Dynamic and integrative approaches to understanding pathogen spillover

Modeling spillover dynamics: understanding emerging pathogens of public health concern

The emergence of infectious diseases with pandemic potential is a major public health threat worldwide. The World Health Organization reports that about 60% of emerging infectious diseases are zoonoses, originating from spillover events. Although the mechanisms behind spillover events remain unclear, mathematical modeling offers a way to understand the intricate interactions among pathogens, wildlife, humans, and their shared environment. Aiming at gaining insights into the dynamics of spillover events and the outcome of an eventual disease outbreak in a population, we propose a continuous time stochastic modeling framework. This framework links the dynamics of animal reservoirs and human hosts to simulate cross-species disease transmission. We conduct a thorough analysis of the model followed by numerical experiments that explore various spillover scenarios. The results suggest that although most epidemic outbreaks caused by novel zoonotic pathogens do not persist in the human population, the rising number of spillover events can avoid long-lasting extinction and lead to unexpected large outbreaks. Hence, global efforts to reduce the impacts of emerging diseases should not only address post-emergence outbreak control but also need to prevent pandemics before they are established.

Host Barriers Limit Viral Spread in a Spillover Host: A Study of Deformed Wing Virus in the Bumblebee Bombus terrestris

The transmission of pathogens from reservoir to recipient host species, termed pathogen spillover, can profoundly impact plant, animal, and public health. However, why some pathogens lead to disease emergence in a novel species while others fail to establish or do not elicit disease is often poorly understood. There is strong evidence that deformed wing virus (DWV), an (+)ssRNA virus, spills over from its reservoir host, the honeybee Apis mellifera, into the bumblebee Bombus terrestris. However, the low impact of DWV on B. terrestris in laboratory experiments suggests host barriers to virus spread in this recipient host. To investigate potential host barriers, we followed the spread of DWV genotype B (DWV-B) through a host's body using RT-PCR after experimental transmission to bumblebees in comparison to honeybees. Inoculation was per os, mimicking food-borne transmission, or by injection into the bee's haemocoel, mimicking vector-based transmission. In honeybees, DWV-B was present in both honeybee faeces and haemolymph within 3 days of inoculation per os or by injection. In contrast, DWV-B was not detected in B. terrestris haemolymph after inoculation per os, suggesting a gut barrier that hinders DWV-B's spread through the body of a B. terrestris. DWV-B was, however, detected in B. terrestris faeces after injection and feeding, albeit at a lower abundance than that observed for A. mellifera, suggesting that B. terrestris sheds less DWV-B than A. mellifera in faeces when infected. Barriers to viral spread in B. terrestris following oral infection may limit DWV's impact on this spillover host and reduce its contribution to the community epidemiology of DWV.

Robust evidence for bats as reservoir hosts is lacking in most African virus studies: a review and call to optimize sampling and conserve bats

Africa experiences frequent emerging disease outbreaks among humans, with bats often proposed as zoonotic pathogen hosts. We comprehensively reviewed virus-bat findings from papers published between 1978 and 2020 to evaluate the evidence that African bats are reservoir and/or bridging hosts for viruses that cause human disease. We present data from 162 papers (of 1322) with original findings on (1) numbers and species of bats sampled across bat families and the continent, (2) how bats were selected for study inclusion, (3) if bats were terminally sampled, (4) what types of ecological data, if any, were recorded and (5) which viruses were detected and with what methodology. We propose a scheme for evaluating presumed virus-host relationships by evidence type and quality, using the contrasting available evidence for Orthoebolavirus versus Orthomarburgvirus as an example. We review the wording in abstracts and discussions of all 162 papers, identifying key framing terms, how these refer to findings, and how they might contribute to people's beliefs about bats. We discuss the impact of scientific research communication on public perception and emphasize the need for strategies that minimize human-bat conflict and support bat conservation. Finally, we make recommendations for best practices that will improve virological study metadata.

The final frontier: ecological and evolutionary dynamics of a global parasite invasion

Studying rapid biological changes accompanying the introduction of alien organisms into native ecosystems can provide insights into fundamental ecological and evolutionary theory. While powerful, this quasi-experimental approach is difficult to implement because the timing of invasions and their consequences are hard to predict, meaning that baseline pre-invasion data are often missing. Exceptionally, the eventual arrival of Varroa destructor (hereafter Varroa) in Australia has been predicted for decades. Varroa is a major driver of honeybee declines worldwide, particularly as vectors of diverse RNA viruses. The detection of Varroa in 2022 at over a hundred sites poses a risk of further spread across the continent. At the same time, careful study of Varroa's spread, if it does become established, can provide a wealth of information that can fill knowledge gaps about its effects worldwide. This includes how Varroa affects honeybee populations and pollination. Even more generally, Varroa invasion can serve as a model for evolution, virology and ecological interactions between the parasite, the host and other organisms.

