Marine infectious disease dynamics and outbreak thresholds: contact transmission, pandemic infection, and the potential role of filter feeders

Symbiotic survey of the bay scallop (Argopecten irradians) from the Gulf coast of Florida, USA

The bay scallop Argopecten irradians supported a commercial fishery in Florida but their population declined and the fishery closed in 1994. A recreational fishery remains open along the west coast of Florida despite continued threats from overfishing and a changing environment. Disease is among those threats, as it is for bivalve fisheries globally. We examined the relationship between bay scallop population density, its symbiotic microbiome, and geographic location. We focused on three sites within the range of Florida's recreational scallop fishery: St. Joseph Bay (northern extent), offshore of the Steinhatchee River (central), and offshore of Hernando County (southern extent). The study was conducted prior to the seasonal opening of the fishery to minimize the impact of fishing on our results. We also sampled caged scallops that are used for restocking in St. Joseph Bay to assess the effect of artificially high density and confinement on the scallop pathobiome. Using a combination of traditional histological methods, molecular diagnostics, and metagenomics, a suite of 15 symbionts were identified. Among them, RNA-seq data revealed four novel + ssRNA viral genomes: three picorna-like viruses and one hepe-like virus. The DNA-seq library revealed a novel Mycoplasma species. Histological evaluation revealed that protozoan, helminth and crustacean infections were common in A. irradians. These potential pathogens add to those already known for A. irradians and underscores the risk they pose to the fishery.

Strategies for managing marine disease

The incidence of emerging infectious diseases (EIDs) has increased in wildlife populations in recent years and is expected to continue to increase with global environmental change. Marine diseases are relatively understudied compared with terrestrial diseases but warrant parallel attention as they can disrupt ecosystems, cause economic loss, and threaten human livelihoods. Although there are many existing tools to combat the direct and indirect consequences of EIDs, these management strategies are often insufficient or ineffective in marine habitats compared with their terrestrial counterparts, often due to fundamental differences between marine and terrestrial systems. Here, we first illustrate how the marine environment and marine organism life histories present challenges and opportunities for wildlife disease management. We then assess the application of common disease management strategies to marine versus terrestrial systems to identify those that may be most effective for marine disease outbreak prevention, response, and recovery. Finally, we recommend multiple actions that will enable more successful management of marine wildlife disease emergencies in the future. These include prioritizing marine disease research and understanding its links to climate change, improving marine ecosystem health, forming better monitoring and response networks, developing marine veterinary medicine programs, and enacting policy that addresses marine and other wildlife diseases. Overall, we encourage a more proactive rather than reactive approach to marine wildlife disease management and emphasize that multidisciplinary collaborations are crucial to managing marine wildlife health.

A Deterministic Model for Understanding Nonlinear Viral Dynamics in Oysters

Contamination of oysters with a variety of viruses is one key pathway to trigger outbreaks of massive oyster mortality as well as human illnesses, including gastroenteritis and hepatitis. Much effort has gone into examining the fate of viruses in contaminated oysters, yet the current state of knowledge of nonlinear virus-oyster interactions is not comprehensive because most studies have focused on a limited number of processes under a narrow range of experimental conditions. A framework is needed for describing the complex nonlinear virus-oyster interactions. Here, we introduce a mathematical model that includes key processes for viral dynamics in oysters, such as oyster filtration, viral replication, the antiviral immune response, apoptosis, autophagy, and selective accumulation. We evaluate the model performance for two groups of viruses, those that replicate in oysters (e.g., ostreid herpesvirus) and those that do not (e.g., norovirus), and show that this model simulates well the viral dynamics in oysters for both groups. The model analytically explains experimental findings and predicts how changes in different physiological processes and environmental conditions nonlinearly affect in-host viral dynamics, for example, that oysters at higher temperatures may be more resistant to infection by ostreid herpesvirus. It also provides new insight into food treatment for controlling outbreaks, for example, that depuration for reducing norovirus levels is more effective in environments where oyster filtration rates are higher. This study provides the foundation of a modeling framework to guide future experiments and numerical modeling for better prediction and management of outbreaks.

