku were collared
with GPS loggers only [24]. To estimate contacts between dogs the GPS data was searched for
concurrent location fixes within the same minute that were less than 20 m apart, based on loca-
tion error values of the GPS loggers and the one minute interval set for recording GPS fixes
[7]. The identification of GPS fixes meeting these conditions was achieved using R (“space-
time” package). To enable a better comparison between the contacts estimated by the video
and GPS methods, GPS contacts within the same five minute interval were then counted as
one contact.
Camera versus GPS contacts comparison. To compare the contact rates estimated by the
two deployment tools (camera versus GPS), videos were searched for contacts recorded with
other dogs wearing a GPS logger only. Because the video cameras used in this study only pro-
duce analysable data during daylight hours, these comparisons were restricted to daylight
hours. Therefore dog-borne cameras provided a conservative estimate of contact rates.
The GPS devices can only gather contact data between two dogs fitted with GPS collars,
however the video camera method is not restricted in this way. To better compare the contact
rates estimated by the two deployment tools (camera versus GPS), we isolated contact data
derived from the video footage to only include dogs fitted with a GPS device (Fig 4G and 4H).
These dogs could be identified based on colouration patterns and visual markings. Contacts
recorded by GPS were restricted to the time period when the camera on the respective dog was
recording. The contact rates derived from the video camera data analysis are likely to be un-
derestimated because a considerable number of contacts by the same dog could occur within
each five minute interval, but were only recorded as a single contact in this study. Therefore,
for a better comparison with the GPS method, we also merged multiple GPS contacts within
the same five-minute interval into a binary outcome.
Results
Video camera data
We recorded a total of 97 hr of video footage from cameras deployed on the six community
dogs. Videos were recorded for a duration of 4.5–22 hr (mean = 19.1). The hours of usable
footage that was analysed ranged from 2.8–10.8 hr (Table 2). Data from one of the six cameras
was excluded from further analysis because the camera was retrieved 4 hr after it was attached
and this dog did not move from its initial resting position under a truck. The other cameras
were retrieved after 4.5–24 hr without damage.
We observed a wide range of social behaviours (Fig 4). The majority (69%) of all observed
interactions between community dogs involved direct physical contact (Table 2). Direct con-
tact behaviours included sniffing, licking, mouthing and play fighting. No contacts appeared
to be aggressive however multiple teeth baring incidents were observed during play fights (Fig
4A, 4B, 4D and 4F).
A total of 153 contacts were recorded for the five dogs, equating to daily estimated contact
rates of 8, 55, 93, 141 and 147 (Table 2). Using different coloration patterns and visual marks
(including those that were fitted with GPS loggers), a total of 42 unique dogs were identified in
Estimating contact rates between free-roaming domestic dogs using novel miniature cameras
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the videos. The number of unique dogs contacted ranged from 1 to 19 (Table 2). For all five
dogs fitted with camera collars, only six contacts were with dogs that could not be identified
on coloration patterns and visual marks due to poor visibility.
Fig 4. Contact data from cameras attached to community dogs from the Northern Peninsula Area of
Cape York, Queensland and Galiwin’ku, East Arnhem Land, the Northern Territory. The study was
conducted during September and October 2014. Observations include (a) direct contact during play fight, (b)
the same dog moments after (a) indicating direct physical contact during play flight, (c) dog defecating, (d) two
dogs play fighting in close contact, (e) female dog urinating [urine indicated by red arrow], (f) direct bite to the
camera during a play fight, (g) and (h) GPS loggers from identified dogs included in the GPS study (i) dog
sniffing the rear of another dog (j) two dogs touching muzzles. Images (a) to (f) are from dog ID130, images
(g) and (h) are from dog ID115. The mandible of the camera-equipped is indicated by the white x where
visible.