Viral spillover risk increases with climate change in High Arctic lake sediments

The host spectrum of viruses is quite diverse, as they can sustainedly infect a few species to several phyla. When confronted with a new host, a virus may even infect it and transmit sustainably in this new host, a process called ‘viral spillover’. However, the risk of such events is difficult to quantify. As climate change is rapidly transforming environments, it is becoming critical to quantify the potential for spillovers. To address this issue, we resorted to a metagenomics approach and focused on two environments, soil and lake sediments from Lake Hazen, the largest High Arctic freshwater lake in the world. We used DNA and RNA sequencing to reconstruct the lake’s virosphere in both its sediments and soils, as well as its range of eukaryotic hosts. We then estimated the spillover risk by measuring the congruence between the viral and the eukaryotic host phylogenetic trees, and show that spillover risk increases with runoff from glacier melt, a proxy for climate change. Should climate change also shift species range of potential viral vectors and reservoirs northwards, the High Arctic could become fertile ground for emerging pandemics.

Synthesizing the connections between environmental disturbances and zoonotic spillover

Zoonotic spillover is a phenomenon characterized by the transfer of pathogens between different animal species. Most human emerging infectious diseases originate from non-human animals, and human-related environmental disturbances are the driving forces of the emergence of new human pathogens. Synthesizing the sequence of basic events involved in the emergence of new human pathogens is important for guiding the understanding, identification, and description of key aspects of human activities that can be changed to prevent new outbreaks, epidemics, and pandemics. This review synthesizes the connections between environmental disturbances and increased risk of spillover events based on the One Health perspective. Anthropogenic disturbances in the environment (e.g., deforestation, habitat fragmentation, biodiversity loss, wildlife exploitation) lead to changes in ecological niches, reduction of the dilution effect, increased contact between humans and other animals, changes in the incidence and load of pathogens in animal populations, and alterations in the abiotic factors of landscapes. These phenomena can increase the risk of spillover events and, potentially, facilitate new infectious disease outbreaks. Using Brazil as a study model, this review brings a discussion concerning anthropogenic activities in the Amazon region and their potential impacts on spillover risk and spread of emerging diseases in this region.

THE IMPLEMENTATION GAP IN EMERGING DISEASE RISK MANAGEMENT IN THE WILDLIFE TRADE

The wildlife trade has been characterized as one of the biggest risk factors in the emergence of new infectious diseases. In the shadow of COVID-19, there is growing political and scientific urgency to manage this risk. Existing studies and experiences make it clear that something must be done but are less clear on how to get it done. It is a quite different task to accumulate evidence on the presence of pathogens, their locations in the supply chain, and their spillover to new hosts than to identify effective ways to prevent and mitigate emerging disease under real-world conditions. This study sought peer-reviewed evidence on the effectiveness, acceptability, feasibility, and sustainability of risk reduction interventions for zoonotic and nonzoonotic disease emergence in the wildlife trade. An environmental scan triangulated information from a scoping review following a Preferred Reporting Items for Systematic Reviews and Meta-analysis extension for scoping review protocol, two narrative literature reviews, and key informant interviews of 26 international wildlife health experts. Existing literature has been inattentive to program implementation or evaluation studies. There was insufficient evidence to identify effective and sustainable risk management actions. Studies on the effects of social, epidemiologic, and ecologic context on intervention success was lacking, as was research using a complex systems perspective. The lack of systematic program evaluations or implementation studies leaves decision makers with insufficient evidence to select interventions likely to be acceptable, effective, and sustainable within and across the disparate context of the wildlife trade. This necessitates adaptive risk management and innovations in program implementation and evaluation to ensure evidence-based risk management.

Linking rattiness, geography and environmental degradation to spillover Leptospira infections in marginalised urban settings: An eco-epidemiological community-based cohort study in Brazil

Background

Zoonotic spillover from animal reservoirs is responsible for a significant global public health burden, but the processes that promote spillover events are poorly understood in complex urban settings. Endemic transmission of Leptospira, the agent of leptospirosis, in marginalised urban communities occurs through human exposure to an environment contaminated by bacteria shed in the urine of the rat reservoir. However, it is unclear to what extent transmission is driven by variation in the distribution of rats or by the dispersal of bacteria in rainwater runoff and overflow from open sewer systems.

