Structural network characteristics affect epidemic severity and prediction in social contact networks

Understanding and mitigating epidemic spread in complex networks requires the measurement of structural network properties associated with epidemic risk. Classic measures of epidemic thresholds like the basic reproduction number (R0) have been adapted to account for the structure of social contact networks but still may be unable to capture epidemic potential relative to more recent measures based on spectral graph properties. Here, we explore the ability of R0 and the spectral radius of the social contact network to estimate epidemic susceptibility. To do so, we simulate epidemics on a series of constructed (small world, scale-free, and random networks) and a collection of over 700 empirical biological social contact networks. Further, we explore how other network properties are related to these two epidemic estimators (R0 and spectral radius) and mean infection prevalence in simulated epidemics. Overall, we find that network properties strongly influence epidemic dynamics and the subsequent utility of R0 and spectral radius as indicators of epidemic risk.

Estimating R 0 from early exponential growth: parallels between 1918 influenza and 2020 SARS-CoV-2 pandemics

The large spatial scale, geographical overlap, and similarities in transmission mode between the 1918 H1N1 influenza and 2020 SARS-CoV-2 pandemics together provide a novel opportunity to investigate relationships between transmission of two different diseases in the same location. To this end, we use initial exponential growth rates in a Bayesian hierarchical framework to estimate the basic reproductive number, R ₀, of both disease outbreaks in a common set of 43 cities in the United States. By leveraging multiple epidemic time series across a large spatial area, we are able to better characterize the variation in R ₀ across the United States. Additionally, we provide one of the first city-level comparisons of R ₀ between these two pandemics and explore how demography and outbreak timing are related to R ₀. Despite similarities in transmission modes and a common set of locations, R ₀ estimates for COVID-19 were uncorrelated with estimates of pandemic influenza R ₀ in the same cities. Also, the relationships between R ₀ and key population or epidemic traits differed between diseases. For example, epidemics that started later tended to be less severe for COVID-19, while influenza epidemics exhibited an opposite pattern. Our results suggest that despite similarities between diseases, epidemics starting in the same location may differ markedly in their initial progression.

Effects of occupancy estimation on abundance-occupancy relationships

Abundance-occupancy relationships predict that species that occupy more sites are also more locally abundant, where occupancy is usually estimated following the assumption that species can occupy all sampled sites. Here we use the National Ecological Observatory Network small-mammal data to assess whether this assumption affects abundance-occupancy relationships. We estimated occupancy considering all sampled sites (traditional occupancy) and only the sites found within the species geographic range (spatial occupancy) and realized environmental niche (environmental occupancy). We found that when occupancy was estimated considering only sites possible for the species to colonize (spatial and environmental occupancy) weaker abundance-occupancy relationships were observed. This shows that the assumption that the species can occupy all sampled sites directly affects the assessment of abundance-occupancy relationships. Estimating occupancy considering only sites that are possible for the species to colonize will consequently lead to a more robust assessment of abundance-occupancy relationships.

Improved chromosome-level genome assembly of the Glanville fritillary butterfly (Melitaea cinxia) integrating Pacific Biosciences long reads and a high-density linkage map

BACKGROUND

The Glanville fritillary (Melitaea cinxia) butterfly is a model system for metapopulation dynamics research in fragmented landscapes. Here, we provide a chromosome-level assembly of the butterfly's genome produced from Pacific Biosciences sequencing of a pool of males, combined with a linkage map from population crosses.

RESULTS

The final assembly size of 484 Mb is an increase of 94 Mb on the previously published genome. Estimation of the completeness of the genome with BUSCO indicates that the genome contains 92-94% of the BUSCO genes in complete and single copies. We predicted 14,810 genes using the MAKER pipeline and manually curated 1,232 of these gene models.

CONCLUSIONS

The genome and its annotated gene models are a valuable resource for future comparative genomics, molecular biology, transcriptome, and genetics studies on this species.

The ghost of hosts past: impacts of host extinction on parasite specificity

A growing body of research is focused on the extinction of parasite species in response to host endangerment and declines. Beyond the loss of parasite species richness, host extinction can impact apparent parasite host specificity, as measured by host richness or the phylogenetic distances among hosts. Such impacts on the distribution of parasites across the host phylogeny can have knock-on effects that may reshape the adaptation of both hosts and parasites, ultimately shifting the evolutionary landscape underlying the potential for emergence and the evolution of virulence across hosts. Here, we examine how the reshaping of host phylogenies through extinction may impact the host specificity of parasites, and offer examples from historical extinctions, present-day endangerment, and future projections of biodiversity loss. We suggest that an improved understanding of the impact of host extinction on contemporary host-parasite interactions may shed light on core aspects of disease ecology, including comparative studies of host specificity, virulence evolution in multi-host parasite systems, and future trajectories for host and parasite biodiversity. This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.

