High-resolution contact networks of free-ranging domestic dogs Canis familiaris and implications for transmission of infection

Abstract

Contact patterns strongly influence the dynamics of disease transmission in both human and non-human animal populations. Domestic dogs Canis familiaris are a social species and are a reservoir for several zoonotic infections, yet few studies have empirically determined contact patterns within dog populations. Using high-resolution proximity logging technology, we characterised the contact networks of free-ranging domestic dogs from two settlements (n = 108 dogs, covering >80% of the population in each settlement) in rural Chad. We used these data to simulate the transmission of an infection comparable to rabies and investigated the effects of including observed contact heterogeneities on epidemic outcomes. We found that dog contact networks displayed considerable heterogeneity, particularly in the duration of contacts and that the network had communities that were highly correlated with household membership. Simulations using observed contact networks had smaller epidemic sizes than those that assumed random mixing, demonstrating the unsuitability of homogenous mixing models in predicting epidemic outcomes. When contact heterogeneities were included in simulations, the network position of the individual initially infected had an important effect on epidemic outcomes. The risk of an epidemic occurring was best predicted by the initially infected individual’s ranked degree, while epidemic size was best predicted by the individual’s ranked eigenvector centrality. For dogs in one settlement, we found that ranked eigenvector centrality was correlated with range size. Our results demonstrate that observed heterogeneities in contacts are important for the prediction of epidemiological outcomes in free-ranging domestic dogs. We show that individuals presenting a higher risk for disease transmission can be identified by their network position and provide evidence that observable traits hold potential for informing targeted disease management strategies.

Unifying spatial and social network analysis in disease ecology

“…Where spatial data are easier to collect than social interactions, verifying that the two correlate may allow the use of spatial data to approximate social contacts; furthermore, social networks and contact events are commonly approximated using parameterised movement data (see below, Box 2 and Section 5). For example, a study of African domestic dog populations used GPS tracking and proximity loggers to demonstrate that individual home range size correlated well with network centrality, which in turn influenced individual propensity to spark simulated rabies epidemics (Wilson‐Aggarwal et al., 2019). Similar logic could apply to any system in which ranging behaviour covaries predictably with sociality; however, strong spatial‐social correlations are not ubiquitous.…”

Measuring social networks in primates: wearable sensors vs. direct observations

“…The number of contacts per day was ∼ 1, 135 on average and ranged from 754 (June continuous distribution spanning all values in between (see Fig. 2), as observed in many 273 different contexts for human and animal groups [14,15,18,20,39,44]. In fact, we report 274 on the same graph the statistics of contact durations measured by wearable sensors between students in a school, reported in [19] and freely accessible: it turns out that the 276 distributions of contact durations of baboons and of humans are indeed very similar.…”

“…Wearable sensors data A subgroup of 13 baboons, consisting only of juveniles and 127 adults (all individuals at least 6 years old) were collared with leather collars. The 128 collars were fitted with wearable proximity sensors (RFID tags) developed by the 129 SocioPatterns collaboration (http://www.sociopatterns.org/), already used in many 130 studies involving humans [14,16,18,20,40,44,51], and recently also animals [39]. In our 131 setting, each sensor was secured in a customized box specially designed with a 3-D 132 printer to contain the sensor and a long-life battery connected to it.…”

Observing the unwatchable: Integrating automated sensing, naturalistic observations and animal social network analysis in the age of big data

“…It is worth noting that the presence of a tag on an animal may change a subject's behaviour (Coughlin & van Heezik, 2014) or its interactions with other individuals (Burley, 1986) (Albery et al, 2020). For instance, high-resolution proximity loggers reveal that two correlated behaviours-an individuals' social network position and ranging behaviour-explain epidemic outcomes in free-ranging domestic dogs (Wilson-Aggarwal et al, 2019). Second, each automated method must be carefully validated using naturalistic observations.…”