IMPORTANCE The fate of viruses in contaminated oysters has received a significant amount of attention in the fields of oyster aquaculture, food quality control, and public health. However, intensive studies through laboratory experiments and in situ observations are often conducted under a narrow range of experimental conditions and for a specific purpose in their respective fields. Given the complex interactions of various processes and nonlinear viral responses to changes in physiological and environmental conditions, a theoretical framework fully describing the viral dynamics in oysters is warranted to guide future studies from a top-down design. Here, we developed a process-based, in-host modeling framework that builds a bridge for better communications between different disciplines studying virus-oyster interactions.

The economic value of R0 for selective breeding against microparasitic diseases

Background

Microparasitic diseases are caused by bacteria and viruses. Genetic improvement of resistance to microparasitic diseases in breeding programs is desirable and should aim at reducing the basic reproduction ratio \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0. Recently, we developed a method to derive the economic value of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0 for macroparasitic diseases. In epidemiological models for microparasitic diseases, an animal’s disease status is treated as infected or not infected, resulting in a definition of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0 that differs from that for macroparasitic diseases. Here, we extend the method for the derivation of the economic value of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0 to microparasitic diseases.

Methods

When \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0} \le 1$$\end{document}R0≤1, the economic value of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0 is zero because the disease is very rare. When \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0. is higher than 1, genetic improvement of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0 can reduce expenditures on vaccination if vaccination induces herd immunity, or it can reduce production losses due to disease. When vaccination is used to achieve herd immunity, expenditures are proportional to the critical vaccination coverage, which decreases with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0. The effect of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0 on losses is considered separately for epidemic and endemic disease. Losses for epidemic diseases are proportional to the probability and size of major epidemics. Losses for endemic diseases are proportional to the infected fraction of the population at the endemic equilibrium.

Results

When genetic improvement reduces expenditures on vaccination, expenditures decrease with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0 at an increasing rate. When genetic improvement reduces losses in epidemic or endemic diseases, losses decrease with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0 at an increasing rate. Hence, in all cases, the economic value of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0 increases as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0 decreases towards 1.

Discussion

\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0 and its economic value are more informative for potential benefits of genetic improvement than heritability estimates for survival after a disease challenge. In livestock, the potential for genetic improvement is small for epidemic microparasitic diseases, where disease control measures limit possibilities for phenotyping. This is not an issue in aquaculture, where controlled challenge tests are performed in dedicated facilities. If genetic evaluations include infectivity, genetic gain in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0 can be accelerated but this would require different testing designs.

Conclusions

When \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0} \le 1$$\end{document}R0≤1, its economic value is zero. The economic value of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0 is highest at low values of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0 and approaches zero at high values of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{R}}_{0}$$\end{document}R0.

Quantifying the impact of temperature variation on birnavirus transmission dynamics in hard clams Meretrix lusoria

Susceptibility of hard clams Meretrix lusoria to birnavirus (BV) infections caused by temperature variations, from a mechanistic perspective, has rarely been explored. We used a deterministic susceptible-infectious-mortality (SIM) model to derive temperature-dependent key epidemiologic parameters based on data sets of viral infections in hard clams subjected to acute temperature changes. To parameterize seasonal pattern dependence, we estimated monthly based cumulative mortality and basic reproduction numbers (R₀ ) between 1997 and 2017 by way of statistical analysis. Two alternative disease control models were also proposed to assess status of controlled temperature-mediated BV infection by using, respectively, control reproduction number (R_C )-control line criterion and removal strategy-based control measure. We showed that based on R_C -control strategy, when temperatures ranged from 15 to 26.8°C, proportion of susceptible hard clams removed should be at least 0.22%. Based on removal-control strategy, we found that by limiting pond water temperature to 25-30°C, together with increased removal rates and periods to remove hard clams, it is better to remove hard clams from June and August to reduce both mortality rate and spread of BV. Our results can be used to monitor BV transmission potential in hard clams that will contribute to government control strategy to eradicate future BV epidemics.

Particle velocity controls phase transitions in contagion dynamics

Interactions often require the proximity between particles. The movement of particles, thus, drives the change of the neighbors which are located in their proximity, leading to a sequence of interactions. In pathogenic contagion, infections occur through proximal interactions, but at the same time, the movement facilitates the co-location of different strains. We analyze how the particle velocity impacts on the phase transitions on the contagion process of both a single infection and two cooperative infections. First, we identify an optimal velocity (close to half of the interaction range normalized by the recovery time) associated with the largest epidemic threshold, such that decreasing the velocity below the optimal value leads to larger outbreaks. Second, in the cooperative case, the system displays a continuous transition for low velocities, which becomes discontinuous for velocities of the order of three times the optimal velocity. Finally, we describe these characteristic regimes and explain the mechanisms driving the dynamics.