Methods

We conducted an eco-epidemiological study in a high-risk community in Salvador, Brazil, by prospectively following a cohort of 1401 residents to ascertain serological evidence for leptospiral infections. A concurrent rat ecology study was used to collect information on the fine-scale spatial distribution of 'rattiness', our proxy for rat abundance and exposure of interest. We developed and applied a novel geostatistical framework for joint spatial modelling of multiple indices of disease reservoir abundance and human infection risk.

Results

The estimated infection rate was 51.4 (95%CI 40.4, 64.2) infections per 1000 follow-up events. Infection risk increased with age until 30 years of age and was associated with male gender. Rattiness was positively associated with infection risk for residents across the entire study area, but this effect was stronger in higher elevation areas (OR 3.27 95% CI 1.68, 19.07) than in lower elevation areas (OR 1.14 95% CI 1.05, 1.53).

Conclusions

These findings suggest that, while frequent flooding events may disperse bacteria in regions of low elevation, environmental risk in higher elevation areas is more localised and directly driven by the distribution of local rat populations. The modelling framework developed may have broad applications in delineating complex animal-environment-human interactions during zoonotic spillover and identifying opportunities for public health intervention.

Funding

This work was supported by the Oswaldo Cruz Foundation and Secretariat of Health Surveillance, Brazilian Ministry of Health, the National Institutes of Health of the United States (grant numbers F31 AI114245, R01 AI052473, U01 AI088752, R01 TW009504 and R25 TW009338); the Wellcome Trust (102330/Z/13/Z), and by the Fundação de Amparo à Pesquisa do Estado da Bahia (FAPESB/JCB0020/2016). MTE was supported by a Medical Research UK doctorate studentship. FBS participated in this study under a FAPESB doctorate scholarship.

A Data-driven Horizon Scan of Bacterial Pathogens at the Wildlife-livestock Interface

Many livestock diseases rely on wildlife for the transmission or maintenance of the pathogen, and the wildlife-livestock interface represents a potential site of disease emergence for novel pathogens in livestock. Predicting which pathogen species are most likely to emerge in the future is an important challenge for infectious disease surveillance and intelligence. We used a machine learning approach to conduct a data-driven horizon scan of bacterial associations at the wildlife-livestock interface for cows, sheep, and pigs. Our model identified and ranked from 76 to 189 potential novel bacterial species that might associate with each livestock species. Wildlife reservoirs of known and novel bacteria were shared among all three species, suggesting that targeting surveillance and/or control efforts towards these reservoirs could contribute disproportionately to reducing spillover risk to livestock. By predicting pathogen-host associations at the wildlife-livestock interface, we demonstrate one way to plan for and prevent disease emergence in livestock.

Assessing the risk of human-to-wildlife pathogen transmission for conservation and public health

The SARS‐CoV‐2 pandemic has led to increased concern over transmission of pathogens from humans to animals, and its potential to threaten conservation and public health. To assess this threat, we reviewed published evidence of human‐to‐wildlife transmission events, with a focus on how such events could threaten animal and human health. We identified 97 verified examples, involving a wide range of pathogens; however, reported hosts were mostly non‐human primates or large, long‐lived captive animals. Relatively few documented examples resulted in morbidity and mortality, and very few led to maintenance of a human pathogen in a new reservoir or subsequent “secondary spillover” back into humans. We discuss limitations in the literature surrounding these phenomena, including strong evidence of sampling bias towards non‐human primates and human‐proximate mammals and the possibility of systematic bias against reporting human parasites in wildlife, both of which limit our ability to assess the risk of human‐to‐wildlife pathogen transmission. We outline how researchers can collect experimental and observational evidence that will expand our capacity for risk assessment for human‐to‐wildlife pathogen transmission.

Experimental cross species transmission of a major viral pathogen in bees is predominantly from honeybees to bumblebees

Cross-species transmission of a pathogen from a reservoir to a recipient host species, spillover, can have major impacts on biodiversity, domestic species and human health. Deformed wing virus (DWV) is a panzootic RNA virus in honeybees that is causal in their elevated colony losses, and several correlative field studies have suggested spillover of DWV from managed honeybees to wild bee species such as bumblebees. Yet unequivocal demonstration of DWV spillover is lacking, while spillback, the transmission of DWV from a recipient back to the reservoir host, is rarely considered. Here, we show in fully crossed laboratory experiments that the transmission of DWV (genotype A) from honeybees to bumblebees occurs readily, yet we neither detected viral transmission from bumblebees to honeybees nor onward transmission from experimentally infected to uninoculated bumblebees. Our results support the potential for viral spillover from honeybees to other bee species in the field when robbing resources from heterospecific nests or when visiting the same flowers. They also underscore the importance of studies on the virulence of DWV in wild bee species so as to evaluate viral impact on individual and population fitness as well as viral adaption to new host species.

