Kimberly VanderWaal

Optimal group size, dispersal decisions and postdispersal relationships in female African lions

We used 40 years of long-term data to test whether dispersal decisions of female African lions, Panthera leo, are sensitive to variations in pride size, interpride competition and the quality of their natal territory. Per capita reproductive success reached a maximum at 3–6 females on the open grass plains of the Serengeti and at 3–11 females in the woodlands. Approximately 50% of female cohorts dispersed when potential pride size exceeded the habitat-specific optimum, whereas only 9% of cohorts dispersed at smaller pride sizes. Cohorts of one to two females rarely dispersed, especially in high-density habitats. Thus, pride size typically remained within the range that maximized individual reproductive success. In the high-density woodland habitat, females were less likely to disperse from prides that were surrounded by large numbers of unrelated females, as would be predicted on the basis of habitat saturation. However, the number of unrelated neighbours did not affect dispersal decisions of females living in the sparsely occupied plains habitat. After pride fission, daughters settled closer to their mothers in areas where there were greater numbers of unrelated female neighbours, but territories were just as exclusive as between unrelated neighbouring prides. Maternal prides in high-quality areas shared a greater percentage of their territory with descendant prides, but this tolerance diminished as relatedness declined through time.

Heterogeneity in pathogen transmission: mechanisms and methodology

Summary Heterogeneity in the ability of hosts to transmit pathogens is among the most fundamental concepts in disease dynamics and has major implications for disease control strategies. The number of secondary infections produced by an infected individual is a function of three components: an individuals infectiousness, the rate at which it contacts susceptible individuals and the duration of infection. Individual-level variation can emerge in each of these components through a combination of behavioural and physiological mechanisms. In this review, we describe mechanisms that promote variation in the number of individuals to which an individual transmits a pathogen, emphasizing insights that can be gained by understanding which components of transmission (infectiousness, contact rate, infection duration) are primarily affected. We also discuss how behavioural and physiological processes generate transmission heterogeneities across multiple scales, from individual-level variation to heterogeneity among species. Strategies for quantifying each transmission component are presented, and we discuss why studies focusing on only one component of the infection process may be misleading. To conclude, we describe how future research focusing on variation in transmission across all three components can provide a more holistic view of heterogeneity in pathogen transmission.

Network structure and prevalence of Cryptosporidium in Belding’s ground squirrels

Although pathogen transmission dynamics are profoundly affected by population social and spatial structure, few studies have empirically demonstrated the population-level implications of such structure in wildlife. In particular, epidemiological models predict that the extent to which contact patterns are clustered decreases a pathogen’s ability to spread throughout an entire population, but this effect has yet to be demonstrated in a natural population. Here, we use network analysis to examine patterns of transmission of an environmentally transmitted parasite, Cryptosporidium spp., in Belding’s ground squirrels (Spermophilus beldingi). We found that the prevalence of Cryptosporidium was negatively correlated with transitivity, a measure of network clustering, and positively correlated with the percentage of juvenile males. Additionally, network transitivity decreased when there were higher percentages of juvenile males; the exploratory behavior demonstrated by juvenile males may have altered the structure of the network by reducing clustering, and low clustering was associated with high prevalence. We suggest that juvenile males are critical in mediating the ability of Cryptosporidium to spread through colonies, and thus may function as “super-spreaders.” Our results demonstrate the utility of a network approach in quantifying mechanistically how differences in contact patterns may lead to system-level differences in infection patterns.

Multi-scale clustering by building a robust and self correcting ultrametric topology on data points.

The advent of high-throughput technologies and the concurrent advances in information sciences have led to an explosion in size and complexity of the data sets collected in biological sciences. The biggest challenge today is to assimilate this wealth of information into a conceptual framework that will help us decipher biological functions. A large and complex collection of data, usually called a data cloud, naturally embeds multi-scale characteristics and features, generically termed geometry. Understanding this geometry is the foundation for extracting knowledge from data. We have developed a new methodology, called data cloud geometry-tree (DCG-tree), to resolve this challenge. This new procedure has two main features that are keys to its success. Firstly, it derives from the empirical similarity measurements a hierarchy of clustering configurations that captures the geometric structure of the data. This hierarchy is then transformed into an ultrametric space, which is then represented via an ultrametric tree or a Parisi matrix. Secondly, it has a built-in mechanism for self-correcting clustering membership across different tree levels. We have compared the trees generated with this new algorithm to equivalent trees derived with the standard Hierarchical Clustering method on simulated as well as real data clouds from fMRI brain connectivity studies, cancer genomics, giraffe social networks, and Lewis Carrolls Doublets network. In each of these cases, we have shown that the DCG trees are more robust and less sensitive to measurement errors, and that they provide a better quantification of the multi-scale geometric structures of the data. As such, DCG-tree is an effective tool for analyzing complex biological data sets.

