There is an obvious and urgent need for novel approaches to treat infectious diseases. The use of monoclonal antibodies in therapy of infectious diseases is now experiencing renewed interest. During the last 5 years radioimmunotherapy (RIT), a modality previously developed only for cancer treatment, has been successfully adapted for the treatment of experimental fungal, bacterial, and viral infections. As our model organism for studying the efficacy, mechanisms, potential toxicity, and radioresistance to RIT, as well as for comparison of RIT with the existing antimicrobial therapies we have chosen the encapsulated yeast Cryptococcus neoformans (CN). The success of RIT approach in laboratory studies provides encouragement for feasibility of therapeutically targeting microbes with labeled antibodies. In addition, the creation of “panantibodies” for RIT which would recognize antigens shared by the whole class of pathogens such as fungi, for example, would facilitate the introduction of RIT into the clinic.

Ekaterina Dadachova

Arturo Casadevall

Cryptococcus neoformans as a Model for Radioimmunotherapy of Infections

To help understand the potential impact of bacterial coinfection during pandemic influenza periods, we undertook a far-reaching review of the existing literature to gain insights into the interaction of influenza and bacterial pathogens. Reports published between 1950 and 2006 were identified from scientific citation databases using standardized search terms. Study outcomes related to coinfection were subjected to a pooled analysis. Coinfection with influenza and bacterial pathogens occurred more frequently in pandemic compared with seasonal influenza periods. The most common bacterial coinfections with influenza virus were due to S. pneumoniae, H. influenzae, Staphylococcus spp., and Streptococcus spp. Of these, S. pneumoniae was the most common cause of bacterial coinfection with influenza and accounted for 40.8% and 16.6% of bacterial coinfections during pandemic and seasonal periods, respectively. These results suggest that bacterial pathogens will play a key role in many countries, as the H1N1(A) influenza pandemic moves forward. Given the role of bacterial coinfections during influenza epidemics and pandemics, the conduct of well-designed field evaluations of public health measures to reduce the burden of these common bacterial pathogens and influenza in at-risk populations is warranted.

Xuan-Yi Wang

Paul E. Kilgore

Kyung Ah Lim

Song-Mei Wang

Jeongseok Lee

Wei Deng

Mei-Qi Mo

Batmunkh Nyambat

Jing-Chen Ma

Michael O. Favorov

John D. Clemens

Influenza and Bacterial Pathogen Coinfections in the 20th Century

The science of networks has revolutionised research into the dynamics of interacting elements. It could be argued that epidemiology in particular has embraced the potential of network theory more than any other discipline. Here we review the growing body of research concerning the spread of infectious diseases on networks, focusing on the interplay between network theory and epidemiology. The review is split into four main sections, which examine: the types of network relevant to epidemiology; the multitude of ways these networks can be characterised; the statistical methods that can be applied to infer the epidemiological parameters on a realised network; and finally simulation and analytical methods to determine epidemic dynamics on a given network. Given the breadth of areas covered and the ever-expanding number of publications, a comprehensive review of all work is impossible. Instead, we provide a personalised overview into the areas of network epidemiology that have seen the greatest progress in recent years or have the greatest potential to provide novel insights. As such, considerable importance is placed on analytical approaches and statistical methods which are both rapidly expanding fields. Throughout this review we restrict our attention to epidemiological issues.

Leon Danon

Ashley P. Ford

Thomas House

Chris P. Jewell

Matt J. Keeling

Gareth O. Roberts

Joshua V. Ross

Matthew C. Vernon

Networks and the Epidemiology of Infectious Disease

Although the approach of contact network epidemiology has been increasing in popularity for studying transmission of infectious diseases in human populations, it has generally been an underutilized approach for investigating disease outbreaks in wildlife populations. In this paper we explore the differences between the type of data that can be collected on human and wildlife populations, provide an update on recent advances that have been made in wildlife epidemiology by using a network approach, and discuss why networks might have been underutilized and why networks could and should be used more in the future. We conclude with ideas for future directions and a call for field biologists and network modelers to engage in more cross-disciplinary collaboration.

Meggan E. Craft

Damien Caillaud

Network Models: An Underutilized Tool in  Wildlife Epidemiology?


		Understanding the scaling of transmission is critical to predicting how infectious diseases will affect populations of different sizes and densities. The two classic &#x201c;mean-field&#x201d; epidemic models&#x2014;either assuming density-dependent or frequency-dependent transmission&#x2014;make predictions that are discordant with patterns seen in either within-population dynamics or across-population comparisons.  In this paper, we propose that the source of this inconsistency lies in the greatly simplifying &#x201c;mean-field&#x201d; assumption of transmission within a fully-mixed population.  Mixing in real populations is more accurately represented by a network of contacts, with interactions and infectious contacts confined to the local social neighborhood. We use network models to show that density-dependent transmission on heterogeneous networks often leads to apparent frequency dependency in the scaling of transmission across populations of different sizes. Network-methodology allows us to reconcile seemingly conflicting patterns of within- and across-population epidemiology.

