The Certainty of Uncertainty: Potential Sources of Bias and Imprecision in Disease Ecology Studies

The prevalence of rodent-borne zoonotic pathogens in the South Gobi desert region of Mongolia

The alpine ecosystems and communities of central Asia are currently undergoing large-scale ecological and socio-ecological changes likely to affect wildlife-livestock-human disease interactions and zoonosis transmission risk. However, relatively little is known about the prevalence of pathogens in this region. Between 2012 and 2015 we screened 142 rodents in Mongolia's Gobi desert for exposure to important zoonotic and livestock pathogens. Rodent seroprevalence to Leptospira spp. was >1/3 of tested animals, Toxoplasma gondii and Coxiella burnetii approximately 1/8 animals, and the hantaviruses being between 1/20 (Puumala-like hantavirus) and <1/100 (Seoul-like hantavirus). Gerbils trapped inside local dwellings were one of the species seropositive to Puumala-like hantavirus, suggesting a potential zoonotic transmission pathway. Seventeen genera of zoonotic bacteria were also detected in the faeces and ticks collected from these rodents, with one tick testing positive to Yersinia. Our study helps provide baseline patterns of disease prevalence needed to infer potential transmission between source and target populations in this region, and to help shift the focus of epidemiological research towards understanding disease transmission among species and proactive disease mitigation strategies within a broader One Health framework.

When Vibrios Take Flight: A Meta-Analysis of Pathogenic Vibrio Species in Wild and Domestic Birds

Of the over 100 species in the genus Vibrio, approximately twelve are associated with clinical disease, such as cholera and vibriosis. Crucially, eleven of those twelve, including Vibrio cholerae and Vibrio vulnificus, have been isolated from birds. Since 1965, pathogenic Vibrio species have been consistently isolated from aquatic and ground-foraging bird species, which has implications for public health, as well as the One Health paradigm defined as an ecology-inspired, integrative framework for the study of health and disease, inclusive of environmental, human, and animal health. In this meta-analysis, we identified 76 studies from the primary literature which report on or examine birds as hosts for pathogenic Vibrio species. We found that the burden of disease in birds was most commonly associated with V. cholerae, followed by V. metschnikovii and V. parahaemolyticus. Meta-analysis wide prevalence of our Vibrio pathogens varied from 19% for V. parahaemolyticus to 1% for V. mimicus. Wild and domestic birds were both affected, which may have implications for conservation, as well as agriculturally associated avian species. As pathogenic Vibrios become more abundant throughout the world as a result of warming estuaries and oceans, susceptible avian species should be continually monitored as potential reservoirs for these pathogens.

Enhancing convolutional neural network predictions of electrocardiograms with left ventricular dysfunction using a novel sub-waveform representation

Background

Electrocardiogram (ECG) deep learning (DL) has promise to improve the outcomes of patients with cardiovascular abnormalities. In ECG DL, researchers often use convolutional neural networks (CNNs) and traditionally use the full duration of raw ECG waveforms that create redundancies in feature learning and result in inaccurate predictions with large uncertainties.

Objective

For enhancing these predictions, we introduced a sub-waveform representation that leverages the rhythmic pattern of ECG waveforms (data-centric approach) rather than changing the CNN architecture (model-centric approach).

Results

We applied the proposed representation to a population with 92,446 patients to identify left ventricular dysfunction. We found that the sub-waveform representation increases the performance metrics compared to the full-waveform representation. We observed a 2% increase for area under the receiver operating characteristic curve and 10% increase for area under the precision-recall curve. We also carefully examined three reliability components of explainability, interpretability, and fairness. We provided an explanation for enhancements obtained by heartbeat alignment mechanism. By developing a new scoring system, we interpreted the clinical relevance of ECG features and showed that sub-waveform representation further pushes the scores towards clinical predictions. Finally, we showed that the new representation significantly reduces prediction uncertainties within subgroups that contributes to individual fairness.

Conclusion

We expect that this added control over the granularity of ECG data will improve the DL modeling for new artificial intelligence technologies in the cardiovascular space.

A Review of Non-Invasive Sampling in Wildlife Disease and Health Research: What’s New?