Predicting the zoonotic capacity of mammals to transmit SARS-CoV-2

Back and forth transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between humans and animals will establish wild reservoirs of virus that endanger long-term efforts to control COVID-19 in people and to protect vulnerable animal populations. Better targeting surveillance and laboratory experiments to validate zoonotic potential requires predicting high-risk host species. A major bottleneck to this effort is the few species with available sequences for angiotensin-converting enzyme 2 receptor, a key receptor required for viral cell entry. We overcome this bottleneck by combining species' ecological and biological traits with three-dimensional modelling of host-virus protein-protein interactions using machine learning. This approach enables predictions about the zoonotic capacity of SARS-CoV-2 for greater than 5000 mammals-an order of magnitude more species than previously possible. Our predictions are strongly corroborated by in vivo studies. The predicted zoonotic capacity and proximity to humans suggest enhanced transmission risk from several common mammals, and priority areas of geographic overlap between these species and global COVID-19 hotspots. With molecular data available for only a small fraction of potential animal hosts, linking data across biological scales offers a conceptual advance that may expand our predictive modelling capacity for zoonotic viruses with similarly unknown host ranges.

Predictors of zoonotic potential in helminths

Helminths are parasites that cause disease at considerable cost to public health and present a risk for emergence as novel human infections. Although recent research has elucidated characteristics conferring a propensity to emergence in other parasite groups (e.g. viruses), the understanding of factors associated with zoonotic potential in helminths remains poor. We applied an investigator-directed learning algorithm to a global dataset of mammal helminth traits to identify factors contributing to spillover of helminths from wild animal hosts into humans. We characterized parasite traits that distinguish between zoonotic and non-zoonotic species with 91% accuracy. Results suggest that helminth traits relating to transmission (e.g. definitive and intermediate hosts) and geography (e.g. distribution) are more important to discriminating zoonotic from non-zoonotic species than morphological or epidemiological traits. Whether or not a helminth causes infection in companion animals (cats and dogs) is the most important predictor of propensity to cause human infection. Finally, we identified helminth species with high modelled propensity to cause zoonosis (over 70%) that have not previously been considered to be of risk. This work highlights the importance of prioritizing studies on the transmission of helminths that infect pets and points to the risks incurred by close associations with these animals. This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.

Spillover, hybridization, and persistence in schistosome transmission dynamics at the human-animal interface

Zoonotic spillover and hybridization of parasites are major emerging public and veterinary health concerns at the interface of infectious disease biology, evolution, and control. Schistosomiasis is a neglected tropical disease of global importance caused by parasites of the Schistosoma genus, and the Schistosoma spp. system within Africa represents a key example of a system where spillover of animal parasites into human populations has enabled formation of hybrids. Combining model-based approaches and analyses of parasitological, molecular, and epidemiological data from northern Senegal, a region with a high prevalence of schistosome hybrids, we aimed to unravel the transmission dynamics of this complex multihost, multiparasite system. Using Bayesian methods and by estimating the basic reproduction number (R₀ ), we evaluate the frequency of zoonotic spillover of Schistosoma bovis from livestock and the potential for onward transmission of hybrid S. bovis × S. haematobium offspring within human populations. We estimate R₀ of hybrid schistosomes to be greater than the critical threshold of one (1.76; 95% CI 1.59 to 1.99), demonstrating the potential for hybridization to facilitate spread and establishment of schistosomiasis beyond its original geographical boundaries. We estimate R₀ for S. bovis to be greater than one in cattle (1.43; 95% CI 1.24 to 1.85) but not in other ruminants, confirming cattle as the primary zoonotic reservoir. Through longitudinal simulations, we also show that where S. bovis and S. haematobium are coendemic (in livestock and humans respectively), the relative importance of zoonotic transmission is predicted to increase as the disease in humans nears elimination.

