Sensitivity analysis of a branching process evolving on a network with application in epidemiology

We perform an analytical sensitivity analysis for a model of a continuous-time branching process evolving on a fixed network. This allows us to determine the relative importance of the model parameters to the growth of the population on the network. We then apply our results to the early stages of an influenza-like epidemic spreading among a set of cities connected by air routes in the United States. We also consider vaccination and analyze the sensitivity of the total size of the epidemic with respect to the fraction of vaccinated people. Our analysis shows that the epidemic growth is more sensitive with respect to transmission rates within cities than travel rates between cities. More generally, we highlight the fact that branching processes offer a powerful stochastic modeling tool with analytical formulas for sensitivity which are easy to use in practice.Comment: 17 pages (30 with SI), Journal of Complex Networks, Feb 201

Inferring collective dynamical states from widely unobserved systems

When assessing spatially-extended complex systems, one can rarely sample the states of all components. We show that this spatial subsampling typically leads to severe underestimation of the risk of instability in systems with propagating events. We derive a subsampling-invariant estimator, and demonstrate that it correctly infers the infectiousness of various diseases under subsampling, making it particularly useful in countries with unreliable case reports. In neuroscience, recordings are strongly limited by subsampling. Here, the subsampling-invariant estimator allows to revisit two prominent hypotheses about the brain's collective spiking dynamics: asynchronous-irregular or critical. We identify consistently for rat, cat and monkey a state that combines features of both and allows input to reverberate in the network for hundreds of milliseconds. Overall, owing to its ready applicability, the novel estimator paves the way to novel insight for the study of spatially-extended dynamical systems.Comment: 7 pages + 12 pages supplementary information + 7 supplementary figures. Title changed to match journal referenc

The Impact of Anthropologically Motivated Human Social Networks on the Transmission Dynamics of Infectious Disease

abstract: Understanding the consequences of changes in social networks is an important an- thropological research goal. This dissertation looks at the role of data-driven social networks on infectious disease transmission and evolution. The dissertation has two projects. The first project is an examination of the effects of the superspreading phenomenon, wherein a relatively few individuals are responsible for a dispropor- tionate number of secondary cases, on the patterns of an infectious disease. The second project examines the timing of the initial introduction of tuberculosis (TB) to the human population. The results suggest that TB has a long evolutionary history with hunter-gatherers. Both of these projects demonstrate the consequences of social networks for infectious disease transmission and evolution. The introductory chapter provides a review of social network-based studies in an- thropology and epidemiology. Particular emphasis is paid to the concept and models of superspreading and why to consider it, as this is central to the discussion in chapter 2. The introductory chapter also reviews relevant epidemic mathematical modeling studies. In chapter 2, social networks are connected with superspreading events, followed by an investigation of how social networks can provide greater understanding of in- fectious disease transmission through mathematical models. Using the example of SARS, the research shows how heterogeneity in transmission rate impacts super- spreading which, in turn, can change epidemiological inference on model parameters for an epidemic. Chapter 3 uses a different mathematical model to investigate the evolution of TB in hunter-gatherers. The underlying question is the timing of the introduction of TB to the human population. Chapter 3 finds that TB’s long latent period is consistent with the evolutionary pressure which would be exerted by transmission on a hunter- igatherer social network. Evidence of a long coevolution with humans indicates an early introduction of TB to the human population. Both of the projects in this dissertation are demonstrations of the impact of var- ious characteristics and types of social networks on infectious disease transmission dynamics. The projects together force epidemiologists to think about networks and their context in nontraditional ways.Dissertation/ThesisDoctoral Dissertation Anthropology 201

Prediction of susceptibility to major depression by a model of interactions of multiple functional genetic variants and environmental factors

