Epidemiological effects of group size variation in social species

Primate malarias as a model for cross-species parasite transmission

Parasites regularly switch into new host species, representing a disease burden and conservation risk to the hosts. The distribution of these parasites also gives insight into characteristics of ecological networks and genetic mechanisms of host-parasite interactions. Some parasites are shared across many species, whereas others tend to be restricted to hosts from a single species. Understanding the mechanisms producing this distribution of host specificity can enable more effective interventions and potentially identify genetic targets for vaccines or therapies. As ecological connections between human and local animal populations increase, the risk to human and wildlife health from novel parasites also increases. Which of these parasites will fizzle out and which have the potential to become widespread in humans? We consider the case of primate malarias, caused by Plasmodium parasites, to investigate the interacting ecological and evolutionary mechanisms that put human and nonhuman primates at risk for infection. Plasmodium host switching from nonhuman primates to humans led to ancient introductions of the most common malaria-causing agents in humans today, and new parasite switching is a growing threat, especially in Asia and South America. Based on a wild host-Plasmodium occurrence database, we highlight geographic areas of concern and potential areas to target further sampling. We also discuss methodological developments that will facilitate clinical and field-based interventions to improve human and wildlife health based on this eco-evolutionary perspective.

Population structure, intergroup interaction, and human contact govern infectious disease impacts in mountain gorilla populations

Infectious zoonotic diseases are a threat to wildlife conservation and global health. They are especially a concern for wild apes, which are vulnerable to many human infectious diseases. As ecotourism, deforestation, and great ape field research increase, the threat of human-sourced infections to wild populations becomes more substantial and could result in devastating population declines. The endangered mountain gorillas (Gorilla beringei beringei) of the Virunga Massif in east-central Africa suffer periodic disease outbreaks and are exposed to infections from human-sourced pathogens. It is important to understand the possible risks of disease introduction and spread in this population and how human contact may facilitate disease transmission. Here we present and evaluate an individual-based, stochastic, discrete-time disease transmission model to predict epidemic outcomes and better understand health risks to the Virunga mountain gorilla population. To model disease transmission we have derived estimates for gorilla contact, interaction, and migration rates. The model shows that the social structure of gorilla populations plays a profound role in governing disease impacts with subdivided populations experiencing less than 25% of the outbreak levels of a single homogeneous population. It predicts that gorilla group dispersal and limited group interactions are strong factors in preventing widespread population-level outbreaks of infectious disease after such diseases have been introduced into the population. However, even a moderate amount of human contact increases disease spread and can lead to population-level outbreaks.

Towards a more healthy conservation paradigm: integrating disease and molecular ecology to aid biological conservation†

Parasites, and the diseases they cause, are important from an ecological and evolutionary perspective because they can negatively affect host fitness and can regulate host populations. Consequently, conservation biology has long recognized the vital role that parasites can play in the process of species endangerment and recovery. However, we are only beginning to understand how deeply parasites are embedded in ecological systems, and there is a growing recognition of the important ways in which parasites affect ecosystem structure and function. Thus, there is an urgent need to revisit how parasites are viewed from a conservation perspective and broaden the role that disease ecology plays in conservation-related research and outcomes. This review broadly focusses on the role that disease ecology can play in biological conservation. Our review specifically emphasizes on how the integration of tools and analytical approaches associated with both disease and molecular ecology can be leveraged to aid conservation biology. Our review first concentrates on disease-mediated extinctions and wildlife epidemics. We then focus on elucidating how host–parasite interactions has improved our understanding of the eco-evolutionary dynamics affecting hosts at the individual, population, community and ecosystem scales. We believe that the role of parasites as drivers and indicators of ecosystem health is especially an exciting area of research that has the potential to fundamentally alter our view of parasites and their role in biological conservation. The review concludes with a broad overview of the current and potential applications of modern genomic tools in disease ecology to aid biological conservation.