Network analysis of cattle movements in Uruguay: Quantifying heterogeneity for risk-based disease surveillance and control.

Movement of livestock between premises is one of the foremost factors contributing to the spread of infectious diseases of livestock. In part to address this issue, the origin and destination for all cattle movements in Uruguay are registered by law. This information has great potential to be used in assessing the risk of disease spread in the Uruguayan cattle population. Here, we analyze cattle movements from 2008 to 2013 using network analysis in order to understand the flows of animals in the Uruguayan cattle industry and to identify targets for surveillance and control measures. Cattle movements were represented as seasonal and annual networks in which farms represented nodes and nodes were linked based on the frequency and quantity of cattle moved. At the farm level, the distribution of the number of unique farms each farm is connected to through outgoing and incoming movements, as well as the number of animals moved, was highly right-skewed; the majority of farms had few to no contacts, whereas the 10% most highly connected farms accounted for 72-83% of animals moved annually. This extreme level of heterogeneity in movement patterns indicates that some farms may be disproportionately important for pathogen spread. Different production types exhibited characteristic patterns of farm-level connectivity, with some types, such a dairies, showing consistently higher levels of centrality. In addition, the observed networks were characterized by lower levels of connectivity and higher levels of heterogeneity than random networks of the same size and density, both of which have major implications for disease dynamics and control strategies. This represents the first in-depth analysis of farm-level livestock movements within South America, and highlights the importance of collecting livestock movement data in order to understand the vulnerability of livestock trade networks to invasion by infectious diseases.

Spatial and Temporal Epidemiology of Lumpy Skin Disease in the Middle East, 2012–2015

Lumpy skin disease virus (LSDV) is an infectious disease of cattle that can have severe economic implications. New LSD outbreaks are currently circulating in the Middle East (ME). Since 2012, severe outbreaks were reported in cattle across the region. Characterizing the spatial and temporal dynamics of LSDV in cattle populations is prerequisite for guiding successful surveillance and control efforts at a regional level in the ME. Here, we aim to model the ecological niche of LSDV and identify epidemic progression patterns over the course of the epidemic. We analyzed publically available outbreak data from the ME for the period 2012–2015 using presence-only maximum entropy ecological niche modeling and the time-dependent method for the estimation of the effective reproductive number (R-TD). High-risk areas (probability >0.60) for LSDV identified by ecological niche modeling included parts of many northeastern ME countries, though Israel and Turkey were estimated to be the most suitable locations for occurrence of LSDV outbreaks. The most important environmental predictors that contributed to the ecological niche of LSDV included annual precipitation, land cover, mean diurnal range, type of livestock production system, and global livestock densities. Average monthly effective R-TD was equal to 2.2 (95% CI: 1.2, 3.5), whereas the largest R-TD was estimated in Israel (R-TD = 22.2, 95 CI: 15.2, 31.5) in September 2013, which indicated that the demographic and environmental conditions during this period were suitable to LSDV super-spreading events. The sharp drop of Isreal’s inferred R-TD in the following month reflected the success of their 2013 vaccination campaign in controlling the disease. Our results identified areas in which underreporting of LSDV outbreaks may have occurred. More epidemiological information related to cattle populations are needed to further improve the inferred spatial and temporal characteristics of currently circulating LSDV. However, the methodology presented here may be useful in guiding the design of risk-based surveillance and control programs in the region as well as aid in the formulation of epidemic preparedness plans in neighboring LSDV-free countries.

Evaluating empirical contact networks as potential transmission pathways for infectious diseases

Networks are often used to incorporate heterogeneity in contact patterns in mathematical models of pathogen spread. However, few tools exist to evaluate whether potential transmission pathways in a population are adequately represented by an observed contact network. Here, we describe a novel permutation-based approach, the network k-test, to determine whether the pattern of cases within the observed contact network are likely to have resulted from transmission processes in the network, indicating that the network represents potential transmission pathways between nodes. Using simulated data of pathogen spread, we compare the power of this approach to other commonly used analytical methods. We test the robustness of this technique across common sampling constraints, including undetected cases, unobserved individuals and missing interaction data. We also demonstrate the application of this technique in two case studies of livestock and wildlife networks. We show that the power of the k-test to correctly identify the epidemiologic relevance of contact networks is substantially greater than other methods, even when 50% of contact or case data are missing. We further demonstrate that the impact of missing data on network analysis depends on the structure of the network and the type of missing data.