Matthew J. Ferrari

Sarah E. Perkins

Laura W. Pomeroy

Ottar N. Bj&#248;rnstad

Pathogens, Social Networks, and the Paradox of  Transmission Scaling

Epidemic percolation networks (EPNs) are directed random networks
that can be used to analyze stochastic “Susceptible-Infectious-Removed”
(SIR) and “Susceptible-Exposed-Infectious-Removed” (SEIR) epidemic
models, unifying and generalizing previous uses of networks and branching processes to analyze mass-action and network-based S(E)IR models.
This paper explains the fundamental concepts underlying the definition
and use of EPNs, using them to build intuition about the final outcomes
of epidemics. We then show how EPNs provide a novel and useful perspective on the design of vaccination strategies.

Eben Kenah

Joel C. Miller

Epidemic Percolation Networks, Epidemic Outcomes, and Interventions

Unlike local transmission of pandemic influenza A (H1N1-2009), which was frequently driven by school children, most cases identified in long-distance intranational and international travelers have been adults. The present study examines the relationship between the probability of temporary extinction and the age-dependent next-generation matrix, focusing on the impact of assortativity. Preferred mixing captures as a good approximation the assortativity of a heterogeneously mixing population. We show that the contribution of a nonmaintenance host (i.e., a host type which cannot sustain transmission on its own) to the risk of a major epidemic is greatly diminished as mixing patterns become more assortative, and in such a scenario, a higher proportion of non-maintenance hosts among index cases elevates the probability of extinction. Despite the presence of various other epidemiological factors that undoubtedly influenced the delay between first importations and the subsequent epidemic, these results suggest that the dominance of adults among imported cases represents one of the possible factors explaining the delays in geographic spread observed during the recent pandemic.

Hiroshi Nishiura

Alex R. Cook

Benjamin J. Cowling

Assortativity and the Probability of Epidemic Extinction: A Case Study of Pandemic Influenza A (H1N1-2009)

The inference of population dynamics from molecular sequence data
is becoming an important new method for the surveillance of infectious
diseases. Here, we examine how heterogeneity in contact shapes the
genealogies of parasitic agents. Using extensive simulations, we find
that contact heterogeneity can have a strong effect on how the structure
of genealogies reflects epidemiologically relevant quantities such as the
proportion of a population that is infected. Comparing the simulations
to BEAST reconstructions, we also find that contact heterogeneity can
increase the number of sequence isolates required to estimate these
quantities over the course of an epidemic. Our results suggest that
data about contact-network structure will be required in addition to
sequence data for accurate estimation of a parasitic agent's genealogy.
We conclude that network models will be important for progress in
this area.

Eamon B. O'Dea

Claus O. Wilke

Contact Heterogeneity and Phylodynamics: How Contact Networks Shape Parasite Evolutionary Trees

The connection between theory and data is an iterative one. In principle, each is informed by the other: data provide the basis for theory that in turn generates the need for new information. This circularity is reflected in the notion of abduction, a concept that focuses on the space between induction (generating theory from data) and deduction (testing theory with data). Einstein, in the 1920s, placed scientific creativity in that space. In the field of social network analysis, some remarkable theory has been developed, accompanied by sophisticated tools to develop, extend, and test the theory. At the same time, important empirical data have been generated that provide insight into transmission dynamics. Unfortunately, the connection between them is often tenuous and the iterative loop is frayed. This circumstance may arise both from data deficiencies and from the ease with which data can be created by simulation. But for whatever reason, theory and empirical data often occupy different orbits. Fortunately, the relationship, while frayed, is not broken, to which several recent analyses merging theory and extant data will attest. Their further rapprochement in the field of social network analysis could provide the field with a more creative approach to experimentation and inference.

Richard Rothenberg

Elizabeth Costenbader

Empiricism and Theorizing in Epidemiology and Social Network Analysis

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Interdisciplinary Perspectives on Infectious Diseases publishes original research articles and review articles&nbsp;related to all aspects of infectious diseases.

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Interdisciplinary Perspectives on Infectious Diseases publishes original research articles and review articles related to all aspects of infectious diseases.

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Interdisciplinary Perspectives on Infectious Diseases

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Interdisciplinary Perspectives on Infectious Diseases