Revealing mechanisms of infectious disease spread through empirical contact networks

The spread of pathogens fundamentally depends on the underlying contacts between individuals. Modeling the dynamics of infectious disease spread through contact networks, however, can be challenging due to limited knowledge of how an infectious disease spreads and its transmission rate. We developed a novel statistical tool, INoDS (Identifying contact Networks of infectious Disease Spread) that estimates the transmission rate of an infectious disease outbreak, establishes epidemiological relevance of a contact network in explaining the observed pattern of infectious disease spread and enables model comparison between different contact network hypotheses. We show that our tool is robust to incomplete data and can be easily applied to datasets where infection timings of individuals are unknown. We tested the reliability of INoDS using simulation experiments of disease spread on a synthetic contact network and find that it is robust to incomplete data and is reliable under different settings of network dynamics and disease contagiousness compared with previous approaches. We demonstrate the applicability of our method in two host-pathogen systems: Crithidia bombi in bumblebee colonies and Salmonella in wild Australian sleepy lizard populations. INoDS thus provides a novel and reliable statistical tool for identifying transmission pathways of infectious disease spread. In addition, application of INoDS extends to understanding the spread of novel or emerging infectious disease, an alternative approach to laboratory transmission experiments, and overcoming common data-collection constraints.     

Seasonal infectious disease epidemiology

Seasonal change in the incidence of infectious diseases is a common phenomenon in both temperate and tropical climates. However, the mechanisms responsible for seasonal disease incidence, and the epidemiological consequences of seasonality, are poorly understood with rare exception. Standard epidemiological theory and concepts such as the basic reproductive number R0 no longer apply, and the implications for interventions that themselves may be periodic, such as pulse vaccination, have not been formally examined. This paper examines the causes and consequences of seasonality, and in so doing derives several new results concerning vaccination strategy and the interpretation of disease outbreak data. It begins with a brief review of published scientific studies in support of different causes of seasonality in infectious diseases of humans, identifying four principal mechanisms and their association with different routes of transmission. It then describes the consequences of seasonality for R0, disease outbreaks, endemic dynamics and persistence. Finally, a mathematical analysis of routine and pulse vaccination programmes for seasonal infections is presented. The synthesis of seasonal infectious disease epidemiology attempted by this paper highlights the need for further empirical and theoretical work.     

Susceptible-infected-recovered epidemics in dynamic contact networks

Contact patterns in populations fundamentally influence the spread of infectious diseases. Current mathematical methods for epidemiological forecasting on networks largely assume that contacts between individuals are fixed, at least for the duration of an outbreak. In reality, contact patterns may be quite fluid, with individuals frequently making and breaking social or sexual relationships. Here, we develop a mathematical approach to predicting disease transmission on dynamic networks in which each individual has a characteristic behaviour (typical contact number), but the identities of their contacts change in time. We show that dynamic contact patterns shape epidemiological dynamics in ways that cannot be adequately captured in static network models or mass-action models. Our new model interpolates smoothly between static network models and mass-action models using a mixing parameter, thereby providing a bridge between disparate classes of epidemiological models. Using epidemiological and sexual contact data from an Atlanta high school, we demonstrate the application of this method for forecasting and controlling sexually transmitted disease outbreaks.     

Genetic reassortment of infectious bursal disease virus in nature

Infectious bursal disease virus (IBDV), a double-stranded RNA virus, is a member of the Birnaviridae family. Four pathotypes of IBDV, attenuated, virulent, antigenic variant, and very virulent (vvIBDV), have been identified. We isolated and characterized the genomic reassortant IBDV strain ZJ2000 from severe field outbreaks in commercial flocks. Full-length genomic sequence analysis showed that ZJ2000 is a natural genetic reassortant virus with segments A and B derived from attenuated and very virulent strains of IBDV, respectively. ZJ2000 exhibited delayed replication kinetics as compared to attenuated strains. However, ZJ2000 was pathogenic to specific pathogen free (SPF) chickens and chicken embryos. Similar to a standard virulent IBDV strain, ZJ2000 caused 26.7% mortality, 100% morbidity, and severe bursal lesions at both gross and histopathological levels. Taken together, our data provide direct evidence for genetic reassortment of IBDV in nature, which may play an important role in the evolution, virulence, and host range of IBDV. Our data also suggest that VP2 is not the sole determinant of IBDV virulence, and that the RNA-dependent RNA polymerase protein, VP1, may play an important role in IBDV virulence. The discovery of reassortant viruses in nature suggests an additional risk of using live IBDV vaccines, which could act as genetic donors for genome reassortment.     