Simple Summary

The interest in wildlife research has increased in the last decades as more scientists work within a One Health framework that regards human, livestock and wildlife health as connected entities. To minimise the impact of research on wildlife, collecting samples with as little disturbance of the animals as possible is important. In our review, we assess the use of so-called non-invasive sampling and summarise which samples can be used successfully when carrying out research on wildlife diseases and health status. Our results show that interest in minimally invasive sampling has steadily increased since the 2010s. Topics able to employ these methods include disease research, but also stress and other hormone assessments, pollution studies, and dietary studies. At the moment, such methods are mainly used to collect samples from land mammals, however, they can also be used in a wide range of other animals. Ever more capable analytical methods will allow for an even wider use of such “animal-friendly” sampling methods.

Abstract

In the last decades, wildlife diseases and the health status of animal populations have gained increasing attention from the scientific community as part of a One Health framework. Furthermore, the need for non-invasive sampling methods with a minimal impact on wildlife has become paramount in complying with modern ethical standards and regulations, and to collect high-quality and unbiased data. We analysed the publication trends on non-invasive sampling in wildlife health and disease research and offer a comprehensive review on the different samples that can be collected non-invasively. We retrieved 272 articles spanning from 1998 to 2021, with a rapid increase in number from 2010. Thirty-nine percent of the papers were focussed on diseases, 58% on other health-related topics, and 3% on both. Stress and other physiological parameters were the most addressed research topics, followed by viruses, helminths, and bacterial infections. Terrestrial mammals accounted for 75% of all publications, and faeces were the most widely used sample. Our review of the sampling materials and collection methods highlights that, although the use of some types of samples for specific applications is now consolidated, others are perhaps still underutilised and new technologies may offer future opportunities for an even wider use of non-invasively collected samples.

The energetic costs of sub-lethal helminth parasites in mammals: a meta-analysis

Parasites, by definition, have a negative effect on their host. However, in wild mammal health and conservation research, sub-lethal infections are commonly assumed to have negligible health effects unless parasites are present in overwhelming numbers. Here, we propose a definition for host health in mammals that includes sub-lethal effects of parasites on the host's capacity to adapt to the environment and maintain homeostasis. We synthesized the growing number of studies on helminth parasites in mammals to assess evidence for the relative magnitude of sub-lethal effects of infection across mammal taxa based on this expanded definition. Specifically, we develop and apply a framework for organizing disparate metrics of parasite effects on host health and body condition according to their impact on an animal's energetic condition, defined as the energetic burden of pathogens on host physiological and behavioural functions that relate directly to fitness. Applying this framework within a global meta-analysis of helminth parasites in wild, laboratory and domestic mammal hosts produced 142 peer-reviewed studies documenting 599 infection-condition effects. Analysing these data within a multiple working hypotheses framework allowed us to evaluate the relative weighted contribution of methodological (study design, sampling protocol, parasite quantification methods) and biological (phylogenetic relationships and host/parasite life history) moderators to variation in the magnitude of health effects. We found consistently strong negative effects of infection on host energetic condition across taxonomic groups, with unusually low heterogeneity in effect sizes when compared with other ecological meta-analyses. Observed effect size was significantly lower within cross-sectional studies (i.e. observational studies that investigated a sub-set of a population at a single point in time), the most prevalent methodology. Furthermore, opportunistic sampling led to a weaker negative effect compared to proactive sampling. In the model of host taxonomic group, the effect of infection on energetic condition in carnivores was not significant. However, when sampling method was included, it explained substantial inter-study variance; proactive sampling showing a strongly significant negative effect while opportunistic sampling detected only a weak, non-significant effect. This may partly underlie previous assumptions that sub-lethal parasites do not have significant effects on host health. We recommend future studies adopt energetic condition as the framework for assessing parasite effects on wildlife health and provide guidelines for the selection of research protocols, health proxies, and relating infection to fitness.

Great future or greedy venture: Precision medicine needs philosophy

INTRODUCTION

Over the past decade, we have witnessed the initiation and implementation of precision medicine (PM), a discipline that promises to individualize and personalize medical management and treatment, rendering them ultimately more precise and effective. Despite of the continuing advances and numerous clinical applications, the potential of PM remains highly controversial, sparking heated debates about its future.

METHOD

The present article reviews the philosophical issues and practical challenges that are critical to the feasibility and implementation of PM.

OUTCOME

The explanation and argument about the relations between PM and computability, uncertainty as well as complexity, show that key foundational assumptions of PM might not be fully validated.

CONCLUSION

The present analysis suggests that our current understanding of PM is probably oversimplified and too superficial. More efforts are needed to realize the hope that PM has elicited, rather than make the term just as a hype.