Setting the Terms for Zoonotic Diseases: Effective Communication for Research, Conservation, and Public Policy

Many of the world's most pressing issues, such as the emergence of zoonotic diseases, can only be addressed through interdisciplinary research. However, the findings of interdisciplinary research are susceptible to miscommunication among both professional and non-professional audiences due to differences in training, language, experience, and understanding. Such miscommunication contributes to the misunderstanding of key concepts or processes and hinders the development of effective research agendas and public policy. These misunderstandings can also provoke unnecessary fear in the public and have devastating effects for wildlife conservation. For example, inaccurate communication and subsequent misunderstanding of the potential associations between certain bats and zoonoses has led to persecution of diverse bats worldwide and even government calls to cull them. Here, we identify four types of miscommunication driven by the use of terminology regarding bats and the emergence of zoonotic diseases that we have categorized based on their root causes: (1) incorrect or overly broad use of terms; (2) terms that have unstable usage within a discipline, or different usages among disciplines; (3) terms that are used correctly but spark incorrect inferences about biological processes or significance in the audience; (4) incorrect inference drawn from the evidence presented. We illustrate each type of miscommunication with commonly misused or misinterpreted terms, providing a definition, caveats and common misconceptions, and suggest alternatives as appropriate. While we focus on terms specific to bats and disease ecology, we present a more general framework for addressing miscommunication that can be applied to other topics and disciplines to facilitate more effective research, problem-solving, and public policy.

From Protein to Pandemic: The Transdisciplinary Approach Needed to Prevent Spillover and the Next Pandemic

Pandemics are a consequence of a series of processes that span scales from viral biology at 10-9 m to global transmission at 106 m. The pathogen passes from one host species to another through a sequence of events that starts with an infected reservoir host and entails interspecific contact, innate immune responses, receptor protein structure within the potential host, and the global spread of the novel pathogen through the naive host population. Each event presents a potential barrier to the onward passage of the virus and should be characterized with an integrated transdisciplinary approach. Epidemic control is based on the prevention of exposure, infection, and disease. However, the ultimate pandemic prevention is prevention of the spillover event itself. Here, we focus on the potential for preventing the spillover of henipaviruses, a group of viruses derived from bats that frequently cross species barriers, incur high human mortality, and are transmitted among humans via stuttering chains. We outline the transdisciplinary approach needed to prevent the spillover process and, therefore, future pandemics.

Predicting the zoonotic capacity of mammals to transmit SARS-CoV-2

Back and forth transmission of SARS-CoV-2 between humans and animals may lead to wild reservoirs of virus that can endanger efforts toward long-term control of COVID-19 in people, and protecting vulnerable animal populations that are particularly susceptible to lethal disease. Predicting high risk host species is key to targeting field surveillance and lab experiments that validate host zoonotic potential. A major bottleneck to predicting animal hosts is the small number of species with available molecular information about the structure of ACE2, a key cellular receptor required for viral cell entry. We overcome this bottleneck by combining species' ecological and biological traits with 3D modeling of virus and host cell protein interactions using machine learning methods. This approach enables predictions about the zoonotic capacity of SARS-CoV-2 for over 5,000 mammals - an order of magnitude more species than previously possible. The high accuracy predictions achieved by this approach are strongly corroborated by in vivo empirical studies. We identify numerous common mammal species whose predicted zoonotic capacity and close proximity to humans may further enhance the risk of spillover and spillback transmission of SARS-CoV-2. Our results reveal high priority areas of geographic overlap between global COVID-19 hotspots and potential new mammal hosts of SARS-CoV-2. With molecular sequence data available for only a small fraction of potential host species, predictive modeling integrating data across multiple biological scales offers a conceptual advance that may expand our predictive capacity for zoonotic viruses with similarly unknown and potentially broad host ranges.

Zoonotic spillover: Understanding basic aspects for better prevention

The transmission of pathogens from wild animals to humans is called “zoonotic spillover”. Most human infectious diseases (60-75%) are derived from pathogens that originally circulated in non-human animal species. This demonstrates that spillover has a fundamental role in the emergence of new human infectious diseases. Understanding the factors that facilitate the transmission of pathogens from wild animals to humans is essential to establish strategies focused on the reduction of the frequency of spillover events. In this context, this article describes the basic aspects of zoonotic spillover and the main factors involved in spillover events, considering the role of the inter-species interactions, phylogenetic distance between host species, environmental drivers, and specific characteristics of the pathogens, animals, and humans. As an example, the factors involved in the emergence of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic are discussed, indicating what can be learned from this public health emergency, and what can be applied to the Brazilian scenario. Finally, this article discusses actions to prevent or reduce the frequency of zoonotic spillover events.