The deterministic limit of infectious disease models with dynamic partners

This paper analyzes the large population dynamics of an infectious disease model with contacts that occur during partnerships. The model allows for concurrent partnerships following a very broad class of dynamic laws. Previous work, with a stochastic version of the model, computed the reproductive number, the initial growth rate, and the final size. In the present paper, the deterministic system that is the limit for large populations is constructed. The construction is unusual in requiring two different scaling factors. Next, the approximation used by Watts and May for a related model is compared with the exact solution. This approximation is most accurate at the beginning of the epidemic and when partnerships are short. Lastly, the model is generalized to allow dependencies among partnerships. This generalization permits proportional mixing with an arbitrary distribution on the number of partners.     

Successful elimination of a lethal wildlife infectious disease in nature

Methods to mitigate the impacts of emerging infectious diseases affecting wildlife are urgently needed to combat loss of biodiversity. However, the successful mitigation of wildlife pathogens in situ has rarely occurred. Indeed, most strategies for combating wildlife diseases remain theoretical, despite the wealth of information available for combating infections in livestock and crops. Here, we report the outcome of a 5-year effort to eliminate infection with Batrachochytrium dendrobatidis affecting an island system with a single amphibian host. Our initial efforts to eliminate infection in the larval reservoir using a direct application of an antifungal were successful ex situ but infection returned to previous levels when tadpoles with cleared infections were returned to their natal sites. We subsequently combined antifungal treatment of tadpoles with environmental chemical disinfection. Infection at four of the five pools where infection had previously been recorded was eradicated, and remained so for 2 years post-application.     

Applications and advances in acoustic monitoring for infectious disease epidemiology

Emerging infectious diseases continue to pose a significant burden on global public health, and there is a critical need to better understand transmission dynamics arising at the interface of human activity and wildlife habitats. Passive acoustic monitoring (PAM), more typically applied to questions of biodiversity and conservation, provides an opportunity to collect and analyse audio data in relative real time and at low cost. Acoustic methods are increasingly accessible, with the expansion of cloud-based computing, low-cost hardware, and machine learning approaches. Paired with purposeful experimental design, acoustic data can complement existing surveillance methods and provide a novel toolkit to investigate the key biological parameters and ecological interactions that underpin infectious disease epidemiology.     

The dynamic nature of thermophily

1. Evidence for a close relation between thermophilic and mesophilic bacteria is discussed. 2. It is shown that in the absence of nutrients thermophilic bacteria at 55°C. die as rapidly as mesophilic bacteria, and that enzyme systems of the thermophils are rapidly inactivated at this temperature. 3. It is concluded that the thermophils can live at high temperatures because they can synthesize enzymes and other cellular constituents faster than these are destroyed by heat. 4. In order to account for this great synthetic capacity at high temperatures, and for the high minimum temperatures observed for many thermophils, it is postulated that these organisms have a higher temperature coefficient of enzyme synthesis than mesophils.     

The stochastic nature of biochemical networks

Cell behaviour and the cellular environment are stochastic. Phenotypes vary across isogenic populations and in individual cells over time. Here we will argue that to understand the abilities of cells we need to understand their stochastic nature. New experimental techniques allow gene expression to be followed in single cells over time and reveal stochastic bursts of both mRNA and protein synthesis in many different types of organisms. Stochasticity has been shown to be exploited by bacteria and viruses to decide between different behaviours. In fluctuating environments, cells that respond stochastically can out-compete those that sense environmental changes, and stochasticity may even have contributed to chromosomal gene order. We will focus on advances in modelling stochasticity, in understanding its effects on evolution and cellular design, and on means by which it may be exploited in biotechnology and medicine.     