Data Proliferation, Reconciliation, and Synthesis in Viral Ecology

The fields of viral ecology and evolution are rapidly expanding, motivated in part by concerns around emerging zoonoses. One consequence is the proliferation of host–virus association data, which underpin viral macroecology and zoonotic risk prediction but remain fragmented across numerous data portals. In the present article, we propose that synthesis of host–virus data is a central challenge to characterize the global virome and develop foundational theory in viral ecology. To illustrate this, we build an open database of mammal host–virus associations that reconciles four published data sets. We show that this offers a substantially richer view of the known virome than any individual source data set but also that databases such as these risk becoming out of date as viral discovery accelerates. We argue for a shift in practice toward the development, incremental updating, and use of synthetic data sets in viral ecology, to improve replicability and facilitate work to predict the structure and dynamics of the global virome.

Global prevalence of Mesocestoides infections in animals - A systematic review and meta-analysis

Mesocestoides spp. are zoonotic cestodes found as adults in carnivorous domestic and wild definitive hosts and as metacestodes in several taxa of intermediate hosts. Although several regional studies record its occurrence in different host populations, the global prevalence and patterns of occurrence of Mesocestoides spp. are not fully understood. The objective of this study was to conduct a systematic review and meta-analysis of published literature to estimate the global prevalence of Mesocestoides spp. in major definitive and intermediate host taxa. Records published in English were collected from NCBI PubMed, Science Direct, Web of Science and Google Scholar databases, with 364 papers being included in the meta-analysis. The overall pooled prevalence estimates show that 21.72 % (95 % CI: 18.49-25.14) of terrestrial carnivore definitive hosts and 7.09 % (95 % CI: 5.79-8.51) of intermediate hosts are infected. Among definitive hosts, opossums and foxes were most commonly infected with pooled global prevalence of 48.16 % (95 % CI: 14.62 - 82.69) and 35.97 % (295 % CI: 9.54 - 42.66) respectively. Pooled global prevalence in domestic dogs and cats were 7.97 % (95 % CI: 5.67 - 10.63) and 8.32 % (95 % CI: 3.78 - 14.41) respectively. Among intermediate hosts, birds and snakes were most commonly infected with pooled global prevalence of 16.19 % (95 %CI: 5.9 - 30.31) and 15.74 % (95 % CI: 10.59 - 21.69) respectively. Our analysis demonstrates that prevalence of Mesocestoides spp. is variable across the world. The sylvatic cycle in wild hosts is likely to be more important than the domestic cycle for the maintenance of Mesocestoides spp. globally. Currently available genetic data at the mitochondrial COI locus was also phylogenetically analyzed. The genetic data supports the taxonomic distinctiveness of only a few of the numerous morphologically described Mesocestoides spp.

Angiostrongylus vasorum in foxes (Vulpes vulpes) and wolves (Canis lupus italicus) from Abruzzo region, Italy

In Europe wildlife animals such as the red fox (Vulpes vulpes) are considered the main reservoir for Angiostrongylus vasorum as well as a potential threat for domestic dog infection. Though this parasite is endemic in fox populations, data on A. vasorum infection in wolves (Canis lupus italicus) are still scant, having only recently been described in Northwestern Spain, in Italy, in Croatia and in Slovakia. Based on the rising number of cases of canine lungworm infection in Central Italy (Abruzzo region), the aim of the present study was to investigate the infection by A. vasorum in fox and wolf populations sharing the same geographical area of dogs. From October 2008 to November 2019, A. vasorum specimens were collected, through routine post-mortem examination, from 56 carcasses (44 foxes and 12 wolves). Adult parasites were searched for in the right side of the heart and in pulmonary artery of all carcasses. First stage of larvae (L1) was searched in faeces using the Baermann technique and in lungs by tissue impressions. Overall, 230 adult specimens were collected and identified on a morphological basis. To confirm the morphological identification, 4 adult specimens (n = 3 from fox, n = 1 from wolf) were molecularly identified as A. vasorum by amplification of partial fragment of nuclear 18S rRNA (~1700 bp) genes. The anatomo-pathological and parasitological examinations indicated the presence of A. vasorum in 33 foxes (75%) and in 8 wolves (66.7%). The level of prevalence of infested wolves was higher than the previous one reported in other European countries. Interestingly, the prevalence of infection in foxes herein recorded was higher than that described in dogs (8.9%) living in the same geographical area. This result may confirm the hypothesis that the spread of canine angiostrongylosis is linked to fox populations infection.