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Video footage provided additional information on habitat utilisation. Most dog activity was
observed in urban environments (Table 3), however some dogs were also seen in surrounding
bush areas, inside houses and one dog (ID130) was observed occupying the nearby beach (Fig
3A). Dog-to-dog contacts were observed in all habitats occupied by the community dogs
(Table 3). Contact rates during periods of monitoring were greater in the bush and at the
beach than in an urban environment including inside houses. Interactions between commu-
nity dogs often occurred early in the morning between 5am and 10am, and contacts were low-
est during the middle of the day, between 10am and 3pm (Table 4). One dog (ID23) was
confined (not able to freely roam, restricted by fencing or tethering) with a chain at two differ-
ent times (a total of 2.4 hr) during its 8.7 hr period of monitoring. Few (2/20) contacts were
observed while the dog was confined.
GPS data
The GPS loggers recorded 933 (ID130), 416 (ID14) and 624 (ID42) fixes for three dogs during
the time period of video recording, after having excluded 25, 4 and 0 GPS fixes, respectively,
based on the exclusion criteria of >20 km/h. However, the GPS loggers failed to record fixes
for the remaining three dogs. The total numbers of contacts with other dogs in the same com-
munity estimated from the GPS data during that period were 3, 1 and 16 for dogs ID130, 14
and 42, respectively (Table 5).
Table 2. Summary of the contact data derived from a video camera collar study of five community dogs and extrapolation of contact data to pro-
vide contact estimates over 24 hr in Galiwin’ku, East Arnhem Land, the Northern Territory and the Northern Peninsula Area (NPA) of Queensland,
conducted in September and October 2014.
Dog
ID
Duration of usable
video footage (min)
Contacts Number of unique
c
dogs contacted
Estimated total number of
contacts per 24 hr
Estimated total number of
unique contacts per 24 hr
Total Direct
a
Indirect
b
42 185 1 0 1 1 8 8
14 170 11 1 10 3 93 25
23 520 20 13 7 7 55 19
115 560 55 44 11 12 141 31
130 645 66 47 19 19 147 42
Total 2080 153 105 48 42 89
d
25
d
a
where physical contact between two dogs occurred
b
where dogs were observed in the camera footage from a distance, however no physical contact occurred
c
dogs which could be individually identified (based on colour patterns and markings)
d
mean
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Table 3. Contact (observation time, minutes) behaviour in habitats occupied by community dogs in Galiwin’ku, East Arnhem Land, the Northern
Territory and the Northern Peninsula Area (NPA) of Queensland, conducted in September and October 2014.
Contacts
Dog ID Inside house Urban Surrounding bush Beach Contacts per hr of observation
42 nil
a
1 (185) nil nil 0.3
14 6 (100) 5 (65) 0 (5) nil 3.6 / 4.6 / 0
23 nil 20 (520) nil nil 2.3
115 nil 30 (475) 25 (85) nil 3.8 / 17.6
130 nil 42 (555) 17 (60) 7 (30) 4.5 / 17.0 / 14.0
a
no observation in this environment
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Table 4. Contact behaviour of community dogs at different times of the day in a video camera study conducted in Galiwin’ku, East Arnhem Land,
the Northern Territory and the Northern Peninsula Area (NPA) of Queensland, September and October 2014.
Dog ID Time of day Contacts Duration of usable video footage (min) Contacts per hr observation
42 5am–10am 0 55 0
10am–3pm 0 0 0
3pm–8pm 1 130 0.5
Total 1 185 0.3
14 5am–10am 0 0 0
10am–3pm 0 0 0
3pm–8pm 11 170 3.9
Total 11 170 3.9
23 5am–10am 17 225 4.5
10am–3pm 0 20 0
3pm–8pm 3 275 0.7
Total 20 520 2.3
115 5am–10am 22 240 5.5
10am–3pm 27 185 8.8
3pm–8pm 6 135 2.7
Total 55 560 5.9
130 5am–10am 30 135 13.3
10am–3pm 6 270 1.3
3pm–8pm 30 240 7.5
Total 66 645 6.1
https://doi.org/10.1371/journal.pone.0181859.t004
Table 5. Contacts between paired GPS-collared community dogs in East Arnhem Land, the Northern Territory and the Northern Peninsula Area
(NPA) of Queensland, in September and October 2014. Contacts are calculated when concurrent GPS fixes are within 20 m and 1 min.