Retrospective methodology to estimate daily infections from deaths (REMEDID) in COVID-19: the Spain case study

The number of new daily infections is one of the main parameters to understand the dynamics of an epidemic. During the COVID-19 pandemic in 2020, however, such information has been underestimated. Here, we propose a retrospective methodology to estimate daily infections from daily deaths, because those are usually more accurately documented. Given the incubation period, the time from illness onset to death, and the case fatality ratio, the date of death can be estimated from the date of infection. We apply this idea conversely to estimate infections from deaths. This methodology is applied to Spain and its 19 administrative regions. Our results showed that probable daily infections during the first wave were between 35 and 42 times more than those officially documented on 14 March, when the national government decreed a national lockdown and 9 times more than those documented by the updated version of the official data. The national lockdown had a strong effect on the growth rate of virus transmission, which began to decrease immediately. Finally, the first inferred infection in Spain is about 43 days before the official data were available during the first wave. The current official data show delays of 15-30 days in the first infection relative to the inferred infections in 63% of the regions. In summary, we propose a methodology that allows reinterpretation of official daily infections, improving data accuracy in infection magnitude and dates because it assimilates valuable information from the National Seroprevalence Studies.

Town population size and structuring into villages and households drive infectious disease risks in pre-healthcare Finland

Social life is often considered to cost in terms of increased parasite or pathogen risk. However, evidence for this in the wild remains equivocal, possibly because populations and social groups are often structured, which affects the local transmission and extinction of diseases. We test how the structuring of towns into villages and households influenced the risk of dying from three easily diagnosable infectious diseases-smallpox, pertussis and measles-using a novel dataset covering almost all of Finland in the pre-healthcare era (1800-1850). Consistent with previous results, the risk of dying from all three diseases increased with the local population size. However, the division of towns into a larger number of villages decreased the risk of dying from smallpox and to some extent of pertussis but it slightly increased the risk for measles. Dividing towns into a larger number of households increased the length of the epidemic for all three diseases and led to the expected slower spread of the infection. However, this could be seen only when local population sizes were small. Our results indicate that the effect of population structure on epidemics, disease or parasite risk varies between pathogens and population sizes, hence lowering the ability to generalize the consequences of epidemics in spatially structured populations, and mapping the costs of social life, via parasites and diseases.

African Lions and Zoonotic Diseases: Implications for Commercial Lion Farms in South Africa

Simple Summary

In South Africa, thousands of African lions are bred on farms for commercial purposes, such as tourism, trophy hunting, and traditional medicine. Lions on farms often have direct contact with people, such as farm workers and tourists. Such close contact between wild animals and humans creates opportunities for the spread of zoonotic diseases (diseases that can be passed between animals and people). To help understand the health risks associated with lion farms, our study compiled a list of pathogens (bacteria, viruses, parasites, and fungi) known to affect African lions. We reviewed 148 scientific papers and identified a total of 63 pathogens recorded in both wild and captive lions, most of which were parasites (35, 56%), followed by viruses (17, 27%) and bacteria (11, 17%). This included pathogens that can be passed from lions to other animals and to humans. We also found a total of 83 diseases and clinical symptoms associated with these pathogens. Given that pathogens and their associated infectious diseases can cause harm to both animals and public health, we recommend that the lion farming industry in South Africa takes action to prevent and manage potential disease outbreaks.

Abstract

African lions (Panthera leo) are bred in captivity on commercial farms across South Africa and often have close contact with farm staff, tourists, and other industry workers. As transmission of zoonotic diseases occurs through close proximity between wildlife and humans, these commercial captive breeding operations pose a potential risk to thousands of captive lions and to public health. An understanding of pathogens known to affect lions is needed to effectively assess the risk of disease emergence and transmission within the industry. Here, we conduct a systematic search of the academic literature, identifying 148 peer-reviewed studies, to summarize the range of pathogens and parasites known to affect African lions. A total of 63 pathogenic organisms were recorded, belonging to 35 genera across 30 taxonomic families. Over half were parasites (35, 56%), followed by viruses (17, 27%) and bacteria (11, 17%). A number of novel pathogens representing unidentified and undescribed species were also reported. Among the pathogenic inventory are species that can be transmitted from lions to other species, including humans. In addition, 83 clinical symptoms and diseases associated with these pathogens were identified. Given the risks posed by infectious diseases, this research highlights the potential public health risks associated with the captive breeding industry. We recommend that relevant authorities take imminent action to help prevent and manage the risks posed by zoonotic pathogens on lion farms.

