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Close contacts between individuals provide opportunities for the transmission of diseases, including COVID-19. While individuals take part in many different types of interactions, including those with classmates, co-workers and household members, it is the conglomeration of all of these interactions that produces the complex social contact network interconnecting individuals across the population. Thus, while an individual might decide their own risk tolerance in response to a threat of infection, the consequences of such decisions are rarely so confined, propagating far beyond any one person. We assess the effect of different population-level risk-tolerance regimes, population structure in the form of age and household-size distributions, and different interaction types on epidemic spread in plausible human contact networks to gain insight into how contact network structure affects pathogen spread through a population. In particular, we find that behavioural changes by vulnerable individuals in isolation are insufficient to reduce those individuals’ infection risk and that population structure can have varied and counteracting effects on epidemic outcomes. The relative impact of each interaction type was contingent on assumptions underlying contact network construction, stressing the importance of empirical validation. Taken together, these results promote a nuanced understanding of disease spread on contact networks, with implications for public health strategies. 

Close contacts between individuals provide opportunities for the transmission of diseases, including COVID-19. While individuals take part in many different types of interactions, including those with classmates, co-workers and household members, it is the conglomeration of all of these interactions that produces the complex social contact network interconnecting individuals across the population. Thus, while an individual might decide their own risk tolerance in response to a threat of infection, the consequences of such decisions are rarely so confined, propagating far beyond any one person. We assess the effect of different population-level risk-tolerance regimes, population structure in the form of age and household-size distributions, and different interaction types on epidemic spread in plausible human contact networks to gain insight into how contact network structure affects pathogen spread through a population. In particular, we find that behavioural changes by vulnerable individuals in isolation are insufficient to reduce those individuals’ infection risk and that population structure can have varied and counteracting effects on epidemic outcomes. The relative impact of each interaction type was contingent on assumptions underlying contact network construction, stressing the importance of empirical validation. Taken together, these results promote a nuanced understanding of disease spread on contact networks, with implications for public health strategies. 

Abstract Understanding how the movement of individuals affects disease dynamics is critical to accurately predicting and responding to the spread of disease in an increasingly interconnected world. In particular, it is not yet known how movement between patches affects local disease dynamics (e.g., whether pathogen prevalence remains steady or oscillates through time). Considering a set of small, archetypal metapopulations, we find three surprisingly simple patterns emerge in local disease dynamics following the introduction of movement between patches: (1) movement between identical patches with cyclical pathogen prevalence dampens oscillations in the destination while increasing synchrony between patches; (2) when patches differ from one another in the absence of movement, adding movement allows dynamics to propagate between patches, alternatively stabilizing or destabilizing dynamics in the destination based on the dynamics at the origin; and (3) it is easier for movement to induce cyclical dynamics than to induce a steady-state. Considering these archetypal networks (and the patterns they exemplify) as building blocks of larger, more realistically complex metapopulations provides an avenue for novel insights into the role of host movement on disease dynamics. Moreover, this work demonstrates a framework for future predictive modelling of disease spread in real populations. 

Pathogen management strategies in wildlife are typically accompanied by an array of uncertainties such as the efficacy of vaccines or potential unintended consequences of interventions. In the context of such uncertainties, models of disease transmission can provide critical insight for optimizing pathogen management, especially for species of conservation concern. The endangered Florida panther experienced an outbreak of feline leukaemia virus (FeLV) in 2002–2004, and continues to be affected by this deadly virus. Ongoing management efforts aim to mitigate the effects of FeLV on panthers, but with limited information about which strategies may be most effective and efficient. We used a simulation-based approach to determine optimal FeLV management strategies in panthers. We simulated the use of proactive FeLV management strategies (i.e. proactive vaccination) and several reactive strategies, including reactive vaccination and test-and-removal. Vaccination strategies accounted for imperfect vaccine-induced immunity, specifically partial immunity in which all vaccinates achieve partial pathogen protection. We compared the effectiveness of these different strategies in mitigating the number of FeLV mortalities and the duration of outbreaks. Results showed that inadequate proactive vaccination can paradoxically increase the number of disease-induced mortalities in FeLV outbreaks. These effects were most likely due to imperfect vaccine immunity causing vaccinates to serve as a semi-susceptible population, thereby allowing outbreaks to persist in circumstances otherwise conducive to fadeout. Combinations of proactive vaccination with reactive test-and-removal or vaccination, however, had a synergistic effect in reducing the impacts of FeLV outbreaks, highlighting the importance of using mixed strategies in pathogen management. Synthesis and applications. Management-informed disease simulations are an important tool for identifying unexpected negative consequences and synergies among pathogen management strategies. In particular, we find that imperfect vaccine-induced immunity necessitates further consideration to avoid unintentionally worsening epidemics in some conditions. However, mixing proactive and reactive interventions can improve pathogen control while mitigating uncertainties associated with imperfect interventions. 

