One model to rule them all? Modelling approaches across OneHealth for human, animal and plant epidemics

Re-Envisioning the Plant Disease Triangle: Full Integration of the Host Microbiota and a Focal Pivot to Health Outcomes

The disease triangle is a structurally simple but conceptually rich model that is used in plant pathology and other fields of study to explain infectious disease as an outcome of the three-way relationship between a host, a pathogen, and their environment. It also serves as a guide for finding solutions to treat, predict, and prevent such diseases. With the omics-driven, evidence-based realization that the abundance and activity of a pathogen are impacted by proximity to and interaction with a diverse multitude of other microorganisms colonizing the same host, the disease triangle evolved into a tetrahedron shape, which features an added fourth dimension representing the host-associated microbiota. Another variant of the disease triangle emerged from the recently formulated pathobiome paradigm, which deviates from the classical "one pathogen" etiology of infectious disease in favor of a scenario in which disease represents a conditional outcome of complex interactions between and among a host, its microbiota (including microbes with pathogenic potential), and the environment. The result is a version of the original disease triangle where "pathogen" is substituted with "microbiota." Here, as part of a careful and concise review of the origin, history, and usage of the disease triangle, I propose a next step in its evolution, which is to replace the word "disease" in the center of the host-microbiota-environment triad with the word "health." This triangle highlights health as a desirable outcome (rather than disease as an unwanted state) and as an emergent property of host-microbiota-environment interactions. Applied to the discipline of plant pathology, the health triangle offers an expanded range of targets and approaches for the diagnosis, prediction, restoration, and maintenance of plant health outcomes. Its applications are not restricted to infectious diseases only, and its underlying framework is more inclusive of all microbial contributions to plant well-being, including those by mycorrhizal fungi and nitrogen-fixing bacteria, for which there never was a proper place in the plant disease triangle. The plant health triangle also may have an edge as an education and communication tool to convey and stress the importance of healthy plants and their associated microbiota to a broader public and stakeholdership.

The Greening of One Health: Plants, Pathogens, and the Environment

One Health has an aspirational goal of ensuring the health of humans, animals, plants, and the environment through transdisciplinary, collaborative research. At its essence, One Health addresses the human clash with Nature by formulating strategies to repair and restore a (globally) perturbed ecosystem. A more nuanced evaluation of humankind's impact on the environment (Nature, Earth, Gaia) would fully intercalate plants, plant pathogens, and beneficial plant microbes into One Health. Here, several examples point out how plants and plant microbes are keystones of One Health. Meaningful cross-pollination between plant, animal, and human health practitioners can drive discovery and application of innovative tools to address the many complex problems within the One Health framework.

From molecules to ecosystems: Insights into a network of interactions for a Chagas disease outbreak using Triatoma brasiliensis as natural samplers

Exploring the dynamics of disease transmission involves an understanding of complex interactions within the eco-epidemiologic framework. In the context of Chagas disease (CD), elements are mainly represented by the interactions among the pathogen, insect vector, host, humans and the environment. We performed quantitative and qualitative analyses on a dataset derived from 98 Triatoma brasiliensis infected by trypanosomatids, which were linked to a CD outbreak in the semi-arid region of northeastern Brazil. We extracted invertebrate-derived DNA (iDNA) from these insects, comprising 18 populations around the outbreak area, each indicative of various strata of anthropogenic influence. Food source (FS) diversity, representing potential parasite reservoirs, was determined through mitochondrial gene (cyt b) sequencing of vertebrates, and parasite genotyping was accessed using fluorescent amplified fragment barcodes (FFLB) of trypanosomatids. We also assessed the residents' awareness of breeding sites for CD vectors in the inspected houses. The quantification of Trypanosoma cruzi was estimated via real-time PCR and is denominated here as the average parasite load (PL) per insect (T. cruzi/intestinal unit). We aimed to address vector-parasite-host-environment interactions that were discussed based on their significance among the components. Notably, among the significant interactions, we observed that the PL in the insects was significantly influenced by FS. Infected insects that fed on the classic reservoir, Didelphis albiventris, and Galea spixii exhibited higher PLs, compared to those that fed on Kerodon rupestris (p < 0.04)-a primary host. While D. albiventris is already recognized as a synanthropic species, we propose that G. spixii may also be undergoing a synanthropic process. Conversely, domestic cats are frequently identified as FS in infected insects from the sylvatic environment, suggesting a possible change in their behavior towards a wild state. Therefore, we propose that neglected anthropogenic actions have facilitated the reciprocal (sylvatic-peridomestic) circulation of T. cruzi-especially noted for TcI because it was predominant in insects found in peridomestic environments. Residents are often unaware of the existence of insect breeding grounds near their homes, particularly when it involves the storage of materials without planning for use, such as piles of tiles, bricks and wood. Although indirect inferences about the interaction among vector-parasite-host-environment are still incipient, we highlight the potential use of vectors as natural samplers of biological and ecological components in transmitting the disease.

