Stochasticity in pandemic spread over the World Airline Network explained by local flight connections

Massive growth in human mobility has dramatically increased the risk and rate of pandemic spread. Macro-level descriptors of the topology of the World Airline Network (WAN) explains middle and late stage dynamics of pandemic spread mediated by this network, but necessarily regard early stage variation as stochastic. We propose that much of early stage variation can be explained by appropriately characterizing the local topology surrounding the debut location of an outbreak. We measure for each airport the expected force of infection (AEF) which a pandemic originating at that airport would generate. We observe, for a subset of world airports, the minimum transmission rate at which a disease becomes pandemically competent at each airport. We also observe, for a larger subset, the time until a pandemically competent outbreak achieves pandemic status given its debut location. Observations are generated using a highly sophisticated metapopulation reaction-diffusion simulator under a disease model known to well replicate the 2009 influenza pandemic. The robustness of the AEF measure to model misspecification is examined by degrading the network model. AEF powerfully explains pandemic risk, showing correlation of 0.90 to the transmission level needed to give a disease pandemic competence, and correlation of 0.85 to the delay until an outbreak becomes a pandemic. The AEF is robust to model misspecification. For 97% of airports, removing 15% of airports from the model changes their AEF metric by less than 1%. Appropriately summarizing the size, shape, and diversity of an airport's local neighborhood in the WAN accurately explains much of the macro-level stochasticity in pandemic outcomes.Comment: article text: 6 pages, 5 figures, 28 reference

Understanding the spreading power of all nodes in a network: a continuous-time perspective

Centrality measures such as the degree, k-shell, or eigenvalue centrality can identify a network's most influential nodes, but are rarely usefully accurate in quantifying the spreading power of the vast majority of nodes which are not highly influential. The spreading power of all network nodes is better explained by considering, from a continuous-time epidemiological perspective, the distribution of the force of infection each node generates. The resulting metric, the \textit{expected force}, accurately quantifies node spreading power under all primary epidemiological models across a wide range of archetypical human contact networks. When node power is low, influence is a function of neighbor degree. As power increases, a node's own degree becomes more important. The strength of this relationship is modulated by network structure, being more pronounced in narrow, dense networks typical of social networking and weakening in broader, looser association networks such as the Internet. The expected force can be computed independently for individual nodes, making it applicable for networks whose adjacency matrix is dynamic, not well specified, or overwhelmingly large

Morphological correlates to cognitive dysfunction in schizophrenia as studied with Bayesian regression

BACKGROUND: Relationships between cognitive deficits and brain morphological changes observed in schizophrenia are alternately explained by less gray matter in the brain cerebral cortex, by alterations in neural circuitry involving the basal ganglia, and by alteration in cerebellar structures and related neural circuitry. This work explored a model encompassing all of these possibilities to identify the strongest morphological relationships to cognitive skill in schizophrenia. METHODS: Seventy-one patients with schizophrenia and sixty-five healthy control subjects were characterized by neuropsychological tests covering six functional domains. Measures of sixteen brain morphological structures were taken using semi-automatic and fully manual tracing of MRI images, with the full set of measures completed on thirty of the patients and twenty controls. Group differences were calculated. A Bayesian decision-theoretic method identified those morphological features, which best explained neuropsychological test scores in the context of a multivariate response linear model with interactions. RESULTS: Patients performed significantly worse on all neuropsychological tests except some regarding executive function. The most prominent morphological observations were enlarged ventricles, reduced posterior superior vermis gray matter volumes, and increased putamen gray matter volumes in the patients. The Bayesian method associated putamen volumes with verbal learning, vigilance, and (to a lesser extent) executive function, while caudate volumes were associated with working memory. Vermis regions were associated with vigilance, executive function, and, less strongly, visuo-motor speed. Ventricular volume was strongly associated with visuo-motor speed, vocabulary, and executive function. Those neuropsychological tests, which were strongly associated to ventricular volume, showed only weak association to diagnosis, possibly because ventricular volume was regarded a proxy for diagnosis. Diagnosis was strongly associated with the other neuropsychological tests, implying that the morphological associations for these tasks reflected morphological effects and not merely group volumetric differences. Interaction effects were rarely associated, indicating that volumetric relationships to neuropsychological performance were similar for both patients and controls. CONCLUSION: The association of subcortical and cerebellar structures to verbal learning, vigilance, and working memory supports the importance of neural connectivity to these functions. The finding that a morphological indicator of diagnosis (ventricular volume) provided more explanatory power than diagnosis itself for visuo-motor speed, vocabulary, and executive function suggests that volumetric abnormalities in the disease are more important for cognition than non-morphological features

Machine learning as a statistical tool in schizophrenia research

Schizophrenia is a heterogeneous and multi-factored disease. Investigation of the disorder could profit from statistical methods which can address multiple putative factors and large, complex datasets. Machine learning is a branch of statistical analysis which has specialized in developing such methods. This dissertation contains four investigations of schizophrenia, each highlighting a different aspect of how machine learning can address topical questions in schizophrenia research. The first study, "Potential genetic variants in schizophrenia: A Bayesian analysis," tested 36 candidate genetic loci to identify those which associated with increased risk of schizophrenia. Genetic effect sizes are small, requiring large samples to detect. Yet certain potentially interesting genetic variants are rare, making collecting such samples difficult. Early selection of genes worth further pursuit can save much wasted time and effort. Six loci were indicated. The second study, "Morphological correlates to cognitive dysfunction in schizophrenia as studied with Bayesian regression," compared a set of brain morphological measures to identify those which best explained cognitive skill scores. Measures included volumes of cortical, subcortical, and cerebellar structure selected to reflect conflicting models of the morphological substrates of cognition and cognitive deficit in schizophrenia. It found that subcortical and cerebellar structures better explained cognitive skill than cortical structures. The third study, "Investigating possible subtypes of schizophrenia pa- tients and controls based on brain cortical thickness," searched for cortical regions which showed evidence of morphologically distinguishable subtypes. The clinical heterogeneity of schizophrenia suggests that many disease factors may lead to morphologically distinguishable subtypes in patients. The same method applied to a mixed sample of case and control subjects provided a non-parametric investigation of cortical thickness variation in the disease. Morphological subtypes were not found in the patients. One third of the cortex was found to have two distinguishable types when patients and healthy control subjects were examined together. The fourth study, "Grey and white matter proportional relationships in the cerebellar vermis altered in schizophrenia," hypothesized that proportional relationships between grey and white matter tissue volumes in the vermis would be strong in healthy control subjects and weakened in patients, reflecting an optimum balance dictated by contrasting biological constraints and disturbed in the disease. This was found to be the case, suggesting an alternate model for vermis neuropathology in schizophrenia. These studies show that machine learning can identify promising avenues for further exploration, discern among overlapping hypotheses, elucidate the structure of the data, and allow the formulation of novel hypotheses based on the structure of the data

Additional file 2 of Measuring the potential of individual airports for pandemic spread over the world airline network

Airport AEF values. This CSV file gives the AEF of the airports as calculated and used in the current study. Airports are indexed by IATA code, and also by city and country. AEF values are normalized to the range 0,100. (CSV 131 kb

Additional file 1 of Measuring the potential of individual airports for pandemic spread over the world airline network

Supplementary figures. This supplement presents figures which further explore topics raised in the main text. (PDF 878 kb

Hive plot of a putative HIV transmission cluster

<p>The hive plot(s) of the transmission network. Learn how we made the plot at www.scipirate.com//?p=2197</p