Effective Network Size Predicted From Simulations of Pathogen Outbreaks Through Social Networks Provides a Novel Measure of Structure-Standardized Group Size

The transmission of infectious disease through a population is often modeled assuming that interactions occur randomly in groups, with all individuals potentially interacting with all other individuals at an equal rate. However, it is well known that pairs of individuals vary in their degree of contact. Here, we propose a measure to account for such heterogeneity: effective network size (ENS), which refers to the size of a maximally complete network (i.e., unstructured, where all individuals interact with all others equally) that corresponds to the outbreak characteristics of a given heterogeneous, structured network. We simulated susceptible-infected (SI) and susceptible-infected-recovered (SIR) models on maximally complete networks to produce idealized outbreak duration distributions for a disease on a network of a given size. We also simulated the transmission of these same diseases on random structured networks and then used the resulting outbreak duration distributions to predict the ENS for the group or population. We provide the methods to reproduce these analyses in a public R package, "enss." Outbreak durations of simulations on randomly structured networks were more variable than those on complete networks, but tended to have similar mean durations of disease spread. We then applied our novel metric to empirical primate networks taken from the literature and compared the information represented by our ENSs to that by other established social network metrics. In AICc model comparison frameworks, group size and mean distance proved to be the metrics most consistently associated with ENS for SI simulations, while group size, centralization, and modularity were most consistently associated with ENS for SIR simulations. In all cases, ENS was shown to be associated with at least two other independent metrics, supporting its use as a novel metric. Overall, our study provides a proof of concept for simulation-based approaches toward constructing metrics of ENS, while also revealing the conditions under which this approach is most promising.

Infectious Disease in Wild Animal Populations: Examining Transmission and Control with Mathematical Models

The mathematical modeling of ecological interactions is an essential tool in predicting the behavior of complex systems across landscapes. The scientific literature is growing with examples of models used to explore predator-prey interactions, resource selection, population growth, and dynamics of disease transmission. These models provide managers with an efficient alternative means of testing new management and control strategies without resorting to empirical testing that is often costly, time-consuming, and impractical. This chapter presents a review of four types of mathematical models used to understand and predict the spread of infectious diseases in wild animals: compartmental, metapopulation, spatial, and contact network models. Descriptions of each model’s uses and limitations are used to provide a look at the complexities involved in modeling the spread of diseases and the trade-offs that accompany selecting one modeling approach over another. Potential avenues for the improvement and use of these models in future studies are also discussed, as are specific examples of how each type of model has improved our understanding of infectious diseases in populations of wild animals.

Microbial Genomics of a Host-Associated Commensal Bacterium in Fragmented Populations of Endangered Takahe

Isolation of wildlife into fragmented populations as a consequence of anthropogenic-mediated environmental change may alter host-pathogen relationships. Our understanding of some of the epidemiological features of infectious disease in vulnerable populations can be enhanced by the use of commensal bacteria as a proxy for invasive pathogens in natural ecosystems. The distinctive population structure of a well-described meta-population of a New Zealand endangered flightless bird, the takahe (Porphyrio hochstetteri), provided a unique opportunity to investigate the influence of host isolation on enteric microbial diversity. The genomic epidemiology of a prevalent rail-associated endemic commensal bacterium was explored using core genome and ribosomal multilocus sequence typing (rMLST) of 70 Campylobacter sp. nova 1 isolated from one third of the takahe population resident in multiple locations. While there was evidence of recombination between lineages, bacterial divergence appears to have occurred and multivariate analysis of 52 rMLST genes revealed location-associated differentiation of C. sp. nova 1 sequence types. Our results indicate that fragmentation and anthropogenic manipulation of populations can influence host-microbial relationships, with potential implications for niche adaptation and the evolution of micro-organisms in remote environments. This study provides a novel framework in which to explore the complex genomic epidemiology of micro-organisms in wildlife populations.