Major depressive disorder (MDD) is the most common psychiatric disorder and the second overall cause of disability. Even though a significant amount of the variance in the MDD phenotype is explained by inheritance, specific genetic variants conferring susceptibility to MDD explain only a minimal proportion of MDD causality. Moreover, genome-wide association studies have only identified two small-sized effect loci that reach genome-wide significance. In this study, a group of Mexican-American patients with MDD and controls recruited for a pharmacogenetic study were genotyped for nonsynonymous single-nucleotide polymorphisms (nsSNPs) and used to explore the interactions of multiple functional genetic variants with risk-classification tree analysis. The risk-classification tree analysis model and linkage disequilibrium blocks were used to replicate exploratory findings in the database of genotypes and phenotypes (dbGaP) for major depression, and pathway analysis was performed to explore potential biological mechanisms using the branching events. In exploratory analyses, we found that risk-classification tree analysis, using 15 nsSNPs that had a nominal association with MDD diagnosis, identified multiple increased-MDD genotype clusters and significant additive interactions in combinations of genotype variants that were significantly associated with MDD. The results in the dbGaP for major depression disclosed a multidimensional dependent phenotype constituted of MDD plus significant modifiers (smoking, marriage status, age, alcohol abuse/dependence and gender), which then was used for the association tree analysis. The reconstructed tree analysis for the dbGaP data showed robust reliability and replicated most of the genes involved in the branching process found in our exploratory analyses. Pathway analysis using all six major events of branching (PSMD9, HSD3B1, BDNF, GHRHR, PDE6C and PDLIM5) was significant for positive regulation of cellular and biological processes that are relevant to growth and organ development. Our findings not only provide important insights into the biological pathways underlying innate susceptibility to MDD but also offer a predictive framework based on interactions of multiple functional genetic variants and environmental factors. These findings identify novel targets for therapeutics and for translation into preventive, clinical and personalized health care

HIV-TRACE (Transmission Cluster Engine):A tool for large scale molecular epidemiology of HIV-1 and other rapidly evolving pathogens

In modern applications of molecular epidemiology, genetic sequence data are routinely used to identify clusters of transmission in rapidly evolving pathogens, most notably HIV-1. Traditional 'shoe-leather' epidemiology infers transmission clusters by tracing chains of partners sharing epidemiological connections (e.g., sexual contact). Here, we present a computational tool for identifying a molecular transmission analog of such clusters: HIV-TRACE (TRAnsmission Cluster Engine). HIV-TRACE implements an approach inspired by traditional epidemiology, by identifying chains of partners whose viral genetic relatedness imply direct or indirect epidemiological connections. Molecular transmission clusters are constructed using codon-aware pairwise alignment to a reference sequence followed by pairwise genetic distance estimation among all sequences. This approach is computationally tractable and is capable of identifying HIV-1 transmission clusters in large surveillance databases comprising tens or hundreds of thousands of sequences in near real time, that is, on the order of minutes to hours. HIV-TRACE is available at www.hivtrace.org and from www.github.com/veg/hivtrace, along with the accompanying result visualization module from www.github.com/veg/hivtrace-viz. Importantly, the approach underlying HIV-TRACE is not limited to the study of HIV-1 and can be applied to study outbreaks and epidemics of other rapidly evolving pathogens

A survey of statistical network models

Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active network community and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online networking communities such as Facebook, MySpace, and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data. Our goal in this review is to provide the reader with an entry point to this burgeoning literature. We begin with an overview of the historical development of statistical network modeling and then we introduce a number of examples that have been studied in the network literature. Our subsequent discussion focuses on a number of prominent static and dynamic network models and their interconnections. We emphasize formal model descriptions, and pay special attention to the interpretation of parameters and their estimation. We end with a description of some open problems and challenges for machine learning and statistics.Comment: 96 pages, 14 figures, 333 reference

Evolution towards Multi-Year Periodicity in Epidemics

We studied why many diseases has multi-year period in their epidemiological dynamics, whereas a main source of the fluctuation is a seasonality with period of one year. Previous studies using a compartment model succeeded to generate a multi-year epidemics when they have a large seasonal difference in a transmission rate. However, those studies have focused on the dynamical consequence of seasonal forcing in epidemiological dynamics and an adaptation of pathogens in the seasonal environment has been neglected. In this paper, we describe our study of the evolution of pathogens sensitivity to seasonality and show that a larger fluctuation in the transmission rate can be favored in the life history evolution of pathogens, suggesting that multi-year periodicity may evolve by natural selection. Our result Our result proposes a new aspect of the evolution of multi-year epidemics