The dynamic nature of eukaryotic genomes

Analyses of diverse eukaryotes reveal that genomes are dynamic, sometimes dramatically so. In numerous lineages across the eukaryotic tree of life, DNA content varies within individuals throughout life cycles and among individuals within species. Discovery of examples of genome dynamism is accelerating as genome sequences are completed from diverse eukaryotes. Though much is known about genomes in animals, fungi, and plants, these lineages represent only 3 of the 60-200 lineages of eukaryotes. Here, we discuss diverse genomic strategies in exemplar eukaryotic lineages, including numerous microbial eukaryotes, to reveal dramatic variation that challenges established views of genome evolution. For example, in the life cycle of some members of the "radiolaria," ploidy increases from haploid (N) to approximately 1,000N, whereas intrapopulation variability of the enteric parasite Entamoeba ranges from 4N to 40N. Variation has also been found within our own species, with substantial differences in both gene content and chromosome lengths between individuals. Data on the dynamic nature of genomes shift the perception of the genome from being fixed and characteristic of a species (typological) to plastic due to variation within and between species.     

Molecular epidemiology of infectious diseases

Data on molecular epidemiology of bacterial infections is summarized. The term definitions of "molecular epidemiology", "taxonomic species" are given, the limits and species structure of prokaryotes are described. The basic mechanisms of the prokaryotes variability in the epidemic process, as well as the possibilities and limitations of microbiological, molecular-biological and population--genetic typing methods, are characterized. The tactics of molecular-biological studies in analyzing the population structure on the global, regional and local levels is presented. The economic effectiveness of measures taken with due consideration of information on the clonal structure of causative agents of hospital infections is shown.     

An open-access database of infectious disease transmission trees to explore superspreader epidemiology

Historically, emerging and reemerging infectious diseases have caused large, deadly, and expensive multinational outbreaks. Often outbreak investigations aim to identify who infected whom by reconstructing the outbreak transmission tree, which visualizes transmission between individuals as a network with nodes representing individuals and branches representing transmission from person to person. We compiled a database, called OutbreakTrees, of 382 published, standardized transmission trees consisting of 16 directly transmitted diseases ranging in size from 2 to 286 cases. For each tree and disease, we calculated several key statistics, such as tree size, average number of secondary infections, the dispersion parameter, and the proportion of cases considered superspreaders, and examined how these statistics varied over the course of each outbreak and under different assumptions about the completeness of outbreak investigations. We demonstrated the potential utility of the database through 2 short analyses addressing questions about superspreader epidemiology for a variety of diseases, including Coronavirus Disease 2019 (COVID-19). First, we found that our transmission trees were consistent with theory predicting that intermediate dispersion parameters give rise to the highest proportion of cases causing superspreading events. Additionally, we investigated patterns in how superspreaders are infected. Across trees with more than 1 superspreader, we found preliminary support for the theory that superspreaders generate other superspreaders. In sum, our findings put the role of superspreading in COVID-19 transmission in perspective with that of other diseases and suggest an approach to further research regarding the generation of superspreaders. These data have been made openly available to encourage reuse and further scientific inquiry.

This study compiles and standardizes reported infectious disease transmission trees to analyze trends in superspreader frequency and generation; these data support theories that intermediate dispersion parameters give rise to the highest proportion of cases causing superspreading events, and that superspreaders generate other superspreaders.     

Infectious disease transmission and contact networks in wildlife and livestock

The use of social and contact networks to answer basic and applied questions about infectious disease transmission in wildlife and livestock is receiving increased attention. Through social network analysis, we understand that wild animal and livestock populations, including farmed fish and poultry, often have a heterogeneous contact structure owing to social structure or trade networks. Network modelling is a flexible tool used to capture the heterogeneous contacts of a population in order to test hypotheses about the mechanisms of disease transmission, simulate and predict disease spread, and test disease control strategies. This review highlights how to use animal contact data, including social networks, for network modelling, and emphasizes that researchers should have a pathogen of interest in mind before collecting or using contact data. This paper describes the rising popularity of network approaches for understanding transmission dynamics in wild animal and livestock populations; discusses the common mismatch between contact networks as measured in animal behaviour and relevant parasites to match those networks; and highlights knowledge gaps in how to collect and analyse contact data. Opportunities for the future include increased attention to experiments, pathogen genetic markers and novel computational tools.     