Fate and transport modelling for evaluating antibiotic resistance in aquatic environments: Current knowledge and research priorities

Antibiotics have revolutionised medicine in the last century and enabled the prevention of bacterial infections that were previously deemed untreatable. However, in parallel, bacteria have increasingly developed resistance to antibiotics through various mechanisms. When resistant bacteria find their way into terrestrial and aquatic environments, animal and human exposures increase, e.g., via polluted soil, food, and water, and health risks multiply. Understanding the fate and transport of antibiotic resistant bacteria (ARB) and the transfer mechanisms of antibiotic resistance genes (ARGs) in aquatic environments is critical for evaluating and mitigating the risks of resistant-induced infections. The conceptual understanding of sources and pathways of antibiotics, ARB, and ARGs from society to the water environments is essential for setting the scene and developing an appropriate framework for modelling. Various factors and processes associated with hydrology, ecology, and climate change can significantly affect the fate and transport of ARB and ARGs in natural environments. This article reviews current knowledge, research gaps, and priorities for developing water quality models to assess the fate and transport of ARB and ARGs. The paper also provides inputs on future research needs, especially the need for new predictive models to guide risk assessment on AR transmission and spread in aquatic environments.

On a handshake: business-to-business trust in the biosecurity behaviours of the UK live plant trade

The movement of plants through the ornamental plant trade presents a major source of risk for the introduction and spread of plant pests and pathogens. To minimise the likelihood of infested or infected plants moving through the value chain, individual businesses can adopt a range of biosecurity practices to prevent introduction on site, as well as detecting and then containing or eradicating any plant pests or pathogens present. However, a major additional source of risk is the arrival of unhealthy plants sourced from a supplier. Using the example of bacterial plant pathogen Xylella fastidiosa which has a large host range and potentially devastating economic and environmental impacts, we highlight the importance of trust when businesses navigate the risks of sourcing plants. Through interviews and a survey with a range of plant businesses, we show (i) how two general types of risk-relational risk associated with suppliers acting in good faith, and performance risk associated with suppliers having the ability to perform as expected-can be applied to the challenge of sourcing healthy plants, (ii) how businesses respond to these risks through behaviours based on trust and control, and (iii) the potential outcomes of trust-based and control-based behaviours in the presence of a hard to detect pathogen such as Xylella fastidiosa. We conclude that trust is a significant component in decision-making in the live plant trade, and as such any behavioural interventions designed to encourage better biosecurity practices in the industry should capitalise on this understanding to strengthen responses and avoid undermining of effort.

Persistent cross-species SARS-CoV-2 variant infectivity predicted via comparative molecular dynamics simulation

Widespread human transmission of SARS-CoV-2 highlights the substantial public health, economic and societal consequences of virus spillover from wildlife and also presents a repeated risk of reverse spillovers back to naive wildlife populations. We employ comparative statistical analyses of a large set of short-term molecular dynamic (MD) simulations to investigate the potential human-to-bat (genus Rhinolophus) cross-species infectivity allowed by the binding of SARS-CoV-2 receptor-binding domain (RBD) to angiotensin-converting enzyme 2 (ACE2) across the bat progenitor strain and emerging human strain variants of concern (VOC). We statistically compare the dampening of atom motion across protein sites upon the formation of the RBD/ACE2 binding interface using various bat versus human target receptors (i.e. bACE2 and hACE2). We report that while the bat progenitor viral strain RaTG13 shows some pre-adaption binding to hACE2, it also exhibits stronger affinity to bACE2. While early emergent human strains and later VOCs exhibit robust binding to both hACE2 and bACE2, the delta and omicron variants exhibit evolutionary adaption of binding to hACE2. However, we conclude there is a still significant risk of mammalian cross-species infectivity of human VOCs during upcoming waves of infection as COVID-19 transitions from a pandemic to endemic status.