The Use and Underuse of Model Systems in Infectious Disease Ecology and Evolutionary Biology

AbstractEver since biologists began studying the ecology and evolution of infectious diseases (EEID), laboratory-based model systems have been important for developing and testing theory. Yet what EEID researchers mean by the term "model systems" and what they want from them is unclear. This uncertainty hinders our ability to maximally exploit these systems, identify knowledge gaps, and establish effective new model systems. Here, we borrow a definition of model systems from the biomolecular sciences to assess how EEID researchers are (and are not) using 10 key model systems. According to this definition, model systems in EEID are not being used to their fullest and, in fact, cannot even be considered model systems. Research using these systems consistently addresses only two of the three fundamental processes that underlie disease dynamics-transmission and disease, but not recovery. Furthermore, studies tend to focus on only a few scales of biological organization that matter for disease ecology and evolution. Moreover, the field lacks an infrastructure to perform comparative analyses. We aim to begin a discussion of what we want from model systems, which would further progress toward a thorough, holistic understanding of EEID.

Animal Harms and Food Production: Informing Ethical Choices

Simple Summary

Consideration of animal welfare in food choices has become an influential contemporary theme. Traditional animal welfare views about food have been largely restricted to direct and intentional harms to livestock in intensive animal agriculture settings. However, many harms to animals arising from diverse food production practices in the world are exerted indirectly and unintentionally and often affect wildlife. Here we apply a qualitative analysis of food production by considering the breadth of harms caused by different food production systems to wild as well as domestic animals. Production systems are identified that produce relatively few and relatively many harms. The ethical implications of these findings are discussed for consumers concerned with the broad animal welfare impacts of their food choices.

Abstract

Ethical food choices have become an important societal theme in post-industrial countries. Many consumers are particularly interested in the animal welfare implications of the various foods they may choose to consume. However, concepts in animal welfare are rapidly evolving towards consideration of all animals (including wildlife) in contemporary approaches such as “One Welfare”. This approach requires recognition that negative impacts (harms) may be intentional and obvious (e.g., slaughter of livestock) but also include the under-appreciated indirect or unintentional harms that often impact wildlife (e.g., land clearing). This is especially true in the Anthropocene, where impacts on non-human life are almost ubiquitous across all human activities. We applied the “harms” model of animal welfare assessment to several common food production systems and provide a framework for assessing the breadth (not intensity) of harms imposed. We considered all harms caused to wild as well as domestic animals, both direct effects and indirect effects. We described 21 forms of harm and considered how they applied to 16 forms of food production. Our analysis suggests that all food production systems harm animals to some degree and that the majority of these harms affect wildlife, not livestock. We conclude that the food production systems likely to impose the greatest overall breadth of harms to animals are intensive animal agriculture industries (e.g., dairy) that rely on a secondary food production system (e.g., cropping), while harvesting of locally available wild plants, mushrooms or seaweed is likely to impose the least harms. We present this conceptual analysis as a resource for those who want to begin considering the complex animal welfare trade-offs involved in their food choices.

Preparing for Emerging Zoonotic Viruses

The risk of emergence and spread of novel human pathogens originating from an animal reservoir has increased in the past decades. However, the unpredictable nature of disease emergence makes surveillance and preparedness challenging. Knowledge of general risk factors for emergence and spread, combined with local level data is needed to develop a risk-based methodology for early detection. This involves the implementation of the One Health approach, integrating human, animal and environmental health sectors, as well as social sciences, bioinformatics and more. Recent technical advances, such as metagenomic sequencing, will aid the rapid detection of novel pathogens on the human-animal interface.

Evidence for and against deformed wing virus spillover from honey bees to bumble bees: a reverse genetic analysis

Deformed wing virus (DWV) is a persistent pathogen of European honey bees and the major contributor to overwintering colony losses. The prevalence of DWV in honey bees has led to significant concerns about spillover of the virus to other pollinating species. Bumble bees are both a major group of wild and commercially-reared pollinators. Several studies have reported pathogen spillover of DWV from honey bees to bumble bees, but evidence of a sustained viral infection characterized by virus replication and accumulation has yet to be demonstrated. Here we investigate the infectivity and transmission of DWV in bumble bees using the buff-tailed bumble bee Bombus terrestris as a model. We apply a reverse genetics approach combined with controlled laboratory conditions to detect and monitor DWV infection. A novel reverse genetics system for three representative DWV variants, including the two master variants of DWV—type A and B—was used. Our results directly confirm DWV replication in bumble bees but also demonstrate striking resistance to infection by certain transmission routes. Bumble bees may support DWV replication but it is not clear how infection could occur under natural environmental conditions.

