Dynamic vs. static social networks in models of parasite transmission: predicting Cryptosporidium spread in wild lemurs

Abstract

Summary1. Social networks provide an established tool to implement heterogeneous contact structures in epidemiological models. Dynamic temporal changes in contact structure and ranging behaviour of wildlife may impact disease dynamics. A consensus has yet to emerge, however, concerning the conditions in which network dynamics impact model outcomes, as compared to static approximations that average contact rates over longer time periods. Furthermore, as many pathogens can be transmitted both environmentally and via close contact, it is important to investigate the relative influence of both transmission routes in real-world populations. 2. Here, we use empirically derived networks from a population of wild primates, Verreaux's sifakas (Propithecus verreauxi), and simulated networks to investigate pathogen spread in dynamic vs. static social networks. 3. First, we constructed a susceptible-exposed-infected-recovered model of Cryptosporidium spread in wild Verreaux's sifakas. We incorporated social and environmental transmission routes and parameterized the model for two different climatic seasons. Second, we used simulated networks and greater variation in epidemiological parameters to investigate the conditions in which dynamic networks produce larger outbreak sizes than static networks. 4. We found that average outbreak size of Cryptosporidium infections in sifakas was larger when the disease was introduced in the dry season than in the wet season, driven by an increase in home range overlap towards the end of the dry season. Regardless of season, dynamic networks always produced larger average outbreak sizes than static networks. Larger outbreaks in dynamic models based on simulated networks occurred especially when the probability of transmission and recovery were low. Variation in tie strength in the dynamic networks also had a major impact on outbreak size, while network modularity had a weaker influence than epidemiological parameters that determine transmission and recovery. 5. Our study adds to emerging evidence that dynamic networks can change predictions of disease dynamics, especially if the disease shows low transmissibility and a long infectious period, and when environmental conditions lead to enhanced between-group contact after an infectious agent has been introduced.

Covariation between the physiological and behavioral components of pathogen transmission: host heterogeneity determines epidemic outcomes

“…Universally, differences between types of covariation were strongest for theoretical pathogens with lower transmission efficiency, which suggests that such heterogeneity may be most important for less infectious, more chronic diseases in wildlife such as bovine tuberculosis (Cosgrove et al 2012). This finding is consistent with studies using empirically informed networks that have found dynamic interactions to be more important at lower transmissibility (Chen et al 2014, Springer et al 2017. Additionally, differences in the time it took to reach maximum prevalence for different types of covariation were most pronounced for simulations with higher variation in contact rate.…”

“…Similarly, incorporating dynamic, empirically-based interactions in livestock networks markedly changed predicted epidemic outcomes; Chen et al (2014) incorporated temporal variability with and without changes in individuals' degree order and observed greater discrepancies in predictions for pathogens with lower values of R 0 . Springer et al (2017) found that incorporating dynamic interactions increased the theoretical transmission of cryptosporidium through wild lemur networks. However, Stehlé et al (2011) suggested that daily aggregated networks were acceptable proxies for realtime dynamic networks for an SEIR model of conference attendees.…”

Social status mediates the fitness costs of infection with canine distemper virus in Serengeti spotted hyenas

“…Social networks are increasingly integrating disease dynamics in their analyses (e.g. Springer, Kappeler, & Nunn, ; Vazquez‐Prokopec et al., ; Volz & Meyers, ) but to our knowledge no network‐based study has ever considered dynamic processes in both the pathogen and the host. The main challenge of social network analyses most likely lies in the difficulty of describing feedback dynamics between host social networks and disease‐related fitness costs in the host (Van Segbroeck, Santos, & Pacheco, ; Volz & Meyers, ).…”

The dynamics of transmission and the dynamics of networks

“…A static network would thus overestimate the potential reach of the outbreak. Overall, Springer, Kappeler & Nunn () found largely overlapping estimates when comparing simulated spreads on dynamic and static representation of the same observation data: networks constructed from a population containing multiple groups of Verreaux's sifakas ( Propithecus verreauxi ). However, there was a general trend for larger outbreak sizes in static representations of the network, in line with expectations.…”

“…Whether and how these factors are accounted for can fundamentally change the predicted impact of a spreading epidemic. Springer, Kappeler & Nunn (2017) investigate the role of different modes of transmission and network dynamics on the predicted size of a disease outbreak across several groups of Verreaux's sifakas, a group-living species of lemur. While some factors, such as seasonality, led to consistent differences in the structure of social networks, using dynamic vs. static representations of networks generated differences in the predicted outbreak size of an emergent disease.…”