ABSTRACT Wildlife can be exposed to antimicrobial-resistant bacteria (ARB) via multiple pathways. Spatial overlap with domestic animals is a prominent exposure pathway. However, most studies of wildlife-domestic animal interfaces have focused on livestock and little is known about the wildlife-companion animal interface. Here, we investigated the prevalence and phylogenetic relatedness of extended-spectrum cephalosporin-resistant (ESC-R) Escherichia coli from raccoons ( Procyon lotor ) and domestic dogs ( Canis lupus familiaris ) in the metropolitan area of Chicago, IL, USA. To assess the potential importance of spatial overlap with dogs, we explored whether raccoons sampled at public parks (i.e., parks where people and dogs could enter) differed in prevalence and phylogenetic relatedness of ESC-R E. coli to raccoons sampled at private parks (i.e., parks where people and dogs could not enter). Raccoons had a significantly higher prevalence of ESC-R E. coli (56.9%) than dogs (16.5%). However, the richness of ESC-R E. coli did not vary by host species. Further, core single-nucleotide polymorphism (SNP)-based phylogenetic analyses revealed that isolates did not cluster by host species, and in some cases displayed a high degree of similarity (i.e., differed by less than 20 core SNPs). Spatial overlap analyses revealed that ESC-R E. coli were more likely to be isolated from raccoons at public parks than raccoons at private parks, but only for parks located in suburban areas of Chicago, not urban areas. That said, ESC-R E. coli isolated from raccoons did not genetically cluster by park of origin. Our findings suggest that domestic dogs and urban/suburban raccoons can have a diverse range of ARB, some of which display a high degree of genetic relatedness (i.e., differ by less than 20 core SNPs). Given the differences in prevalence, domestic dogs are unlikely to be an important source of exposure for mesocarnivores in urbanized areas. IMPORTANCE Antimicrobial-resistant bacteria (ARB) have been detected in numerous wildlife species across the globe, which may have important implications for human and animal health. Wildlife can be exposed to ARB via numerous pathways, including via spatial overlap with domestic animals. However, the interface with domestic animals has mostly been explored for livestock and little is known about the interface between wild animals and companion animals. Our work suggests that urban and suburban wildlife can have similar ARB to local domestic dogs, but local dogs are unlikely to be a direct source of exposure for urban-adapted wildlife. This finding is important because it underscores the need to incorporate wildlife into antimicrobial resistance surveillance efforts, and to investigate whether certain urban wildlife species could act as additional epidemiological pathways of exposure for companion animals, and indirectly for humans. 

Abstract An animal's social behaviour both influences and changes in response to its parasites. Here we consider these bidirectional links between host social behaviours and parasite infection, both those that occur from ecological vs evolutionary processes. First, we review how social behaviours of individuals and groups influence ecological patterns of parasite transmission. We then discuss how parasite infection, in turn, can alter host social interactions by changing the behaviour of both infected and uninfected individuals. Together, these ecological feedbacks between social behaviour and parasite infection can result in important epidemiological consequences. Next, we consider the ways in which host social behaviours evolve in response to parasites, highlighting constraints that arise from the need for hosts to maintain benefits of sociality while minimizing fitness costs of parasites. Finally, we consider how host social behaviours shape the population genetic structure of parasites and the evolution of key parasite traits, such as virulence. Overall, these bidirectional relationships between host social behaviours and parasites are an important yet often underappreciated component of population-level disease dynamics and host–parasite coevolution. 

Human behavior (movement, social contacts) plays a central role in the spread of pathogens like SARS-CoV-2. The rapid spread of SARS-CoV-2 was driven by global human movement, and initial lockdown measures aimed to localize movement and contact in order to slow spread. Thus, movement and contact patterns need to be explicitly considered when making reopening decisions, especially regarding return to work. Here, as a case study, we consider the initial stages of resuming research at a large research university, using approaches from movement ecology and contact network epidemiology. First, we develop a dynamical pathogen model describing movement between home and work; we show that limiting social contact, via reduced people or reduced time in the workplace are fairly equivalent strategies to slow pathogen spread. Second, we develop a model based on spatial contact patterns within a specific office and lab building on campus; we show that restricting on-campus activities to labs (rather than labs and offices) could dramatically alter (modularize) contact network structure and thus, potentially reduce pathogen spread by providing a workplace mechanism to reduce contact. Here we argue that explicitly accounting for human movement and contact behavior in the workplace can provide additional strategies to slow pathogen spread that can be used in conjunction with ongoing public health efforts. 

Host heterogeneity in pathogen transmission is widespread and presents a major hurdle to predicting and minimizing disease outbreaks. Using Drosophila melanogaster infected with Drosophila C virus as a model system, we integrated experimental measurements of social aggregation, virus shedding, and disease-induced mortality from different genetic lines and sexes into a disease modelling framework. The experimentally measured host heterogeneity produced substantial differences in simulated disease outbreaks, providing evidence for genetic and sex-specific effects on disease dynamics at a population level. While this was true for homogeneous populations of single sex/genetic line, the genetic background or sex of the index case did not alter outbreak dynamics in simulated, heterogeneous populations. Finally, to explore the relative effects of social aggregation, viral shedding and mortality, we compared simulations where we allowed these traits to vary, as measured experimentally, to simulations where we constrained variation in these traits to the population mean. In this context, variation in infectiousness, followed by social aggregation, was the most influential component of transmission. Overall, we show that host heterogeneity in three host traits dramatically affects population-level transmission, but the relative impact of this variation depends on both the susceptible population diversity and the distribution of population-level variation.