Effects of heterogeneous and clustered contact patterns on infectious disease dynamics

The spread of infectious diseases fundamentally depends on the pattern of contacts between individuals. Although studies of contact networks have shown that heterogeneity in the number of contacts and the duration of contacts can have far-reaching epidemiological consequences, models often assume that contacts are chosen at random and thereby ignore the sociological, temporal and/or spatial clustering of contacts. Here we investigate the simultaneous effects of heterogeneous and clustered contact patterns on epidemic dynamics. To model population structure, we generalize the configuration model which has a tunable degree distribution (number of contacts per node) and level of clustering (number of three cliques). To model epidemic dynamics for this class of random graph, we derive a tractable, low-dimensional system of ordinary differential equations that accounts for the effects of network structure on the course of the epidemic. We find that the interaction between clustering and the degree distribution is complex. Clustering always slows an epidemic, but simultaneously increasing clustering and the variance of the degree distribution can increase final epidemic size. We also show that bond percolation-based approximations can be highly biased if one incorrectly assumes that infectious periods are homogeneous, and the magnitude of this bias increases with the amount of clustering in the network. We apply this approach to model the high clustering of contacts within households, using contact parameters estimated from survey data of social interactions, and we identify conditions under which network models that do not account for household structure will be biased.     

The dynamics of sexual contact networks: effects on disease spread and control

Sexually transmitted pathogens persist in populations despite the availability of biomedical interventions and knowledge of behavioural changes that would reduce individual-level risk. While behavioural risk factors are shared between many sexually transmitted infections, the prevalence of these diseases across different risk groups varies. Understanding this heterogeneity and identifying better control strategies depends on an improved understanding of the complex social contact networks over which pathogens spread. To date, most efforts to study the impact of sexual network structure on disease dynamics have focused on static networks. However, the interaction between the dynamics of partnership formation and dissolution and the dynamics of transmission plays a role, both in restricting the effective network accessible to the pathogen, and in modulating the transmission dynamics. We present a simple method to simulate dynamical networks of sexual partnerships. We inform the model using survey data on sexual attitudes and lifestyles, and investigate how the duration of infectiousness changes the effective contact network over which disease may spread. We then simulate several control strategies: screening, vaccination and behavioural interventions. Previous theory and research has advanced the importance of core groups for spread and control of STD. Our work is consistent with the importance of core groups, but extends this idea to consider how the duration of infectiousness associated with a particular pathogen interacts with host behaviours to define these high risk subpopulations. Characteristics of the parts of the network accessible to the pathogen, which represent the network structure of sexual contacts from the “point of view” of the pathogen, are substantially different from those of the network as a whole. The pathogen itself plays an important role in determining this effective network structure; specifically, we find that if the pathogen’s duration of infectiousness is short, infection is more concentrated in high-activity, high-concurrency individuals even when all other factors are held constant. Widespread screening programmes would be enhanced by follow-up interventions targeting higher-risk individuals, because screening shortens the expected duration of infectiousness and causes a greater relative decrease in prevalence among lower-activity than in higher-activity individuals. Even for pathogens with longer durations of infectiousness, our findings suggest that targeting vaccination and behavioural interventions towards high-activity individuals provides comparable benefits to population-wide interventions.     

The multifunctional nature of mitochondrial contact site proteins

Mitochondria make physical contact with nearly every other membrane in the cell, and these contacts have a wide variety of functions that are carried out by proteins that reside at the sites of contact. Over the past decade, tremendous insight into the identity and functions of proteins localized to mitochondrial contact sites has been gained. In doing so, it has become clear that one protein or protein complex can contribute to contact site formation and function in a wide variety of ways. Thus, complex and often surprising relationships between the roles of a mitochondrial contact site and its multifunctional resident proteins continue to be unraveled.