A Review of Pathogen Transmission at the Backyard Chicken-Wild Bird Interface

Habitat conversion and the expansion of domesticated, invasive species into native habitats are increasingly recognized as drivers of pathogen emergence at the agricultural-wildlife interface. Poultry agriculture is one of the largest subsets of this interface, and pathogen spillover events between backyard chickens and wild birds are becoming more commonly reported. Native wild bird species are under numerous anthropogenic pressures, but the risks of pathogen spillover from domestic chickens have been historically underappreciated as a threat to wild birds. Now that the backyard chicken industry is one of the fastest growing industries in the world, it is imperative that the principles of biosecurity, specifically bioexclusion and biocontainment, are legislated and implemented. We reviewed the literature on spillover events of pathogens historically associated with poultry into wild birds. We also reviewed the reasons for biosecurity failures in backyard flocks that lead to those spillover events and provide recommendations for current and future backyard flock owners.

Skirmish or war: the emergence of agricultural plant pathogens

Understanding the ecological and evolutionary processes underlying the emergence of infectious disease is critically important in guiding prevention, management and breeding strategies. Novel pathogen lineages may arise within agricultural environments, wild hosts or from non-host associated disease reservoirs. Although the source of most disease outbreaks remains unknown, environmental and zoonotic origins are frequently identified in mammalian pathosystems and expanded sampling of plant pathosystems reveals important links with wild populations. This review describes key ecological and evolutionary processes underlying disease emergence, with particular emphasis on shifts from wild reservoirs to cultivated hosts and genetic mechanisms driving host adaption subsequent to emergence.

Lassa Virus Genetics

In a pattern repeated across a range of ecological niches, arenaviruses have evolved a compact four-gene genome to orchestrate a complex life cycle in a narrow range of susceptible hosts. A number of mammalian arenaviruses cross-infect humans, often causing a life-threatening viral hemorrhagic fever. Among this group of geographically bound zoonoses, Lassa virus has evolved a unique niche that leads to significant and sustained human morbidity and mortality. As a biosafety level 4 pathogen, direct study of the pathogenesis of Lassa virus is limited by the sparse availability, high operating costs, and technical restrictions of the high-level biocontainment laboratories required for safe experimentation. In this chapter, we introduce the relationship between genome structure and the life cycle of Lassa virus and outline reverse genetic approaches used to probe and describe functional elements of the Lassa virus genome. We then review the tools used to obtain viral genomic sequences used for phylogeny and molecular diagnostics, before shifting to a population perspective to assess the contributions of phylogenetic analysis in understanding the evolution and ecology of Lassa virus in West Africa. We finally consider the future outlook and clinical applications for genetic study of Lassa virus.

Molecular identification of the Trypanosoma (Herpetosoma) lewisi clade in black rats (Rattus rattus) from Australia

Invasive rodent species are known hosts for a diverse range of infectious microorganisms and have long been associated with the spread of disease globally. The present study describes molecular evidence for the presence of a Trypanosoma sp. from black rats (Rattus rattus) in northern Sydney, Australia. Sequences of the 18S ribosomal RNA (rRNA) locus were obtained in two out of eleven (18%) blood samples with subsequent phylogenetic analysis confirming the identity within the Trypanosoma lewisi clade.

Reframing conservation physiology to be more inclusive, integrative, relevant and forward-looking: reflections and a horizon scan

Applying physiological tools, knowledge and concepts to understand conservation problems (i.e. conservation physiology) has become commonplace and confers an ability to understand mechanistic processes, develop predictive models and identify cause-and-effect relationships. Conservation physiology is making contributions to conservation solutions; the number of 'success stories' is growing, but there remain unexplored opportunities for which conservation physiology shows immense promise and has the potential to contribute to major advances in protecting and restoring biodiversity. Here, we consider how conservation physiology has evolved with a focus on reframing the discipline to be more inclusive and integrative. Using a 'horizon scan', we further explore ways in which conservation physiology can be more relevant to pressing conservation issues of today (e.g. addressing the Sustainable Development Goals; delivering science to support the UN Decade on Ecosystem Restoration), as well as more forward-looking to inform emerging issues and policies for tomorrow. Our horizon scan provides evidence that, as the discipline of conservation physiology continues to mature, it provides a wealth of opportunities to promote integration, inclusivity and forward-thinking goals that contribute to achieving conservation gains. To advance environmental management and ecosystem restoration, we need to ensure that the underlying science (such as that generated by conservation physiology) is relevant with accompanying messaging that is straightforward and accessible to end users.

