When can we infer mechanism from parasite aggregation? A constraint-based approach to disease ecology

Reconstructing multi-strain pathogen interactions from cross-sectional survey data via statistical network inference

Infectious diseases often involve multiple pathogen species or multiple strains of the same pathogen. As such, knowledge of how different pathogens interact is key to understand and predict the outcome of interventions targeting only a subset of species or strains involved in disease. Population-level data may be useful to infer pathogen strain interactions, but most previously used inference methods only consider uniform interactions between all strains or focus on marginal pairwise interactions. As such, these methods are prone to bias induced by indirect interactions through other strains. Here, we evaluated statistical network inference for reconstructing heterogeneous interactions from cross-sectional surveys detecting joint presence/absence patterns of pathogen strains within hosts. We applied various network models to simulated survey data, representing endemic infection states of multiple pathogen strains with potential interactions in acquisition or clearance of infection. Satisfactory performance was demonstrated by the estimators converging to the true interactions. Accurate reconstruction of interaction networks was achieved by regularization or penalization for sample size. Although performance deteriorated in the presence of host heterogeneity, this was overcome by correcting for individual-level risk factors. Our work demonstrates how statistical network inference could prove useful for detecting multi-strain pathogen interactions and may have applications beyond epidemiology.

Paragonimus westermani infection of freshwater crab Sundathelphusa philippina and melaniid snails in Cadacan River in Irosin, Sorsogon, Philippines

Paragonimiasis, the disease caused by Paragonimus westermani, is transmitted primarily by freshwater crabs Sundathelphusa philippina in the Philippines. Human infection has been recorded, but there is a dearth of published information on the extent of infection in animal reservoirs, particularly in crabs and snails. This study aimed to investigate the infection status and risk factors of P. westermani in freshwater crabs and melaniid snails collected in an endemic village along Cadacan River in Irosin, Sorsogon, where human cases of paragonimiasis were previously reported. A total of 246 freshwater crabs (118 females, 128 males) were dissected, and the gills, muscles, gonads, and viscera were examined for the presence of metacercariae; of which, 41.87% were found infected. The metacercariae were recovered from the gills (100%) and muscle tissues (7.3%) of infected crabs. Male crabs were more likely to be infected (49.22%) than female crabs (33.90%) (p < 0.05). Moreover, 70.87% of crabs showed low parasite intensity levels at ≤ 30 metacercariae/g tissue. A negative weak correlation was observed between parasite intensity and crab weight and carapace length, i.e., highly infected crabs were found to be smaller in size. Meanwhile, only 12% of the 150 melaniid snails collected were positive with cercariae with Tarebia granifera and Jagora asperata as the most infected species. Household survey conducted revealed that some knowledge, attitudes, and practices of the locals contribute to the sustained transmission of the parasite in this endemic area. These findings revealed that P. westermani is still prevalent among intermediate hosts and that some social and environmental factors contributed to the sustained parasite transmission in this endemic community.

Transient disease dynamics across ecological scales

Analyses of transient dynamics are critical to understanding infectious disease transmission and persistence. Identifying and predicting transients across scales, from within-host to community-level patterns, plays an important role in combating ongoing epidemics and mitigating the risk of future outbreaks. Moreover, greater emphases on non-asymptotic processes will enable timely evaluations of wildlife and human diseases and lead to improved surveillance efforts, preventive responses, and intervention strategies. Here, we explore the contributions of transient analyses in recent models spanning the fields of epidemiology, movement ecology, and parasitology. In addition to their roles in predicting epidemic patterns and endemic outbreaks, we explore transients in the contexts of pathogen transmission, resistance, and avoidance at various scales of the ecological hierarchy. Examples illustrate how (i) transient movement dynamics at the individual host level can modify opportunities for transmission events over time; (ii) within-host energetic processes often lead to transient dynamics in immunity, pathogen load, and transmission potential; (iii) transient connectivity between discrete populations in response to environmental factors and outbreak dynamics can affect disease spread across spatial networks; and (iv) increasing species richness in a community can provide transient protection to individuals against infection. Ultimately, we suggest that transient analyses offer deeper insights and raise new, interdisciplinary questions for disease research, consequently broadening the applications of dynamical models for outbreak preparedness and management.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s12080-021-00514-w.

Parasite resource manipulation drives bimodal variation in infection duration

Over a billion people on earth are infected with helminth parasites and show remarkable variation in parasite burden and chronicity. These parasite distributions are captured well by classic statistics, such as the negative binomial distribution. But the within-host processes underlying this variation are not well understood. In this study, we explain variation in macroparasite infection outcomes on the basis of resource flows within hosts. Resource flows realize the interactions between parasites and host immunity and metabolism. When host metabolism is modulated by parasites, we find a positive feedback of parasites on their own resources. While this positive feedback results in parasites improving their resource availability at high burdens, giving rise to chronic infections, it also results in a threshold biomass required for parasites to establish in the host, giving rise to acute infections when biomass fails to clear the threshold. Our finding of chronic and acute outcomes in bistability contrasts with classic theory, yet is congruent with the variation in helminth burdens observed in human and wildlife populations.