GPS Video
Dog 1 Dog 2 Estimated contacts during
day/night time
b
Days in
common
c
Contacts per
day
Estimated
contacts
Days in
common
d
Contacts per
day
Seisia_130 Seisia_11 3 / 0 0.88 3.41 5 0.91 5.50
Seisia_130 Seisia_12
a
– – – 5 0.91 5.50
Seisia_130 Seisia_28 0 / 0 0.56 0 2
e
0.91 2.20
Seisia_130 Seisia_124
a
– – – 17 0.91 18.68
Seisia_130 Seisia_123
a
– – – 2 0.91 2.20
TOTAL 3 / 0 0.72 4.17 31 0.91 34.08
Galiwinku_14 Galiwinku_17 1 / 0 0.48 2.08 0 0.54 0
TOTAL 1 / 0 0.48 2.08 0 0.54 0
Galiwinku_42 Galiwinku_07 1 / 2 0.79 3.80 0 0.60 0
Galiwinku_42 Galiwinku_101 2/ 11 0.79 16.46 0 0.60 0
TOTAL 16 0.79 20.25 0 0.60 0
a
GPS logger failed
b
day time was defined between sunrise (06:03) and sunset (18:24) at 15
th
of October 2014 in Galiwin’ku, according to the Geoscience Australia website
(http://www.ga.gov.au/bin/geodesy/run/sunrisenset)
c
number of days during which both dog 1 and dog 2 were wearing a GPS collar simultaneously
d
number of days during which the camera on dog 1 was recording and the GPS collar was attached to dog 2
e
one of the two contacts occurred outside the recording time of the GPS unit
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Camera versus GPS contacts comparison
Footage from two (ID115 and ID130) of the five dogs equipped with video cameras showed
contact with dogs equipped with GPS loggers. Dog ID115 contacted ID120 on two occasions
at (24 hr time) 18:23 and 18:26. Dog ID130 was observed to come into contact with five other
GPS collared dogs (ID11, 12, 28, 124 and 123) on 31 occasions during the video camera moni-
toring period (Table 5). A contact rate of 34 per 24 hr was estimated using the video camera
between GPS collared dogs. GPS loggers on dog ID12, 123 and 124 malfunctioned and there-
fore did not record any data. While two video contacts between dog ID130 and dog ID28 were
observed, only one of these contacts occurred while the GPS device was recording data; based
on the GPS data, a contact between those two dogs was not recorded. Five and three contacts
were recorded between dog ID130 and dog ID11 using the video camera footage and the GPS
data, respectively. These contacts occurred during similar time periods (16:56–17:05 for the
GPS contacts and 16:58, 17:03, 17:09, 17:14 and 17:19 for the video observed contacts). Dog ID
14 and 42 recorded contacts via GPS logger only, of which one (06:37) and three (17:24, 17:56,
18:16) occurred during day time (before sunset) and 13 after sunset (20:54–04:21), when the
video camera was not able to record visible footage (Table 5).
Discussion
This study demonstrates the value of video camera collars for descriptive characterisation of
contacts and foraging behaviours of FRDDs in remote Indigenous communities. It also dem-
onstrates that both video camera collars and GPS loggers can be used to estimate contact rates
between FRDDs. Disease transmission is a complex process and comparisons of technologies
to monitor contact data can provide a more complete understanding of disease dynamics [16].
Moreover, transmission parameters informed by contact rates are a crucial step in modelling
disease spread and devising appropriate control strategies [5].