Systematic Review of Wolbachia Symbiont Detection in Mosquitoes: An Entangled Topic about Methodological Power and True Symbiosis

Wolbachia is an endosymbiotic bacterium that naturally infects several arthropods and nematode species. Wolbachia gained particular attention due to its impact on their host fitness and the capacity of specific Wolbachia strains in reducing pathogen vector and agricultural pest populations and pathogens transmission. Despite the success of mosquito/pathogen control programs using Wolbachia-infected mosquito release, little is known about the abundance and distribution of Wolbachia in most mosquito species, a crucial knowledge for planning and deployment of mosquito control programs and that can further improve our basic biology understanding of Wolbachia and host relationships. In this systematic review, Wolbachia was detected in only 30% of the mosquito species investigated. Fourteen percent of the species were considered positive by some studies and negative by others in different geographical regions, suggesting a variable infection rate and/or limitations of the Wolbachia detection methods employed. Eighty-three percent of the studies screened Wolbachia with only one technique. Our findings highlight that the assessment of Wolbachia using a single approach limited the inference of true Wolbachia infection in most of the studied species and that researchers should carefully choose complementary methodologies and consider different Wolbachia-mosquito population dynamics that may be a source of bias to ascertain the correct infectious status of the host species.

How sure are you? A web-based application to confront imperfect detection of respiratory pathogens in bighorn sheep

The relationships between host-pathogen population dynamics in wildlife are poorly understood. An impediment to progress in understanding these relationships is imperfect detection of diagnostic tests used to detect pathogens. If ignored, imperfect detection precludes accurate assessment of pathogen presence and prevalence, foundational parameters for deciphering host-pathogen dynamics and disease etiology. Respiratory disease in bighorn sheep (Ovis canadensis) is a significant impediment to their conservation and restoration, and effective management requires a better understanding of the structure of the pathogen communities. Our primary objective was to develop an easy-to-use and accessible web-based Shiny application that estimates the probability (with associated uncertainty) that a respiratory pathogen is present in a herd and its prevalence given imperfect detection. Our application combines the best-available information on the probabilities of detection for various respiratory pathogen diagnostic protocols with a hierarchical Bayesian model of pathogen prevalence. We demonstrated this application using four examples of diagnostic tests from three herds of bighorn sheep in Montana. For instance, one population with no detections of Mycoplasma ovipneumoniae (PCR assay) still had an 6% probability of the pathogen being present in the herd. Similarly, the apparent prevalence (0.32) of M. ovipneumoniae in another herd was a substantial underestimate of estimated true prevalence (0.46: 95% CI = [0.25, 0.71]). The negative bias of naïve prevalence increased as the probability of detection of testing protocols worsened such that the apparent prevalence of Mannheimia haemolytica (culture assay) in a herd (0.24) was less than one third that of estimated true prevalence (0.78: 95% CI = [0.43, 0.99]). We found a small difference in the estimates of the probability that Mannheimia spp. (culture assay) was present in one herd between the binomial sampling approach (0.24) and the hypergeometric approach (0.22). Ignoring the implications of imperfect detection and sampling variation for assessing pathogen communities in bighorn sheep can result in spurious inference on pathogen presence and prevalence, and potentially poorly informed management decisions. Our Shiny application makes the rigorous assessment of pathogen presence, prevalence and uncertainty straightforward, and we suggest it should be incorporated into a new paradigm of disease monitoring.

Towards an understanding of the avian virome

The last two decades have seen the rise of viromics, the study of viral communities through the detection and characterization of virus genome sequences. Here we systematically review and summarize the scope and limitations of our current understanding of avian viromes, in both domesticated and wild-bird populations. We compare this viromic work to the broader literature on avian prokaryotic microbiomes, and highlight the growing importance of structured sampling and experimental design for testing explanatory hypotheses. We provide a number of recommendations for sample collection and preliminary data analysis to guide the development of avian viromics. Avian viromes have the potential to inform disease surveillance in poultry and improve our understanding of the risk of zoonotic viruses to human health.

