Monitoring Linked Epidemics: The Case of Tuberculosis and HIV

Background: The tight epidemiological coupling between HIV and its associated opportunistic infections leads to challenges and opportunities for disease surveillance. Methodology/Principal Findings: We review efforts of WHO and collaborating agencies to track and fight the TB/HIV coepidemic, and discuss modeling—via mathematical, statistical, and computational approaches—as a means to identify disease indicators designed to integrate data from linked diseases in order to characterize how co-epidemics change in time and space. We present RTB/HIV, an index comparing changes in TB incidence relative to HIV prevalence, and use it to identify those sub-Saharan African countries with outlier TB/HIV dynamics. R TB/HIV can also be used to predict epidemiological trends, investigate the coherency of reported trends, and cross-check the anticipated impact of public health interventions. Identifying the cause(s) responsible for anomalous RTB/HIV values can reveal information crucial to the management of public health. Conclusions/Significance: We frame our suggestions for integrating and analyzing co-epidemic data within the context of global disease monitoring. Used routinely, joint disease indicators such as RTB/HIV could greatly enhance the monitoring an

Beyond multimorbidity:What can we learn from complexity science?

Multimorbidity - the occurrence of two or more long-term conditions in an individual - is a major global concern, placing a huge burden on healthcare systems, physicians, and patients. It challenges the current biomedical paradigm, in particular conventional evidence-based medicine's dominant focus on single-conditions. Patients' heterogeneous range of clinical presentations tend to escape characterization by traditional means of classification, and optimal management cannot be deduced from clinical practice guidelines. In this article, we argue that person-focused care based in complexity science may be a transformational lens through which to view multimorbidity, to complement the specialism focus on each particular disease. The approach offers an integrated and coherent perspective on the person's living environment, relationships, somatic, emotional and cognitive experiences and physiological function. The underlying principles include non-linearity, tipping points, emergence, importance of initial conditions, contextual factors and co-evolution, and the presence of patterned outcomes. From a clinical perspective, complexity science has important implications at the theoretical, practice and policy levels. Three essential questions emerge: (1) What matters to patients? (2) How can we integrate, personalize and prioritize care for whole people, given the constraints of their socio-ecological circumstances? (3) What needs to change at the practice and policy levels to deliver what matters to patients? These questions have no simple answers, but complexity science principles suggest a way to integrate understanding of biological, biographical and contextual factors, to guide an integrated approach to the care of people with multimorbidity

The Iterative Signature Algorithm for the analysis of large scale gene expression data

We present a new approach for the analysis of genome-wide expression data. Our method is designed to overcome the limitations of traditional techniques, when applied to large-scale data. Rather than alloting each gene to a single cluster, we assign both genes and conditions to context-dependent and potentially overlapping transcription modules. We provide a rigorous definition of a transcription module as the object to be retrieved from the expression data. An efficient algorithm, that searches for the modules encoded in the data by iteratively refining sets of genes and conditions until they match this definition, is established. Each iteration involves a linear map, induced by the normalized expression matrix, followed by the application of a threshold function. We argue that our method is in fact a generalization of Singular Value Decomposition, which corresponds to the special case where no threshold is applied. We show analytically that for noisy expression data our approach leads to better classification due to the implementation of the threshold. This result is confirmed by numerical analyses based on in-silico expression data. We discuss briefly results obtained by applying our algorithm to expression data from the yeast S. cerevisiae.Comment: Latex, 36 pages, 8 figure

Planning and Leveraging Event Portfolios: Towards a Holistic Theory

This conceptual paper seeks to advance the discourse on the leveraging and legacies of events by examining the planning, management, and leveraging of event portfolios. This examination shifts the common focus from analyzing single events towards multiple events and purposes that can enable cross-leveraging among different events in pursuit of attainment and magnification of specific ends. The following frameworks are proposed: (1) event portfolio planning and leveraging, and (2) analyzing events networks and inter-organizational linkages. These frameworks are intended to provide, at this infancy stage of event portfolios research, a solid ground for building theory on the management of different types and scales of events within the context of a portfolio aimed to obtain, optimize and sustain tourism, as well as broader community benefits

Disease and the Dynamics of Food Webs

What models and statistical tools can best help us assess how ecosystems respond to the impact of multiple factors, such as disease, predation, fire, and rain