An HLD Model for Tomato Bacterial Canker Focusing on Epidemics of the Pathogen Due to Cutting by Infected Scissors

A healthy, latently infected, diseased (HLD) plant model for botanical epidemics was defined for tomato bacterial canker (TBC) caused by the pathogenic plant bacteria, Clavibacter michiganensis subsp. michiganensis (Cmm). To estimate the infection probability parameter, inoculation experiments were conducted in which it was assumed that infection is transferred to healthy plants through contaminated scissors used to cut symptomless infected plants. The approximate concentration of Cmm in symptomless infected plants was 1 × 10⁶ cells/mL, and the probability of infection of healthy tomato plants was approximately 0.75 due to cutting with scissors soaked in a cell suspension of Cmm at 1 × 10⁶ cells/mL. Three different HLD models were developed by changing some parameters, and the D curve calculated by the developed HLD model A was quite similar to the curve of the proportion of diseased plants observed in fields that had a severe disease incidence. Under a simulation of disease incidence using this model, the basic reproduction number (R₀) was 2.6. However, if the infected scissors were disinfected using ethanol, R₀ was estimated as 0.3. The HLD model for TBC can be used to simulate the increasing number of diseased plants and the term of disease incidence.

When and why direct transmission models can be used for environmentally persistent pathogens

Variants of the susceptible-infected-removed (SIR) model of Kermack & McKendrick (1927) enjoy wide application in epidemiology, offering simple yet powerful inferential and predictive tools in the study of diverse infectious diseases across human, animal and plant populations. Direct transmission models (DTM) are a subset of these that treat the processes of disease transmission as comprising a series of discrete instantaneous events. Infections transmitted indirectly by persistent environmental pathogens, however, are examples where a DTM description might fail and are perhaps better described by models that comprise explicit environmental transmission routes, so-called environmental transmission models (ETM). In this paper we discuss the stochastic susceptible-exposed-infected-removed (SEIR) DTM and susceptible-exposed-infected-removed-pathogen (SEIR-P) ETM and we show that the former is the timescale separation limit of the latter, with ETM host-disease dynamics increasingly resembling those of a DTM when the pathogen’s characteristic timescale is shortened, relative to that of the host population. Using graphical posterior predictive checks (GPPC), we investigate the validity of the SEIR model when fitted to simulated SEIR-P host infection and removal times. Such analyses demonstrate how, in many cases, the SEIR model is robust to departure from direct transmission. Finally, we present a case study of white spot disease (WSD) in penaeid shrimp with rates of environmental transmission and pathogen decay (SEIR-P model parameters) estimated using published results of experiments. Using SEIR and SEIR-P simulations of a hypothetical WSD outbreak management scenario, we demonstrate how relative shortening of the pathogen timescale comes about in practice. With atttempts to remove diseased shrimp from the population every 24h, we see SEIR and SEIR-P model outputs closely conincide. However, when removals are 6-hourly, the two models’ mean outputs diverge, with distinct predictions of outbreak size and duration.

Mathematical models of the spread and progression of communicable disease in populations are important tools in efforts to prevent and control outbreaks. A common class of disease models assume that infection is transmitted directly from infectious to susceptible individuals when they are in close proximity—so called direct transmission models. These are used widely and have proven invaluable as simplified descriptions of a wide array of infectious diseases in diverse populations. However, many pathogens spread through indirect, environmental routes of transmission, for example via contact with contaminated water sources in the case of cholera, or inhalation of infectious airborne droplets for respiratory infections, such as Covid-19.

We show that direct transmission models work well for such pathogens with short environmental lifetimes and where hosts shed pathogens into the environment at high rates. This means that we do not require information about environmental pathogen levels to understand the behaviour of outbreaks caused by these pathogens. When shedding rates are also low, e.g., with macroparasitic infections, or when variable environmental factors play a role in transmissibility, then explicit modelling of both the pathogen and environmental transmission will provide a more accurate picture than a direct transmission approximation.

