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James O. Lloyd-Smith

Publications and source records attributed to James O. Lloyd-Smith.

9 recordsLinked to original sources
USGS2021

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.

Virunga Massif↗
USGS2017

Inferring infection hazard in wildlife populations by linking data across individual and population scales

Our ability to infer unobservable disease-dynamic processes such as force of infection (infection hazard for susceptible hosts) has transformed our understanding of disease transmission mechanisms and capacity to predict disease dynamics. Conventional methods for inferring FOI estimate a time-averaged value and are based on population-level processes. Because many pathogens exhibit epidemic cycling and FOI is the result of processes acting across the scales of individuals and populations, a flexible framework that extends to epidemic dynamics and links within-host processes to FOI is needed. Specifically, within-host antibody kinetics in wildlife hosts can be short-lived and produce patterns that are repeatable across individuals, suggesting individual-level antibody concentrations could be used to infer time since infection and hence FOI. Using simulations and case studies (influenza A in lesser snow geese and Yersinia pestis in coyotes), we argue that with careful experimental and surveillance design, the population-level FOI signal can be recovered from individual-level antibody kinetics, despite substantial individual-level variation. In addition to improving inference, the cross-scale quantitative antibody approach we describe can reveal insights into drivers of individual-based variation in disease response, and the role of poorly understood processes such as secondary infections, in population-level dynamics of disease.

Ecology Letters↗
USGS2014

Assembling evidence for identifying reservoirs of infection

Many pathogens persist in multihost systems, making the identification of infection reservoirs crucial for devising effective interventions. Here, we present a conceptual framework for classifying patterns of incidence and prevalence, and review recent scientific advances that allow us to study and manage reservoirs simultaneously. We argue that interventions can have a crucial role in enriching our mechanistic understanding of how reservoirs function and should be embedded as quasi-experimental studies in adaptive management frameworks. Single approaches to the study of reservoirs are unlikely to generate conclusive insights whereas the formal integration of data and methodologies, involving interventions, pathogen genetics, and contemporary surveillance techniques, promises to open up new opportunities to advance understanding of complex multihost systems.

Trends in Ecology and Evolution↗
USGS2006

Utility of R0 as a predictor of disease invasion in structured populations

Early theoretical work on disease invasion typically assumed large and well-mixed host populations. Many human and wildlife systems, however, have small groups with limited movement among groups. In these situations, the basic reproductive number, R 0 , is likely to be a poor predictor of a disease pandemic because it typically does not account for group structure and movement of individuals among groups. We extend recent work by combining the movement of hosts, transmission within groups, recovery from infection and the recruitment of new susceptibles into a stochastic model of disease in a host metapopulation. We focus on how recruitment of susceptibles affects disease invasion and how population structure can affect the frequency of superspreading events (SSEs). We show that the frequency of SSEs may decrease with the reduced movement and the group sizes due to the limited number of susceptible individuals available. Classification tree analysis of the model results illustrates the hierarchical nature of disease invasion in host metapopulations. First, the pathogen must effectively transmit within a group ( R 0 >1), and then the pathogen must persist within a group long enough to allow for movement among the groups. Therefore, the factors affecting disease persistence—such as infectious period, group size and recruitment of new susceptibles—are as important as the local transmission rates in predicting the spread of pathogens across a metapopulation.

Journal of the Royal Society Interface↗
USGS2005

Disentangling association patterns in fission-fusion societies using African buffalo as an example

A description of the social network of a population aids us in understanding dispersal, the spread of disease, and genetic structure in that population. Many animal populations can be classified as fission–fusion societies, whereby groups form and separate over time. Examples discussed in the literature include ungulates, primates and cetaceans (Lott and Minta, 1983, Whitehead et al., 1991, Henzi et al., 1997, Christal et al., 1998 and Chilvers and Corkeron, 2002). In this study, we use a heuristic simulation model to illustrate potential problems in applying traditional techniques of association analysis to fission–fusion societies and propose a new index of association: the fission decision index (FDI). We compare the conclusions resulting from traditional methods with those of the FDI using data from African buffalo, Syncerus caffer , in the Kruger National Park. The traditional approach suggested that the buffalo population was spatially and temporally structured into four different ‘herds’ with adult males only peripherally associated with mixed herds. Our FDI method indicated that association decisions of adult males appeared random, but those of other sex and age categories were nonrandom, particularly when we included the fission events associated with adult males. Furthermore, the amount of time that individuals spent together was only weakly correlated with their propensity to remain together during fission events. We conclude with a discussion of the applicability of the FDI to other studies.

Kruger National Park↗
USGS2005

Should we expect population thresholds for wildlife disease?

Host population thresholds for invasion or persistence of infectious disease are core concepts of disease ecology, and underlie on-going and controversial disease control policies based on culling and vaccination. Empirical evidence for these thresholds in wildlife populations has been sparse, however, though recent studies have narrowed this gap. Here we review the theoretical bases for population thresholds for disease, revealing why they are difficult to measure and sometimes are not even expected, and identifying important facets of wildlife ecology left out of current theories. We discuss strengths and weaknesses of selected empirical studies that have reported disease thresholds for wildlife, identify recurring obstacles, and discuss implications of our imperfect understanding of wildlife thresholds for disease control policy.

Trends in Ecology and Evolution↗
USGS2005

Duelling timescales of host mixing and disease spread determine invasion of disease in structured populations

The epidemic potential of a disease is traditionally assessed using the basic reproductive number, R 0 . However, in populations with social or spatial structure a chronic disease is more likely to invade than an acute disease with the same R 0 , because it persists longer within each group and allows for more host movement between groups. Acute diseases ‘perceive’ a more structured host population, and it is more important to consider host population structure in analyses of these diseases. The probability of a pandemic does not arise independently from characteristics of either the host or disease, but rather from the interaction of host movement and disease recovery timescales. The R * statistic, a group-level equivalent of R 0 , is a better indicator of disease invasion in structured populations than the individual-level R 0 .

Ecology Letters↗
USGS2004

Integrating association data and disease dynamics: an illustration using African Buffalo in Kruger National Park

Recognition is a prerequisite for non-random association amongst individuals. We explore how non-random association patterns (i.e. who spends time with whom) affect disease dynamics. We estimated the amount of time individuals spent together per month using radio-tracking data from African buffalo and incorporated these data into a dynamic social network model. The dynamic nature of the network has a strong influence on simulated disease dynamics particularly for diseases with shorter infectious periods. Cluster analyses of the association data demonstrated that buffalo herds were not as well defined as previously thought. Associations were more tightly clustered in 2002 than 2003, perhaps due to drier conditions in 2003. As a result, diseases may spread faster during drought conditions due to increased population mixing. Association data are often collected but this is the first use of empirical data in a network disease model in a wildlife population.

Kruger National Park↗