The free-roaming nature of dogs has consistently been recognised as a risk factor for disease
transmission [25,26]. However, the mechanisms of disease transmission are difficult to visual-
ise in such populations. For characterising contacts between FRDDs, several benefits of analys-
ing videos compared to GPS fixes alone were identified in this study. Most importantly, videos
provide information on the nature of the contact between two dogs, a crucial determinant of
disease transmission. Some diseases may require direct, physical contact (e.g. bites for rabies
or close contacts for canine distemper, see Fig 4B and 4J as an example) for transmission. For
other diseases larger distances between the individuals might be sufficient, or transmission
might be via indirect contacts through contaminated environments or fomites (e.g. canine par-
vovirus that can be spread via contact with canine faeces, see Fig 4C and 4I as an example).
Dogs were observed contacting faeces and urine from other dogs (see Fig 4C and 4E as an
example). Also, not only information on dogs enrolled in the study was collected, but dogs
with video cameras can be used (as “sentinels”) to record contacts between other dogs as well
(see Fig 4D as an example).
Rabies infection influences animal behavior, presenting either as the furious or paralytic
forms, and will thus change the characteristics of observed contacts. Although a lack of aggres-
sive interactions were identified in the present, video camera collars would be particularly use-
ful for documenting effective contacts for rabies transmission, because dog bites and teeth
baring instances were easily observed on the video footage (and these contacts were often close
up and front on).
A simplifying assumption often made in disease spread models is homogenous mixing of
individuals within a population, with constant contact rates over time and within different
environments. However, such an assumption is seldom realistic. Aggregations of dogs are
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expected to promote disease spread, depending on when and where such non-homogenous
mixing occurs. Majumder et al. [27] found that social aggregates of dogs tended to be most
common during foraging forays, away from their households in a population of FRDDs in
urban India. This finding was consistent with our study in which contacts were fewer in houses
and surrounding urban environments than further away from the dogs’ homes, such as the
surrounding bush and the beach. Also, it was evident from the video footage in the present
study that confining dogs by tethering considerably restricts the number of contacts: dog ID23
had 2.8-times more contacts when it was free-roaming as when it was confined. Restricting the
movement of FRDDs is likely a useful control point to prevent disease transmission [5,7] and
is an action that community members can implement in the case of a disease outbreak [28].
Video footage also provides qualitative information regarding the circumstances in which
contacts might occur. A number of feeding and foraging behaviours were observed in video
camera footage, some of which are shown in Fig 5. Foraging behaviours observed included
scavenging through rubbish and rolling on dead animal carcasses. Identified food consumed
included chicken, raw bones, animal carcasses, rubbish, grass and cheese. Many of the foraging
behaviours observed have implications for disease transmission. For example, one dog (ID115)
was observed eating a nappy (diaper). This is consistent with the findings of Brown (2006) who
reported that 35% of dog faecal samples from Indigenous communities contained nappy rem-
nants. Coprophagy of human faeces and scavenging may facilitate the lifecycles of potentially
zoonotic pathogens [29–31] and may contribute to physical transmission of non-zoonotic
human disease e.g. if a dog licks a child after eating a nappy [32].
Contact with dog faeces was also observed (ID130), which is a major transmission point for
certain diseases such as canine parvovirus [2]. Dog faeces within communities also increase
the risk of zoonotic transmission of Echinococcus granulosus to humans [33]. In addition,
Fig 5. Foraging data captured with video camera collar on community dogs, in Galiwin’ku, the
Northern Territory. The study was conducted during September and October 2014. Images include: (a)
dead animal, canine tooth indicated by red arrow, (b) nappy, (c) dog eating raw bones indicated by red arrow,
(d) rubbish. The mandible of the camera-equipped dog in each image is indicated by the white x, images (a) to
(c) are from dog ID115, image (d) is from dog ID130.
https://doi.org/10.1371/journal.pone.0181859.g005
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consumption of raw meat and dead animal carcasses were observed in the current study; both
of these behaviours have been recognized as risk factors for E.granulosus infection in dogs
[33–34]. Furthermore, dogs were observed to spend a considerable amount of time in the bush
where–based on field observations–contact with ticks is likely (Fig 6). This has been recognised
as a major risk factor for canine vector-borne diseases identified in Indigenous community
dogs [25,35]. Four of the five dogs fitted with cameras were observed to be fed outside the
house by a human on one or more occasions. Such information about feeding, foraging behav-
iour and nutrition might also be useful when designing disease control programs, especially if
these involve restricting the movement of FRDDs.