Social structure and Escherichia coli sharing in a group-living wild primate, Verreaux's sifaka

Background

Epidemiological models often use information on host social contacts to predict the potential impact of infectious diseases on host populations and the efficiency of control measures. It can be difficult, however, to determine whether social contacts are actually meaningful predictors of transmission. We investigated the role of host social structure in the transmission of Escherichia coli in a wild population of primates, Verreaux’s sifakas (Propithecus verreauxi). Using multilocus sequence typing (MLST), we compared genetic similarities between E. coli isolates from different individuals and groups to infer transmission pathways.

Results

Correlation of social and transmission networks revealed that membership to the same group significantly predicted sharing of E. coli MLST sequence types (ST). Intergroup encounter rate and a measure of space-use sharing provided equally potent explanations for type sharing between social groups when closely related STs were taken into account, whereas animal age, sex and dispersal history had no influence. No antibiotic resistance was found, suggesting low rates of E. coli spillover from humans into this arboreal species.

Conclusions

We show that patterns of E. coli transmission reflect the social structure of this group-living lemur species. We discuss our results in the light of the species’ ecology and propose scent-marking, a type of social contact not considered in previous epidemiological studies, as a likely route of transmission between groups. However, further studies are needed to explicitly test this hypothesis and to further elucidate the relative roles of direct contact and environmental transmission in pathogen transfer.

Electronic supplementary material

The online version of this article (doi:10.1186/s12898-016-0059-y) contains supplementary material, which is available to authorized users.

Relevance of workplace social mixing during influenza pandemics: an experimental modelling study of workplace cultures

Workplaces are one of the most important regular meeting places in society. The aim of this study was to use simulation experiments to examine the impact of different workplace cultures on influenza dissemination during pandemics. The impact is investigated by experiments with defined social-mixing patterns at workplaces using semi-virtual models based on authentic sociodemographic and geographical data from a North European community (population 136 000). A simulated pandemic outbreak was found to affect 33% of the total population in the community with the reference academic-creative workplace culture; virus transmission at the workplace accounted for 10·6% of the cases. A model with a prevailing industrial-administrative workplace culture generated 11% lower incidence than the reference model, while the model with a self-employed workplace culture (also corresponding to a hypothetical scenario with all workplaces closed) produced 20% fewer cases. The model representing an academic-creative workplace culture with restricted workplace interaction generated 12% lower cumulative incidence compared to the reference model. The results display important theoretical associations between workplace social-mixing cultures and community-level incidence rates during influenza pandemics. Social interaction patterns at workplaces should be taken into consideration when analysing virus transmission patterns during influenza pandemics.

Raccoon contact networks predict seasonal susceptibility to rabies outbreaks and limitations of vaccination

Infectious disease transmission often depends on the contact structure of host populations. Although it is often challenging to capture the contact structure in wild animals, new technology has enabled biologists to obtain detailed temporal information on wildlife social contacts. In this study, we investigated the effects of raccoon contact patterns on rabies spread using network modelling. Raccoons (Procyon lotor) play an important role in the maintenance of rabies in the United States. It is crucial to understand how contact patterns influence the spread of rabies in raccoon populations in order to design effective control measures and to prevent transmission to human populations and other animals. We constructed a dynamic system of contact networks based on empirical data from proximity logging collars on a wild suburban raccoon population and then simulated rabies spread across these networks. Our contact networks incorporated the number and duration of raccoon interactions. We included differences in contacts according to sex and season, and both short-term acquaintances and long-term associations. Raccoons may display different behaviours when infectious, including aggression (furious behaviour) and impaired mobility (dumb behaviour); the network model was used to assess the impact of potential behavioural changes in rabid raccoons. We also tested the effectiveness of different vaccination coverage levels. Our results demonstrate that when rabies enters a suburban raccoon population, the likelihood of a disease outbreak affecting the majority of the population is high. Both the magnitude of rabies outbreaks and the speed of rabies spread depend strongly on the time of year that rabies is introduced into the population. When there is a combination of dumb and furious behaviours in the rabid raccoon population, there are similar outbreak sizes and speed of spread to when there are no behavioural changes due to rabies infection. By incorporating detailed data describing the variation in raccoon contact rates into a network modelling approach, we were able to show that suburban raccoon populations are highly susceptible to rabies outbreaks, that the risk of large outbreaks varies seasonally and that current vaccination target levels may be inadequate to prevent the spread of rabies within these populations. Our findings provide new insights into rabies dynamics in raccoon populations and have important implications for disease control.