The problem of scale in the prediction and management of pathogen spillover

Disease emergence events, epidemics and pandemics all underscore the need to predict zoonotic pathogen spillover. Because cross-species transmission is inherently hierarchical, involving processes that occur at varying levels of biological organization, such predictive efforts can be complicated by the many scales and vastness of data potentially required for forecasting. A wide range of approaches are currently used to forecast spillover risk (e.g. macroecology, pathogen discovery, surveillance of human populations, among others), each of which is bound within particular phylogenetic, spatial and temporal scales of prediction. Here, we contextualize these diverse approaches within their forecasting goals and resulting scales of prediction to illustrate critical areas of conceptual and pragmatic overlap. Specifically, we focus on an ecological perspective to envision a research pipeline that connects these different scales of data and predictions from the aims of discovery to intervention. Pathogen discovery and predictions focused at the phylogenetic scale can first provide coarse and pattern-based guidance for which reservoirs, vectors and pathogens are likely to be involved in spillover, thereby narrowing surveillance targets and where such efforts should be conducted. Next, these predictions can be followed with ecologically driven spatio-temporal studies of reservoirs and vectors to quantify spatio-temporal fluctuations in infection and to mechanistically understand how pathogens circulate and are transmitted to humans. This approach can also help identify general regions and periods for which spillover is most likely. We illustrate this point by highlighting several case studies where long-term, ecologically focused studies (e.g. Lyme disease in the northeast USA, Hendra virus in eastern Australia, Plasmodium knowlesi in Southeast Asia) have facilitated predicting spillover in space and time and facilitated the design of possible intervention strategies. Such studies can in turn help narrow human surveillance efforts and help refine and improve future large-scale, phylogenetic predictions. We conclude by discussing how greater integration and exchange between data and predictions generated across these varying scales could ultimately help generate more actionable forecasts and interventions. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.

Improving risk assessment of the emergence of novel influenza A viruses by incorporating environmental surveillance

Reassortment is an evolutionary mechanism by which influenza A viruses (IAV) generate genetic novelty. Reassortment is an important driver of host jumps and is widespread according to retrospective surveillance studies. However, predicting the epidemiological risk of reassortant emergence in novel hosts from surveillance data remains challenging. IAV strains persist and co-occur in the environment, promoting co-infection during environmental transmission. These conditions offer opportunity to understand reassortant emergence in reservoir and spillover hosts. Specifically, environmental RNA could provide rich information for understanding the evolutionary ecology of segmented viruses, and transform our ability to quantify epidemiological risk to spillover hosts. However, significant challenges with recovering and interpreting genomic RNA from the environment have impeded progress towards predicting reassortant emergence from environmental surveillance data. We discuss how the fields of genomics, experimental ecology and epidemiological modelling are well positioned to address these challenges. Coupling quantitative disease models and natural transmission studies with new molecular technologies, such as deep-mutational scanning and single-virus sequencing of environmental samples, should dramatically improve our understanding of viral co-occurrence and reassortment. We define observable risk metrics for emerging molecular technologies and propose a conceptual research framework for improving accuracy and efficiency of risk prediction. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.

Percolation models of pathogen spillover

Predicting pathogen spillover requires counting spillover events and aligning such counts with process-related covariates for each spillover event. How can we connect our analysis of spillover counts to simple, mechanistic models of pathogens jumping from reservoir hosts to recipient hosts? We illustrate how the pathways to pathogen spillover can be represented as a directed graph connecting reservoir hosts and recipient hosts and the number of spillover events modelled as a percolation of infectious units along that graph. Percolation models of pathogen spillover formalize popular intuition and management concepts for pathogen spillover, such as the inextricably multilevel nature of cross-species transmission, the impact of covariance between processes such as pathogen shedding and human susceptibility on spillover risk, and the assumptions under which the effect of a management intervention targeting one process, such as persistence of vectors, will translate to an equal effect on the overall spillover risk. Percolation models also link statistical analysis of spillover event datasets with a mechanistic model of spillover. Linear models, one might construct for process-specific parameters, such as the log-rate of shedding from one of several alternative reservoirs, yield a nonlinear model of the log-rate of spillover. The resulting nonlinearity is approximately piecewise linear with major impacts on statistical inferences of the importance of process-specific covariates such as vector density. We recommend that statistical analysis of spillover datasets use piecewise linear models, such as generalized additive models, regression clustering or ensembles of linear models, to capture the piecewise linearity expected from percolation models. We discuss the implications of our findings for predictions of spillover risk beyond the range of observed covariates, a major challenge of forecasting spillover risk in the Anthropocene. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.