Cameras have the potential to validate accuracy of GPS data and vice versa, since the posi-
tion (location and time) of dogs wearing GPS collars can be identified in the recorded videos
(see Fig 4G and 4H as examples). The two different methods used in this study to investigate
contact rates–the video camera collars and GPS loggers–differ in many aspects, which also
caused differences in the estimated contact rates. For dog ID130, 4.17 contacts per day were
recorded using the GPS loggers, whereas the contacts between the same dogs (ID11 and ID28)
using the video technique resulted in 7.7 contacts per day (7 contacts during a 0.91 day
period). For other dogs (ID14 and ID42), contacts were only recorded by the GPS loggers and
Fig 6. Tick infestation on the pinna of a community dog in Umagico, the Northern Peninsula Area
(NPA) of Cape York, Queensland. September 2014. Photo credit: C. Bombara.
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were not seen in the video. There are many reasons for these differences. First, contacts were
defined using different spatiotemporal thresholds for each of the technologies. This highlights
the influence of user-specified definitions on contact estimations. In the case of analysis of the
GPS data, the calculated contact rates were particularly sensitive to the GPS spatiotemporal
thresholds used to define a contact (within 20 m within the same minute). In contrast, when
analysing video data a contact was defined as sighting another dog (without a specific spatial
threshold being set) within one five-minute interval. The contact rates are likely to be underes-
timated because a considerable number of contacts by the same dog could occur within each
five-minute interval. Therefore, we also defined several contacts recorded by the GPS within
the same 5 minutes period as one single contact to better compare the two methods.
Both devices were unable to record contacts at some times. GPS-based contacts are esti-
mated only between dogs with fitted GPS loggers, whereas the cameras record contacts with all
dogs within the lens field of view. The video camera has a limited field of view facing forward,
whereas contacts are estimated within a predefined radius of each dog fitted with a GPS logger.
For example, for video cameras no contacts could be recorded when the camera lens was
obscured (including very close contacts, when the dog was laying on the lens or obstruction by
the dog’s mandible), and for GPS loggers fixes could not be recorded when satellite interfer-
ence occurred. The restriction of video camera collars to daylight hours further limited the
time frame in which contacts were recorded for video cameras. For example, the daily video
camera contacts for dog ID130 were estimated from a small proportion of daily activity (10.8
hr out of the total recorded video time of 22 hr). We extrapolated the number of contacts from
a small amount of video footage to estimate daily contact rates, enabling comparisons between
dogs. We acknowledge that these rates might vary considerably from actual contacts because
we were unable to observe activity of the dogs during the night time periods. Our preliminary
findings indicate that, beside the high heterogeneity between individuals, more contacts occur
in the evenings and mornings than in the middle of the day. Because we were unable to use
video footage captured during night time periods, we might have underestimated contacts via
this method. However, this provides a baseline estimate as a comparison in future studies.
Improvement in the video camera technique would therefore include an infrared camera (but
requiring additional battery power) and a wide-angle lens.
Regardless of the technology used, inaccuracy in contact estimates needs to be considered
when using such information to inform disease spread models. A sufficient number of FRDDs
needs to be monitored in a population on more than one occasion to generate robust contact
rate estimates. Nevertheless, we demonstrated that the two devices provided useful contact
data and complementary information, e.g. cameras provided qualitative (characteristics of
contacts) and quantitative contact data however GPS loggers are a more feasible means of
gathering quantitative contact data for a larger population of dogs in the field due to greater
cost-effectiveness. Qualitative, visual representations of dog behaviour are invaluable when
assessing effective contacts between dogs.