Measuring aggregation in parasite populations

Aggregation, a fundamental feature of parasite distributions, has been measured using a variety of indices. We use the definition that parasite-host system A is more aggregated than parasite-host system B if any given proportion of the parasite population is concentrated in a smaller proportion of the host population A than of host population B. This leads to indices based on the Lorenz curve such as the Gini index (Poulin's D), coefficient of variation and the Hoover index, all of which measure departure from a uniform distribution. The Hoover index is particularly useful because it can be interpreted directly in terms of parasites and hosts. An alternative view of aggregation is degree of departure from a Poisson (or random) distribution, as used in the index of dispersion and the negative binomial k. These and Lloyd's mean crowding index are reinterpreted and connected back to Lorenz curves. Aggregation has occasionally been defined as the slope from Taylor's law, although the slope appears unrelated to other indices. The Hoover index may be the method of choice when data points are available, and the coefficient of variation when only variance and mean are given.

Heterogeneity in helminth infections: factors influencing aggregation in a simple host-parasite system

The almost universally-occurring aggregated distributions of helminth burdens in host populations have major significance for parasite population ecology and evolutionary biology, but the mechanisms generating heterogeneity remain poorly understood. For the direct life cycle monogenean Discocotyle sagittata infecting rainbow trout, Oncorhynchus mykiss, variables potentially influencing aggregation can be analysed individually. This study was based at a fish farm where every host individual becomes infected by D. sagittata during each annual transmission period. Worm burdens were examined in one trout population maintained in isolation for 9 years, exposed to self-contained transmission. After this year-on-year recruitment, prevalence was 100% with intensities 10-2628, mean 576, worms per host. Parasite distribution, amongst hosts with the same age and environmental experience, was highly aggregated with variance to mean ratio 834 and negative binomial parameter, k, 0.64. The most heavily infected 20% of fish carried around 80% of the total adult parasite population. Aggregation develops within the first weeks post-infection; hosts typically carried intensities of successive age-specific cohorts that were consistent for that individual, such that heavily-infected individuals carried high numbers of all parasite age classes. Results suggest that host factors alone, operating post-infection, are sufficient to generate strongly overdispersed parasite distributions, rather than heterogeneity in exposure and initial invasion.

Detecting within-host interactions from genotype combination prevalence data

Parasite genetic diversity can provide information on disease transmission dynamics but most mathematical and statistical frameworks ignore the exact combinations of genotypes in infections. We introduce and validate a new method that combines explicit epidemiological modelling of coinfections and regression-Approximate Bayesian Computing (ABC) to detect within-host interactions. Using a susceptible-infected-susceptible (SIS) model, we show that, if sufficiently strong, within-host parasite interactions can be detected from epidemiological data. We also show that, in this simple setting, this detection is robust even in the face of some level of host heterogeneity in behaviour. These simulations results offer promising applications to analyse large datasets of multiple infection prevalence data, such as those collected for genital infections by Human Papillomaviruses (HPVs).

Multi-year dynamics of ranavirus, chytridiomycosis, and co-infections in a temperate host assemblage of amphibians

Chytridiomycosis and ranavirosis are 2 emerging infectious diseases that have caused significant global amphibian decline. Although both have received much scrutiny, little is known about interactions between the 2 causative agents Batrachochytrium dendrobatidis (Bd) and ranavirus (Rv) at the individual host and population levels. We present the first longitudinal assessment of Bd, Rv, and co-infections of a temperate amphibian assemblage in North America. From 2012 to 2016, we assessed the temporal oscillations of Bd, Rv and co-infection dynamics in a sample of 729 animals representing 13 species. Bd, Rv, and co-infected amphibians were detected during all 5 yr. Bd, Rv, and co-infection prevalence all varied annually, with the lowest instances of each at 2.1% (2013), 7.9% (2016), and 0.6% (2016), respectively. The highest Bd, Rv, and co-infection prevalence were recorded in 2012 (26.8%), 2016 (38.3%), and 2015 (10.3%), respectively. There was no association between Bd or Rv infection prevalence and co-infection, either when assessing the entire amphibian assemblage as a whole (odds ratio 1.32, 95% CI: 0.83-2.1, p = 0.29) or within species for amphibians that were more numerically represented (n > 40, p > 0.05). This suggests neither Bd nor Rv facilitate host co-infections within the sampled host assemblage. Instead, the basis for co-infections is the spatiotemporal distribution of both pathogens. Despite lack of interplay between Bd and Rv in this population, our study highlights the importance of considering numerous pathogens that may be present within amphibian habitats in order to properly anticipate interactions that may have direct bearing on disease outcomes.

Biological and statistical processes jointly drive population aggregation: using host-parasite interactions to understand Taylor's power law

The macroecological pattern known as Taylor's power law (TPL) represents the pervasive tendency of the variance in population density to increase as a power function of the mean. Despite empirical illustrations in systems ranging from viruses to vertebrates, the biological significance of this relationship continues to be debated. Here we combined collection of a unique dataset involving 11 987 amphibian hosts and 332 684 trematode parasites with experimental measurements of core epidemiological outcomes to explicitly test the contributions of hypothesized biological processes in driving aggregation. After using feasible set theory to account for mechanisms acting indirectly on aggregation and statistical constraints inherent to the data, we detected strongly consistent influences of host and parasite species identity over 7 years of sampling. Incorporation of field-based measurements of host body size, its variance and spatial heterogeneity in host density accounted for host identity effects, while experimental quantification of infection competence (and especially virulence from the 20 most common host-parasite combinations) revealed the role of species-by-environment interactions. By uniting constraint-based theory, controlled experiments and community-based field surveys, we illustrate the joint influences of biological and statistical processes on parasite aggregation and emphasize their importance for understanding population regulation and ecological stability across a range of systems, both infectious and free-living.