Forecasting parasite sharing under climate change

Species are shifting their distributions in response to climate change. This geographic reshuffling may result in novel co-occurrences among species, which could lead to unseen biotic interactions, including the exchange of parasites between previously isolated hosts. Identifying potential new host-parasite interactions would improve forecasting of disease emergence and inform proactive disease surveillance. However, accurate predictions of future cross-species disease transmission have been hampered by the lack of a generalized approach and data availability. Here, we propose a framework to predict novel host-parasite interactions based on a combination of niche modelling of future host distributions and parasite sharing models. Using the North American ungulates as a proof of concept, we show this approach has high cross-validation accuracy in over 85% of modelled parasites and find that more than 34% of the host-parasite associations forecasted by our models have already been recorded in the literature. We discuss potential sources of uncertainty and bias that may affect our results and similar forecasting approaches, and propose pathways to generate increasingly accurate predictions. Our results indicate that forecasting parasite sharing in response to shifts in host geographic distributions allow for the identification of regions and taxa most susceptible to emergent pathogens under climate change. This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.

The future of zoonotic risk prediction

In the light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programmes will identify hundreds of novel viruses that might someday pose a threat to humans. To support the extensive task of laboratory characterization, scientists may increasingly rely on data-driven rubrics or machine learning models that learn from known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions. What are the prerequisites, in terms of open data, equity and interdisciplinary collaboration, to the development and application of those tools? What effect could the technology have on global health? Who would control that technology, who would have access to it and who would benefit from it? Would it improve pandemic prevention? Could it create new challenges? This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.

Initial abundance and stochasticity influence competitive outcome in communities

Predicting competitive outcomes in communities frequently involves inferences based on deterministic population models since these provide clear criteria for exclusion (e.g. R* rule) or long-term coexistence (e.g. mutual invasibility). However, incorporating stochasticity into population- or community-level processes into models is necessary if the goal is to explain variation in natural systems, which are inherently stochastic. Similarly, in systems with demographic or environmental stochasticity, weaker competitors have the potential to exclude superior competitors, contributing to what is known as 'competitive indeterminacy'. The importance of such effects for natural communities is unknown, in part because it is difficult to demonstrate that multiple forms of stochasticity are present in these communities. Moreover, the effects of multiple forms of stochasticity on competitive outcomes are largely untested, even in theory. Here, we address these issues by examining the role of stochasticity in replicated communities of flour beetles (Tribolium sp.). To do so, we developed a set of two-species stochastic Ricker models incorporating four distinct forms of stochasticity: environmental stochasticity, demographic stochasticity, demographic heterogeneity and stochastic sex determination. By fitting models to experimental data, and simulating fit models to examine long- term behaviour, we found that both the duration of transient coexistence and the degree of competitive indeterminacy were sensitive to the forms of stochasticity included in our models. These findings suggest the current estimates of extinction risk, coexistence and time until competitive exclusion in communities may not be accurate when based on models that exclude relevant forms of stochasticity.

The effect of summer drought on the predictability of local extinctions in a butterfly metapopulation

The ecological impacts of extreme climatic events on population dynamics and community composition are profound and predominantly negative. Using extensive data of an ecological model system, we tested whether predictions from ecological models remain robust when environmental conditions are outside the bounds of observation. We observed a 10-fold demographic decline of the Glanville fritillary butterfly (Melitaea cinxia) metapopulation on the Åland islands, Finland in the summer of 2018 and used climatic and satellite data to demonstrate that this year was an anomaly with low climatic water balance values and low vegetation productivity indices across Åland. Population growth rates were strongly associated with spatiotemporal variation in climatic water balance. Covariates shown previously to affect the extinction probability of local populations in this metapopulation were less informative when populations were exposed to severe drought during the summer months. Our results highlight the unpredictable responses of natural populations to extreme climatic events.