The Fine-Scale Landscape of Immunity and Parasitism in a Wild Ungulate Population

Spatial heterogeneity in susceptibility and exposure to parasites is a common source of confounding variation in disease ecology studies. However, it is not known whether spatial autocorrelation acts on immunity at small scales, within wild animal populations, and whether this predicts spatial patterns in infection. Here we used a well-mixed wild population of individually recognized red deer (Cervus elaphus) inhabiting a heterogeneous landscape to investigate fine-scale spatial patterns of immunity and parasitism. We noninvasively collected 842 fecal samples from 141 females with known ranging behavior over 2 years. We quantified total and helminth-specific mucosal antibodies and counted propagules of three gastrointestinal helminth taxa. These data were analyzed with linear mixed models using the Integrated Nested Laplace Approximation, using a Stochastic Partial Differentiation Equation approach to control for and quantify spatial autocorrelation. We also investigated whether spatial patterns of immunity and parasitism changed seasonally. We discovered substantial spatial heterogeneity in general and helminth-specific antibody levels and parasitism with two helminth taxa, all of which exhibited contrasting seasonal variation in their spatial patterns. Notably, Fasciola hepatica intensity appeared to be strongly influenced by the presence of wet grazing areas, and antibody hotspots did not correlate with distributions of any parasites. Our results suggest that spatial heterogeneity may be an important factor affecting immunity and parasitism in a wide range of study systems. We discuss these findings with regards to the design of sampling regimes and public health interventions, and suggest that disease ecology studies investigate spatial heterogeneity more regularly to enhance their results, even when examining small geographic areas.

A hierarchical Bayesian approach for handling missing classification data

Ecologists use classifications of individuals in categories to understand composition of populations and communities. These categories might be defined by demographics, functional traits, or species. Assignment of categories is often imperfect, but frequently treated as observations without error. When individuals are observed but not classified, these "partial" observations must be modified to include the missing data mechanism to avoid spurious inference.We developed two hierarchical Bayesian models to overcome the assumption of perfect assignment to mutually exclusive categories in the multinomial distribution of categorical counts, when classifications are missing. These models incorporate auxiliary information to adjust the posterior distributions of the proportions of membership in categories. In one model, we use an empirical Bayes approach, where a subset of data from one year serves as a prior for the missing data the next. In the other approach, we use a small random sample of data within a year to inform the distribution of the missing data.We performed a simulation to show the bias that occurs when partial observations were ignored and demonstrated the altered inference for the estimation of demographic ratios. We applied our models to demographic classifications of elk (Cervus elaphus nelsoni) to demonstrate improved inference for the proportions of sex and stage classes.We developed multiple modeling approaches using a generalizable nested multinomial structure to account for partially observed data that were missing not at random for classification counts. Accounting for classification uncertainty is important to accurately understand the composition of populations and communities in ecological studies.

Robustness of Eco-Epidemiological Capture-Recapture Parameter Estimates to Variation in Infection State Uncertainty

Estimating eco-epidemiological parameters in free-ranging populations can be challenging. As known individuals may be undetected during a field session, or their health status uncertain, the collected data are typically "imperfect". Multi-event capture-mark-recapture (MECMR) models constitute a substantial methodological advance by accounting for such imperfect data. In these models, animals can be "undetected" or "detected" at each time step. Detected animals can be assigned an infection state, such as "susceptible" (S), "infected" (I), or "recovered" (R), or an "unknown" (U) state, when for instance no biological sample could be collected. There may be heterogeneity in the assignment of infection states, depending on the manifestation of the disease in the host or the diagnostic method. For example, if obtaining the samples needed to prove viral infection in a detected animal is difficult, this can result in a low chance of assigning the I state. Currently, it is unknown how much uncertainty MECMR models can tolerate to provide reliable estimates of eco-epidemiological parameters and whether these parameters are sensitive to heterogeneity in the assignment of infection states. We used simulations to assess how estimates of the survival probability of individuals in different infection states and the probabilities of infection and recovery responded to (1) increasing infection state uncertainty (i.e., the proportion of U) from 20 to 90%, and (2) heterogeneity in the probability of assigning infection states. We simulated data, mimicking a highly virulent disease, and used SIR-MECMR models to quantify bias and precision. For most parameter estimates, bias increased and precision decreased gradually with state uncertainty. The probabilities of survival of I and R individuals and of detection of R individuals were very robust to increasing state uncertainty. In contrast, the probabilities of survival and detection of S individuals, and the infection and recovery probabilities showed high biases and low precisions when state uncertainty was >50%, particularly when the assignment of the S state was reduced. Considering this specific disease scenario, SIR-MECMR models are globally robust to state uncertainty and heterogeneity in state assignment, but the previously mentioned parameter estimates should be carefully interpreted if the proportion of U is high.