Cross-sectional telephone surveys as a tool to study epidemiological factors and monitor seasonal influenza activity in Malta

Background

Seasonal influenza has major implications for healthcare services as outbreaks often lead to high activity levels in health systems. Being able to predict when such outbreaks occur is vital. Mathematical models have extensively been used to predict epidemics of infectious diseases such as seasonal influenza and to assess effectiveness of control strategies. Availability of comprehensive and reliable datasets used to parametrize these models is limited. In this paper we combine a unique epidemiological dataset collected in Malta through General Practitioners (GPs) with a novel method using cross-sectional surveys to study seasonal influenza dynamics in Malta in 2014–2016, to include social dynamics and self-perception related to seasonal influenza.

Methods

Two cross-sectional public surveys (n = 406 per survey) were performed by telephone across the Maltese population in 2014–15 and 2015–16 influenza seasons. Survey results were compared with incidence data (diagnosed seasonal influenza cases) collected by GPs in the same period and with Google Trends data for Malta. Information was collected on whether participants recalled their health status in past months, occurrences of influenza symptoms, hospitalisation rates due to seasonal influenza, seeking GP advice, and other medical information.

Results

We demonstrate that cross-sectional surveys are a reliable alternative data source to medical records. The two surveys gave comparable results, indicating that the level of recollection among the public is high. Based on two seasons of data, the reporting rate in Malta varies between 14 and 22%. The comparison with Google Trends suggests that the online searches peak at about the same time as the maximum extent of the epidemic, but the public interest declines and returns to background level. We also found that the public intensively searched the Internet for influenza-related terms even when number of cases was low.

Conclusions

Our research shows that a telephone survey is a viable way to gain deeper insight into a population’s self-perception of influenza and its symptoms and to provide another benchmark for medical statistics provided by GPs and Google Trends. The information collected can be used to improve epidemiological modelling of seasonal influenza and other infectious diseases, thus effectively contributing to public health.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11862-x.

Modelling plant health for policy

Plant health is relatively poorly funded compared with animal and human health issues. However, we contend it is at least as complex and likely more so given the number of pests and hosts and that outbreaks occur in poorly monitored open systems. Modelling is often suggested as a method to better consider the threats to plant health to aid resource and time poor decision makers in their prioritisation of responses. However, like other areas of science, the modelling community has not always provided accessible and relevant solutions. We describe some potential solutions to developing plant health models in conjunction with decision makers based upon a recent example and illustrate how an increased emphasis on plant health is slowly expanding the potential role of modelling in decision making. We place the research in the Credibility, Relevance and Legitimacy (CRELE) framework and discuss the implications for future developments in co-construction of policy-linked models.

The Epidemiological Framework for Biological Invasions (EFBI): an interdisciplinary foundation for the assessment of biosecurity threats

Emerging microparasite (e.g. viruses, bacteria, protozoa and fungi) epidemics and the introduction of non-native pests and weeds are major biosecurity threats worldwide. The likelihood of these threats is often estimated from probabilities of their entry, establishment, spread and ease of prevention. If ecosystems are considered equivalent to hosts, then compartment disease models should provide a useful framework for understanding the processes that underpin non-native species invasions. To enable greater cross-fertilisation between these two disciplines, the Epidemiological Framework for Biological Invasions (EFBI) is developed that classifies ecosystems in relation to their invasion status: Susceptible, Exposed, Infectious and Resistant. These states are linked by transitions relating to transmission, latency and recovery. This viewpoint differs markedly from the species-centric approaches often applied to non-native species. It allows generalisations from epidemiology, such as the force of infection, the basic reproductive ratio R0, super-spreaders, herd immunity, cordon sanitaire and ring vaccination, to be discussed in the novel context of non-native species and helps identify important gaps in the study of biological invasions. The EFBI approach highlights several limitations inherent in current approaches to the study of biological invasions including: (i) the variance in non-native abundance across ecosystems is rarely reported; (ii) field data rarely (if ever) distinguish source from sink ecosystems; (iii) estimates of the susceptibility of ecosystems to invasion seldom account for differences in exposure to non-native species; and (iv) assessments of ecosystem susceptibility often confuse the processes that underpin patterns of spread within -and between- ecosystems. Using the invasion of lakes as a model, the EFBI approach is shown to present a new biosecurity perspective that takes account of ecosystem status and complements demographic models to deliver clearer insights into the dynamics of biological invasions at the landscape scale. It will help to identify whether management of the susceptibility of ecosystems, of the number of vectors, or of the diversity of pathways (for movement between ecosystems) is the best way of limiting or reversing the population growth of a non-native species. The framework can be adapted to incorporate increasing levels of complexity and realism and to provide insights into how to monitor, map and manage biological invasions more effectively.