Community context and dispersal stochasticity drive variation in spatial spread

Dispersal is a key process in shaping species spatial distributions. Species interactions and variation in dispersal probabilities may jointly influence species spatial dynamics. However, many studies examine dispersal as a neutral process, independent of community context or intraspecific variation in dispersal behaviour. Here, we use controlled, replicated communities of two Tribolium species (T. castaneum and T. confusum) to examine how intraspecific variation in dispersal behaviour and community context influence dispersal dynamics in simple experimental landscapes composed of homogeneous habitat patches. We found considerable individual-level variation in dispersal probability that was unrelated to body size variation. Further, the context of dispersal mattered, as T. castaneum dispersal was reduced in two-species communities, while T. confusum dispersal was unaffected by community composition. Incorporating individual-level variation into a two-species stochastic spatial Ricker model, we provide evidence that individual-level variability in dispersal behaviour results in more variable spatial spread than assuming individuals have the same dispersal probability. Further, interspecific competition resulted in more variable spatial spread. The variability in spatial spread observed in our tightly controlled and replicated experimental system and in our stochastic model simulations points to potential fundamental limitations in forecasting species shifting ranges without considering potential interspecific interactions and demographic variability in dispersal behaviour.

What determines parasite species richness across host species?

IN FOCUS

Dáttilo, W., Barrozo-Chávez, N., Lira-Noriega, A., Guevara, R., Villalobos, F., Santiago-Alarcon, D., Neves, F. S., Izzo, T., & Ribeiro, S. P. (2020). Species-level drivers of mammalian ectoparasite faunas. Journal of Animal Ecology. https://doi.org/10.1111/1365-2656.13216. The question of what drives the number of parasite species able to infect a given host species is still a largely open question, despite decades of research. Dáttilo and colleagues examine the potential drivers of ectoparasite species across a large set of host species to explore the taxonomic and trait drivers of host-parasite interactions. Here, we contextualize their findings, explore what is known about parasite species richness, and identify some potential next steps towards answers.

Multiple data sources and freely available code is critical when investigating species distributions and diversity: a response to Knouft (2018)

A recent comment from Knouft () has suggested that our original article (Dallas et al. ) was an 'inappropriate application of biodiversity data'. Here, we affirm our results, and address the more general point about biodiversity data use.

Estimating parasite host range

Estimating the number of host species that a parasite can infect (i.e. host range) provides key insights into the evolution of host specialism and is a central concept in disease ecology. Host range is rarely estimated in real systems, however, because variation in species relative abundance and the detection of rare species makes it challenging to confidently estimate host range. We applied a non-parametric richness indicator to estimate host range in simulated and empirical data, allowing us to assess the influence of sampling heterogeneity and data completeness. After validating our method on simulated data, we estimated parasite host range for a sparsely sampled global parasite occurrence database (Global Mammal Parasite Database) and a repeatedly sampled set of parasites of small mammals from New Mexico (Sevilleta Long Term Ecological Research Program). Estimation accuracy varied strongly with parasite taxonomy, number of parasite occurrence records, and the shape of host species-abundance distribution (i.e. the dominance and rareness of species in the host community). Our findings suggest that between 20% and 40% of parasite host ranges are currently unknown, highlighting a major gap in our understanding of parasite specificity, host-parasite network structure, and parasite burdens.

Predicting cryptic links in host-parasite networks

Networks are a way to represent interactions among one (e.g., social networks) or more (e.g., plant-pollinator networks) classes of nodes. The ability to predict likely, but unobserved, interactions has generated a great deal of interest, and is sometimes referred to as the link prediction problem. However, most studies of link prediction have focused on social networks, and have assumed a completely censused network. In biological networks, it is unlikely that all interactions are censused, and ignoring incomplete detection of interactions may lead to biased or incorrect conclusions. Previous attempts to predict network interactions have relied on known properties of network structure, making the approach sensitive to observation errors. This is an obvious shortcoming, as networks are dynamic, and sometimes not well sampled, leading to incomplete detection of links. Here, we develop an algorithm to predict missing links based on conditional probability estimation and associated, node-level features. We validate this algorithm on simulated data, and then apply it to a desert small mammal host-parasite network. Our approach achieves high accuracy on simulated and observed data, providing a simple method to accurately predict missing links in networks without relying on prior knowledge about network structure.