Predicting the Thermal and Allometric Dependencies of Disease Transmission via the Metabolic Theory of Ecology

The metabolic theory of ecology (MTE) provides a general framework of allometric and thermal dependence that may be useful for predicting how climate change will affect disease spread. Using Daphnia magna and a microsporidian gut parasite, we conducted two experiments across a wide thermal range and fitted transmission models that utilize MTE submodels for transmission parameters. We decomposed transmission into contact rate and probability of infection and further decomposed probability of infection into a product of gut residence time (GRT) and per-parasite infection rate of gut cells. Contact rate generally increased with temperature and scaled positively with body size, whereas infection rate had a narrow hump-shaped thermal response and scaled negatively with body size. GRT increased with host size and was longest at extreme temperatures. GRT and infection rate inside the gut combined to create a 3.5 times higher probability of infection for the smallest relative to the largest individuals. Small temperature changes caused large differences in transmission. We also fit several alternative transmission models to data at individual temperatures. The more complex models-parasite antagonism or synergism and host heterogeneity-did not substantially improve the fit to the data. Our results show that transmission rate is the product of several distinct thermal and allometric functions that can be predicted continuously across temperature and host size using the MTE.

Oysters and eelgrass: potential partners in a high pCO2 ocean

Climate change is affecting the health and physiology of marine organisms and altering species interactions. Ocean acidification (OA) threatens calcifying organisms such as the Pacific oyster, Crassostrea gigas. In contrast, seagrasses, such as the eelgrass Zostera marina, can benefit from the increase in available carbon for photosynthesis found at a lower seawater pH. Seagrasses can remove dissolved inorganic carbon from OA environments, creating local daytime pH refugia. Pacific oysters may improve the health of eelgrass by filtering out pathogens such as Labyrinthula zosterae (LZ), which causes eelgrass wasting disease (EWD). We examined how co-culture of eelgrass ramets and juvenile oysters affected the health and growth of eelgrass and the mass of oysters under different pCO₂ exposures. In Phase I, each species was cultured alone or in co-culture at 12°C across ambient, medium, and high pCO₂ conditions, (656, 1,158 and 1,606 μatm pCO₂ , respectively). Under high pCO₂ , eelgrass grew faster and had less severe EWD (contracted in the field prior to the experiment). Co-culture with oysters also reduced the severity of EWD. While the presence of eelgrass decreased daytime pCO₂ , this reduction was not substantial enough to ameliorate the negative impact of high pCO₂ on oyster mass. In Phase II, eelgrass alone or oysters and eelgrass in co-culture were held at 15°C under ambient and high pCO₂ conditions, (488 and 2,013 μatm pCO₂ , respectively). Half of the replicates were challenged with cultured LZ. Concentrations of defensive compounds in eelgrass (total phenolics and tannins), were altered by LZ exposure and pCO₂ treatments. Greater pathogen loads and increased EWD severity were detected in LZ exposed eelgrass ramets; EWD severity was reduced at high relative to low pCO₂ . Oyster presence did not influence pathogen load or EWD severity; high LZ concentrations in experimental treatments may have masked the effect of this treatment. Collectively, these results indicate that, when exposed to natural concentrations of LZ under high pCO₂ conditions, eelgrass can benefit from co-culture with oysters. Further experimentation is necessary to quantify how oysters may benefit from co-culture with eelgrass, examine these interactions in the field and quantify context-dependency.

Marine Infectious Disease Ecology

To put marine disease impacts in context requires a broad perspective on the roles infectious agents have in the ocean. Parasites infect most marine vertebrate and invertebrate species, and parasites and predators can have comparable biomass density, suggesting they play comparable parts as consumers in marine food webs. Although some parasites might increase with disturbance, most probably decline as food webs unravel. There are several ways to adapt epidemiological theory to the marine environment. In particular, because the ocean represents a three-dimensional moving habitat for hosts and parasites, models should open up the spatial scales at which infective stages and host larvae travel. In addition to open recruitment and dimensionality, marine parasites are subject to fishing, filter feeders, dose-dependent infection, environmental forcing, and death-based transmission. Adding such considerations to marine disease models will make it easier to predict which infectious diseases will increase or decrease in a changing ocean.