Parameter Values for Epidemiological Models of Foot-and-Mouth Disease in Swine

In the event of a foot-and-mouth disease (FMD) incursion, response strategies are required to control, contain, and eradicate the pathogen as efficiently as possible. Infectious disease simulation models are widely used tools that mimic disease dispersion in a population and that can be useful in the design and support of prevention and mitigation activities. However, there are often gaps in evidence-based research to supply models with quantities that are necessary to accurately reflect the system of interest. The objective of this study was to quantify values associated with the duration of the stages of FMD infection (latent period, subclinical period, incubation period, and duration of infection), probability of transmission (within-herd and between-herd via spatial spread), and diagnosis of a vesicular disease within a herd using a meta-analysis of the peer-reviewed literature and expert opinion. The latent period ranged from 1 to 7 days and incubation period ranged from 1 to 9 days; both were influenced by strain. In contrast, the subclinical period ranged from 0 to 6 days and was influenced by sampling method only. The duration of infection ranged from 1 to 10 days. The probability of spatial spread between an infected and fully susceptible swine farm was estimated as greatest within 5 km of the infected farm, highlighting the importance of possible long-range transmission through the movement of infected animals. Finally, while most swine practitioners are confident in their ability to detect a vesicular disease in an average sized swine herd, a small proportion expect that up to half of the herd would need to show clinical signs before detection via passive surveillance would occur. The results of this study will be useful in within- and between-herd simulation models to develop efficient response strategies in the event an FMD in swine populations of disease-free countries or regions.

Optimal surveillance strategies for bovine tuberculosis in a low-prevalence country

Bovine tuberculosis (bTB) is a chronic disease of cattle that is difficult to control and eradicate in part due to the costly nature of surveillance and poor sensitivity of diagnostic tests. Like many countries, bTB prevalence in Uruguay has gradually declined to low levels due to intensive surveillance and control efforts over the past decades. In low prevalence settings, broad-based surveillance strategies based on routine testing may not be the most cost-effective way for controlling between-farm bTB transmission, while targeted surveillance aimed at high-risk farms may be more efficient for this purpose. To investigate the efficacy of targeted surveillance, we developed an integrated within- and between-farm bTB transmission model utilizing data from Uruguay’s comprehensive animal movement database. A genetic algorithm was used to fit uncertain parameter values, such as the animal-level sensitivity of skin testing and slaughter inspection, to observed bTB epidemiological data. Of ten alternative surveillance strategies evaluated, a strategy based on eliminating testing in low-risk farms resulted in a 40% reduction in sampling effort without increasing bTB incidence. These results can inform the design of more cost-effective surveillance programs to detect and control bTB in Uruguay and other countries with low bTB prevalence.

Linking social and spatial networks to viral community phylogenetics reveals subtype-specific transmission dynamics in African lions

Heterogeneity within pathogen species can have important consequences for how pathogens transmit across landscapes; however, discerning different transmission routes is challenging. Here, we apply both phylodynamic and phylogenetic community ecology techniques to examine the consequences of pathogen heterogeneity on transmission by assessing subtype-specific transmission pathways in a social carnivore. We use comprehensive social and spatial network data to examine transmission pathways for three subtypes of feline immunodeficiency virus (FIVPle ) in African lions (Panthera leo) at multiple scales in the Serengeti National Park, Tanzania. We used FIVPle molecular data to examine the role of social organization and lion density in shaping transmission pathways and tested to what extent vertical (i.e., father- and/or mother-offspring relationships) or horizontal (between unrelated individuals) transmission underpinned these patterns for each subtype. Using the same data, we constructed subtype-specific FIVPle co-occurrence networks and assessed what combination of social networks, spatial networks or co-infection best structured the FIVPle network. While social organization (i.e., pride) was an important component of FIVPle transmission pathways at all scales, we find that FIVPle subtypes exhibited different transmission pathways at within- and between-pride scales. A combination of social and spatial networks, coupled with consideration of subtype co-infection, was likely to be important for FIVPle transmission for the two major subtypes, but the relative contribution of each factor was strongly subtype-specific. Our study provides evidence that pathogen heterogeneity is important in understanding pathogen transmission, which could have consequences for how endemic pathogens are managed. Furthermore, we demonstrate that community phylogenetic ecology coupled with phylodynamic techniques can reveal insights into the differential evolutionary pressures acting on virus subtypes, which can manifest into landscape-level effects.