Fluctuating temperatures alter environmental pathogen transmission in a Daphnia-pathogen system

Environmental conditions are rarely constant, but instead vary spatially and temporally. This variation influences ecological interactions and epidemiological dynamics, yet most experimental studies examine interactions under constant conditions. We examined the effects of variability in temperature on the host–pathogen relationship between an aquatic zooplankton host (Daphnia laevis) and an environmentally transmitted fungal pathogen (Metschnikowia bicuspidata). We manipulated temperature variability by exposing all populations to mean temperatures of 20°C for the length of the experiments, but introducing periods of 1, 2, and 4 hr each day where the populations were exposed to 28°C followed by periods of the same length (1, 2, and 4 hr, respectively) where the populations were exposed to 12°C. Three experiments were performed to assess the role of thermal variability on Daphnia–pathogen interactions, specifically with respect to: (1) host infection prevalence and intensity; (2) free‐living pathogen survival; and (3) host foraging ecology. We found that temperature variability affected host filtering rate, which is closely related to pathogen transmission in this system. Further, infection prevalence was reduced as a function of temperature variability, while infection intensity was not influenced, suggesting that pathogen transmission was influenced by temperature variability, but the growth of pathogen within infected hosts was not. Host survival was reduced by temperature variability, but environmental pathogen survival was unaffected, suggesting that zooplankton hosts were more sensitive than the fungal pathogen to variable temperatures. Together, these experiments suggest that temperature variability may influence host demography and host–pathogen interactions, providing a link between host foraging ecology and pathogen transmission.

Competition-mediated feedbacks in experimental multispecies epizootics

Competition structures ecological communities and alters host-pathogen interactions. In environmentally transmitted pathogens, an infection-resistant competitor may influence infection dynamics in a susceptible species through the negative impacts of competition (e.g., by reducing host density or causing nutritional stress that increases susceptibility to infection) and/or the positive impacts of reducing transmission efficiency (e.g., by removing environmental pathogen stages). Thus, a non-susceptible competitor may enhance, reduce, or have no net effect on susceptible host density and infection prevalence. Here, we couple an epidemiological model with experimental epidemics to test how resource competition with a non-susceptible competitor (Daphnia pulicaria) influences fungal microparasite (Metschnikowia bicuspidata) infection dynamics in a susceptible host species (D. dentifera). Our model and experiments suggest that competitor density can mediate the direction and magnitude of the effect of competition on infection dynamics, with a peak in infection prevalence occurring at intermediate competitor densities. At low densities, the non-susceptible competitor D. pulicaria may reduce infection prevalence in the susceptible host by removing fungal spores from the environment through feeding. However, when competitor density is increased and resources become limiting, D. pulicaria negatively impacts the susceptible host by increasing susceptible host feeding rates, and therefore fungal spore intake, and further by reducing susceptible host population size as it is driven toward competitive exclusion. In conclusion, these results show that a tradeoff between the competitor as a consumer of pathogen, which serves to reduce epidemic size, and as a modifier of susceptible host foraging ecology, which influences infection rates, may alternately enhance or dampen the magnitude of local epidemics.

Costs of resistance and infection by a generalist pathogen

Pathogen infection is typically costly to hosts, resulting in reduced fitness. However, pathogen exposure may also come at a cost even if the host does not become infected. These fitness reductions, referred to as “resistance costs”, are inducible physiological costs expressed as a result of a trade‐off between resistance to a pathogen and aspects of host fitness (e.g., reproduction). Here, we examine resistance and infection costs of a generalist fungal pathogen (Metschnikowia bicuspidata) capable of infecting a number of host species. Costs were quantified as reductions in host lifespan, total reproduction, and mean clutch size as a function of pathogen exposure (resistance cost) or infection (infection cost). We provide empirical support for infection costs and modest support for resistance costs for five Daphnia host species. Specifically, only one host species examined incurred a significant cost of resistance. This species was the least susceptible to infection, suggesting the possibility that host susceptibility to infection is associated with the detectability and size of resistance cost. Host age at the time of pathogen exposure did not influence the magnitude of resistance or infection cost. Lastly, resistant hosts had fitness values intermediate between unexposed control hosts and infected hosts. Although not statistically significant, this could suggest that pathogen exposure does come at some marginal cost. Taken together, our findings suggest that infection is costly, resistance costs may simply be difficult to detect, and the magnitude of resistance cost may vary among host species as a result of host life history or susceptibility.