Optimising efficacy of antibiotics against systemic infection by varying dosage quantities and times

Mass production and use of antibiotics has led to the rise of resistant bacteria, a problem possibly exacerbated by inappropriate and non-optimal application. Antibiotic treatment often follows fixed-dose regimens, with a standard dose of antibiotic administered equally spaced in time. But are such fixed-dose regimens optimal or can alternative regimens be designed to increase efficacy? Yet, few mathematical models have aimed to identify optimal treatments based on biological data of infections inside a living host. In addition, assumptions to make the mathematical models analytically tractable limit the search space of possible treatment regimens (e.g. to fixed-dose treatments). Here, we aimed to address these limitations by using experiments in a Galleria mellonella (insect) model of bacterial infection to create a fully parametrised mathematical model of a systemic Vibrio infection. We successfully validated this model with biological experiments, including treatments unseen by the mathematical model. Then, by applying artificial intelligence, this model was used to determine optimal antibiotic dosage regimens to treat the host to maximise survival while minimising total antibiotic used. As expected, host survival increased as total quantity of antibiotic applied during the course of treatment increased. However, many of the optimal regimens tended to follow a large initial ‘loading’ dose followed by doses of incremental reductions in antibiotic quantity (dose ‘tapering’). Moreover, application of the entire antibiotic in a single dose at the start of treatment was never optimal, except when the total quantity of antibiotic was very low. Importantly, the range of optimal regimens identified was broad enough to allow the antibiotic prescriber to choose a regimen based on additional criteria or preferences. Our findings demonstrate the utility of an insect host to model antibiotic therapies in vivo and the approach lays a foundation for future regimen optimisation for patient and societal benefits.

Research into optimal antibiotic use to improve efficacy is far behind that of cancer care, where personalised treatment is common. The integration of mathematical models with biological observations offers hope to optimise antibiotic use, and in this present study an in vivo insect model of systemic Vibrio infection was used for the first time to determine critical model parameters for optimal antibiotic treatment. By this approach, the optimal regimens tended to result from a large initial ‘loading’ dose followed by subsequent doses of incremental reductions in antibiotic quantity (dose ‘tapering’). The approach and findings of this study opens a new avenue towards optimal application of our precious antibiotic arsenal and may lead to more effective treatment regimens for patients, thus reducing the health and economic burdens associated with bacterial infections. Importantly, it can be argued that until we understand how to use a single antibiotic optimally, it is unlikely we will identify optimal ways to use multiple antibiotics simultaneously.

Preface to theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'

This preface forms part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.

Detection, forecasting and control of infectious disease epidemics: modelling outbreaks in humans, animals and plants

The 1918 influenza pandemic is one of the most devastating infectious disease epidemics on record, having caused approximately 50 million deaths worldwide. Control measures, including prohibiting non-essential gatherings as well as closing cinemas and music halls, were applied with varying success and limited knowledge of transmission dynamics. One hundred years later, following developments in the field of mathematical epidemiology, models are increasingly used to guide decision-making and devise appropriate interventions that mitigate the impacts of epidemics. Epidemiological models have been used as decision-making tools during outbreaks in human, animal and plant populations. However, as the subject has developed, human, animal and plant disease modelling have diverged. Approaches have been developed independently for pathogens of each host type, often despite similarities between the models used in these complementary fields. With the increased importance of a One Health approach that unifies human, animal and plant health, we argue that more inter-disciplinary collaboration would enhance each of the related disciplines. This pair of theme issues presents research articles written by human, animal and plant disease modellers. In this introductory article, we compare the questions pertinent to, and approaches used by, epidemiological modellers of human, animal and plant pathogens, and summarize the articles in these theme issues. We encourage future collaboration that transcends disciplinary boundaries and links the closely related areas of human, animal and plant disease epidemic modelling. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'. This issue is linked with the subsequent theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'.