How predation and landscape fragmentation affect vole population dynamics

Background: Microtine species in Fennoscandia display a distinct north-south gradient from regular cycles to stable populations. The gradient has often been attributed to changes in the interactions between microtines and their predators. Although the spatial structure of the environment is known to influence predator-prey dynamics of a wide range of species, it has scarcely been considered in relation to the Fennoscandian gradient. Furthermore, the length of microtine breeding season also displays a north-south gradient. However, little consideration has been given to its role in shaping or generating population cycles. Because these factors covary along the gradient it is difficult to distinguish their effects experimentally in the field. The distinction is here attempted using realistic agent-based modelling. Methodology/Principal Findings: By using a spatially explicit computer simulation model based on behavioural and ecological data from the field vole (Microtus agrestis), we generated a number of repeated time series of vole densities whose mean population size and amplitude were measured. Subsequently, these time series were subjected to statistical autoregressive modelling, to investigate the effects on vole population dynamics of making predators more specialised, of altering the breeding season, and increasing the level of habitat fragmentation. We found that fragmentation as well as the presence of specialist predators are necessary for the occurrence of population cycles. Habitat fragmentation and predator assembly jointly determined cycle length and amplitude. Length of vole breeding season had little impact on the oscillations. Significance: There is good agreement between our results and the experimental work from Fennoscandia, but our results allow distinction of causation that is hard to unravel in field experiments. We hope our results will help understand the reasons for cycle gradients observed in other areas. Our results clearly demonstrate the importance of landscape fragmentation for population cycling and we recommend that the degree of fragmentation be more fully considered in future analyses of vole dynamics

Prospects for Advancing Tuberculosis Control Efforts through Novel Therapies

BACKGROUND: Development of new, effective, and affordable tuberculosis (TB) therapies has been identified as a critical priority for global TB control. As new candidates emerge from the global TB drug pipeline, the potential impacts of novel, shorter regimens on TB incidence and mortality have not yet been examined. METHODS AND FINDINGS: We used a mathematical model of TB to evaluate the expected benefits of shortening the duration of effective chemotherapy for active pulmonary TB. First, we considered general relationships between treatment duration and TB dynamics. Next, as a specific example, we calibrated the model to reflect the current situation in the South-East Asia region. We found that even with continued and rapid progress in scaling up the World Health Organization's DOTS strategy of directly observed, short-course chemotherapy, the benefits of reducing treatment duration would be substantial. Compared to a baseline of continuing DOTS coverage at current levels, and with currently available tools, a 2-mo regimen introduced by 2012 could prevent around 20% (range 13%–28%) of new cases and 25% (range 19%–29%) of TB deaths in South-East Asia between 2012 and 2030. If effective treatment with existing drugs expands rapidly, overall incremental benefits of shorter regimens would be lower, but would remain considerable (13% [range 8%–19%] and 19% [range 15%–23%] reductions in incidence and mortality, respectively, between 2012 and 2030). A ten-year delay in the introduction of new drugs would erase nearly three-fourths of the total expected benefits in this region through 2030. CONCLUSIONS: The introduction of new, shorter treatment regimens could dramatically accelerate the reductions in TB incidence and mortality that are expected under current regimens—with up to 2- or 3-fold increases in rates of decline if shorter regimens are accompanied by enhanced case detection. Continued progress in reducing the global TB burden will require a balanced approach to pursuing new technologies while promoting wider implementation of proven strategies

Robust Detection of Hierarchical Communities from Escherichia coli Gene Expression Data

Determining the functional structure of biological networks is a central goal of systems biology. One approach is to analyze gene expression data to infer a network of gene interactions on the basis of their correlated responses to environmental and genetic perturbations. The inferred network can then be analyzed to identify functional communities. However, commonly used algorithms can yield unreliable results due to experimental noise, algorithmic stochasticity, and the influence of arbitrarily chosen parameter values. Furthermore, the results obtained typically provide only a simplistic view of the network partitioned into disjoint communities and provide no information of the relationship between communities. Here, we present methods to robustly detect coregulated and functionally enriched gene communities and demonstrate their application and validity for Escherichia coli gene expression data. Applying a recently developed community detection algorithm to the network of interactions identified with the context likelihood of relatedness (CLR) method, we show that a hierarchy of network communities can be identified. These communities significantly enrich for gene ontology (GO) terms, consistent with them representing biologically meaningful groups. Further, analysis of the most significantly enriched communities identified several candidate new regulatory interactions. The robustness of our methods is demonstrated by showing that a core set of functional communities is reliably found when artificial noise, modeling experimental noise, is added to the data. We find that noise mainly acts conservatively, increasing the relatedness required for a network link to be reliably assigned and decreasing the size of the core communities, rather than causing association of genes into new communities.Comment: Due to appear in PLoS Computational Biology. Supplementary Figure S1 was not uploaded but is available by contacting the author. 27 pages, 5 figures, 15 supplementary file