Nitrate enrichment alters a Daphnia-microparasite interaction through multiple pathways

Nutrient pollution has the potential to alter many ecological interactions, including host-parasite relationships. One of the largest sources of nutrient pollution comes from anthropogenic alteration of the nitrogen (N) cycle, specifically the increased rate of nitrate (NO3-N) deposition to aquatic environments, potentially altering host-parasite relationships. This study aimed to assess the mechanisms through which nitrate may impact host-pathogen relationships using a fungal pathogen (Metschnikowia bicuspidata) parasitic to crustacean zooplankton (Daphnia dentifera) as a tractable model system. First, the influence of nitrate on host population dynamics was assessed along a gradient of nitrate concentrations. Nitrate decreased host population size and increased infection prevalence. Second, the influence of nitrate on host reproduction, mortality, and infection intensity was assessed at the individual host level by examining the relationship between pathogen dose and infection prevalence at ambient (0.4 mg NO3-N*L(-1)) and intermediate (12 mg NO3-N*L(-1)) levels of nitrate. Host fecundity and infection intensity both decreased with increasing pathogen dose, but increased nitrate levels corresponded to greater infection intensities. Nitrate had no effect on host growth rate, suggesting that hosts do not alter feeding behavior in nitrate-treated media compared with ambient conditions. This study suggests that nutrient enrichment may enhance disease through increased transmission and infection intensity, but that high levels of nitrate may result in smaller epidemics through reduced transmission caused by smaller population sizes and increased pathogen mortality.

Chemical attraction of Dermacentor variabilis ticks parasitic to Peromyscus leucopus based on host body mass and sex

Macroparasites are commonly aggregated on a small subset of a host population. Previous explanations for this aggregation relate to differences in immunocompetence or the degree to which hosts encounter parasites. We propose active tick host choice through chemical attraction as a potential mechanism leading to aggregated tick burdens. We test this hypothesis using a Y-maze olfactometer, comparing chemical attraction responses of larval and nymphal Dermacentor variabilis ticks parasitic to the white-footed mouse, Peromyscus leucopus, as a function of host sex and host body mass. We hypothesized that larger hosts and male hosts would be most attractive to searching ticks, as these hosts commonly have higher tick burdens in the field. Chemical attraction trials were run in the presence and absence of a known tick attractant, host-produced carbon dioxide (CO2). Male hosts and larger hosts were preferred by nymphal D. variabilis in the presence and absence of CO2, whereas larvae had no detectable host preference. The current study suggests that host-produced chemical cues may promote aggregated tick burdens among hosts of a single species based on host body mass and sex.

Enhanced Signal-to-Background Ratios in Voltammetric Measurements Made at Diamond Thin-Film Electrochemical Interfaces

Large signal-to-background (S/B) ratios for the Fe(CN)(6)(3)(-)(/4)(-) and IrCl(6)(2)(-)(/3)(-) redox couples in KCl have been observed in cyclic voltammetric measurements made at a conductive diamond thin-film electrode without any conventional surface pretreatment. The S/B ratios were a factor of ∼16 and 8 larger at diamond than at freshly polished glassy carbon (GC) for Fe(CN)(6)(3)(-)(/4)(-) and IrCl(6)(2)(-)(/3)(-), respectively. The polycrystalline diamond film, grown on a p-Si(100) substrate, possessed significant cubic {100} faceting, as evidenced by AFM images, and was of high quality, as indicated by Raman spectroscopy. The high degree of electrochemical activity without surface pretreatment, the enhanced S/B ratios, and the excellent response stability demonstrate that diamond might be an attractive new electrode material for electroanalysis.

Anesthesia in intraoperative radiotherapy patients

Intraoperative radiotherapy (IOR) is a relatively new modality for the treatment of carcinoma. This modality necessitates a multidisciplinary approach among the surgeon, anesthesiologist, radiotherapist, pathologist, and other members of the surgical support team. In addition to appropriate IOR and surgical techniques, the role of the anesthesiologist is crucial in determining patient outcome. Specifically, the degree of preoperative preparation has a direct correlation with a successful postoperative course. Patients considered for surgery are grouped in terms of: (1) primary tumor with no metastasis and/or unresectable loco-regional disease; (2) clinical and investigational evidence of tumor with no proven malignancy; and (3) those with known metastasis but in otherwise good general condition.The primary surgical goal is to localize the tumor, obtain a frozen-section biopsy, and evaluate for resectability at the same time as the radiotherapist evaluates whether IOR is indicated. Thus many facets come together to make the IOR procedures feasible and safe. The 148 patients treated at Howard University Hospital, uneventfully, should serve to justify intraoperative radiotherapy as both a practical and safe tool in the treatment of malignancy.