Additional challenges for the video camera devices include battery life, robustness and the
laborious analysis of the video footages. Possibilities to increase battery life include a lower
power processing chip or motion triggering video cameras, as used by Lavelle et al. [16]. How-
ever, at our study sites with high frequencies of contacts and movements, motion triggered
videos would be expected to activate often (more than in wildlife studies) and thus might not
provide a solution. Robustness is crucial: damage can be caused by a range of factors such as
water (in rivers and sea or heavy rain during the monsoon season), fights between dogs or
unwarranted handling of the collar by people. Although no substantial problems were ob-
served in our study and no cameras were damaged, increased robustness is desirable bearing
in mind the weight of the camera collar and practical limitations for attachment. Finally, the
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extraction of valuable data requires the examination of a large amount of extremely unsteady
video footage. To reduce unsteady footage a camera stabiliser (e.g. Steadicam) could be incor-
porated) [36]. Development of semi-automated methods using machine-learning techniques
will accelerate the analysis of footage increasing the practicality and viability of large-scale use
of dog-borne video cameras in epidemiological studies.
Conclusions
This study demonstrates that dog-borne video cameras are a valuable technology for charac-
terising contacts between FRDDs. Our study demonstrates that there are considerable varia-
tions in contacts between dogs and at different times of the day, therefore extrapolation of
contact data to a wider population of dogs and to a longer time period should be done with
care. Modifying camera specifications and developing methods for the analysis of large vol-
umes of often unsteady footage remain challenges to be overcome before such systems can be
regularly deployed in FRDD populations to monitor dog-to-dog contacts and before they
could be considered as an easily applicable tool to inform epidemic models of disease transmis-
sion. However, dog-borne video cameras provide invaluable evidence of dog behaviour in the
field that might influence disease transmission.
Supporting information
S1 Data. GPS data collected and included in analysis of contact rates for two in Seisia in
the Northern Peninsula Area (10.883˚ S, 142.383˚ E) of Cape York, Queensland, Australia
and four in Galiwin’ku on Elcho Island (12.024˚S, 135.572˚E), Northern Territory, Austra-
lia, in September and October 2014, respectively.
(ZIP)
Acknowledgments
We thank Frank Mau, George Mara and the Northern Peninsula Area Regional Council, and Vir-
ginia Barratj and Julie Wulkurrngu and the Community Advisory Board of Galiwin’ku for facili-
tating the study, and the community members that participated in this study. We also thank
Emma Kennedy and Sacha Woodburn (East Arnhem Shire Regional Council), Bonny Cumming
(Animal Management in Rural and Remote Indigenous Communities) and Peter Fleming (New
South Wales Department of Primary Industries) for assistance in conducting this study.
Author Contributions
Conceptualization: Salome Du¨rr, Michael P. Ward.
Data curation: Courtenay B. Bombara, Gabriel E. Machovsky-Capuska.
Formal analysis: Courtenay B. Bombara, Salome Du¨rr, Gabriel E. Machovsky-Capuska.
Funding acquisition: Michael P. Ward.
Investigation: Courtenay B. Bombara, Salome Du¨rr, Michael P. Ward.
Methodology: Courtenay B. Bombara, Salome Du¨rr, Gabriel E. Machovsky-Capuska, Peter
W. Jones, Michael P. Ward.
Project administration: Michael P. Ward.
Resources: Courtenay B. Bombara, Salome Du¨rr, Gabriel E. Machovsky-Capuska, Peter W.
Jones, Michael P. Ward.
Estimating contact rates between free-roaming domestic dogs using novel miniature cameras
PLOS ONE | https://doi.org/10.1371/journal.pone.0181859 July 27, 2017 14 / 16

Software: Courtenay B. Bombara, Salome Du¨rr, Gabriel E. Machovsky-Capuska, Peter W.
Jones.
Supervision: Salome Du¨rr, Michael P. Ward.
Writing – original draft: Courtenay B. Bombara, Salome Du¨rr, Michael P. Ward.
Writing – review & editing: Courtenay B. Bombara, Salome Du¨rr, Gabriel E. Machovsky-
Capuska, Peter W. Jones, Michael P. Ward.
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