Infectious disease transmission and contact networks in wildlife and livestock

The use of social and contact networks to answer basic and applied questions about infectious disease transmission in wildlife and livestock is receiving increased attention. Through social network analysis, we understand that wild animal and livestock populations, including farmed fish and poultry, often have a heterogeneous contact structure owing to social structure or trade networks. Network modelling is a flexible tool used to capture the heterogeneous contacts of a population in order to test hypotheses about the mechanisms of disease transmission, simulate and predict disease spread, and test disease control strategies. This review highlights how to use animal contact data, including social networks, for network modelling, and emphasizes that researchers should have a pathogen of interest in mind before collecting or using contact data. This paper describes the rising popularity of network approaches for understanding transmission dynamics in wild animal and livestock populations; discusses the common mismatch between contact networks as measured in animal behaviour and relevant parasites to match those networks; and highlights knowledge gaps in how to collect and analyse contact data. Opportunities for the future include increased attention to experiments, pathogen genetic markers and novel computational tools.

Seasonal patterns of mixed species groups in large East African mammals

Mixed mammal species groups are common in East African savannah ecosystems. Yet, it is largely unknown if co-occurrences of large mammals result from random processes or social preferences and if interspecific associations are consistent across ecosystems and seasons. Because species may exchange important information and services, understanding patterns and drivers of heterospecific interactions is crucial for advancing animal and community ecology. We recorded 5403 single and multi-species clusters in the Serengeti-Ngorongoro and Tarangire-Manyara ecosystems during dry and wet seasons and used social network analyses to detect patterns of species associations. We found statistically significant associations between multiple species and association patterns differed spatially and seasonally. Consistently, wildebeest and zebras preferred being associated with other species, whereas carnivores, African elephants, Maasai giraffes and Kirk's dik-diks avoided being in mixed groups. During the dry season, we found that the betweenness (a measure of importance in the flow of information or disease) of species did not differ from a random expectation based on species abundance. In contrast, in the wet season, we found that these patterns were not simply explained by variations in abundances, suggesting that heterospecific associations were actively formed. These seasonal differences in observed patterns suggest that interspecific associations may be driven by resource overlap when resources are limited and by resource partitioning or anti-predator advantages when resources are abundant. We discuss potential mechanisms that could drive seasonal variation in the cost-benefit tradeoffs that underpin the formation of mixed-species groups.

The effect of host social system on parasite population genetic structure: comparative population genetics of two ectoparasitic mites and their bat hosts

Background

The population genetic structure of a parasite, and consequently its ability to adapt to a given host, is strongly linked to its own life history as well as the life history of its host. While the effects of parasite life history on their population genetic structure have received some attention, the effect of host social system has remained largely unstudied. In this study, we investigated the population genetic structure of two closely related parasitic mite species (Spinturnix myoti and Spinturnix bechsteini) with very similar life histories. Their respective hosts, the greater mouse-eared bat (Myotis myotis) and the Bechstein’s bat (Myotis bechsteinii) have social systems that differ in several substantial features, such as group size, mating system and dispersal patterns.

Results

We found that the two mite species have strongly differing population genetic structures. In S. myoti we found high levels of genetic diversity and very little pairwise differentiation, whereas in S. bechsteini we observed much less diversity, strongly differentiated populations and strong temporal turnover. These differences are likely to be the result of the differences in genetic drift and dispersal opportunities afforded to the two parasites by the different social systems of their hosts.

Conclusions

Our results suggest that host social system can strongly influence parasite population structure. As a result, the evolutionary potential of these two parasites with very similar life histories also differs, thereby affecting the risk and evolutionary pressure exerted by each parasite on its host.