The application of statistical network models in disease research

Methods in Ecology and Evolution, Год журнала: 2017, Номер 8(9), С. 1026 - 1041

Опубликована: Март 11, 2017

Summary Host social structure is fundamental to how infections spread and persist, so the statistical modelling of static dynamic networks provides an invaluable tool parameterise realistic epidemiological models. We present a practical guide application network frameworks for hypothesis testing related interactions epidemiology, illustrating some approaches with worked examples using data from population wild European badgers Meles meles naturally infected bovine tuberculosis. Different empirical datasets generate particular issues non‐independence sampling constraints. therefore discuss strengths weaknesses different types answering questions relating disease transmission. argue that designed specifically analysis offer great potential in directly infection. They have be powerful tools analysing contact used studies, but remain untested use spatio‐temporal associations. As result, we developments are critical given ready availability bio‐logging studies. Furthermore, encourage improved integration into research facilitate generation novel help extend our understanding transmission natural populations.

Язык: Английский

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Exponential-Family Models of Random Graphs: Inference in Finite, Super and Infinite Population Scenarios

Statistical Science, Год журнала: 2020, Номер 35(4)

Опубликована: Ноя. 1, 2020

Exponential-family Random Graph Models (ERGMs) constitute a large statistical framework for modeling dense and sparse random graphs with short- or long-tailed degree distributions, covariate effects wide range of complex dependencies. Special cases ERGMs include network equivalents generalized linear models (GLMs), Bernoulli graphs, $\beta $-models, $p_{1}$-models related to Markov fields in spatial statistics image processing. While are widely used practice, questions have been raised about their theoretical properties. These concerns that some near-degenerate many non-projective. To address such questions, careful attention must be paid model specifications underlying assumptions, the inferential settings which employed. As we discuss, near-degeneracy can affect simplistic lacking structure, but well-posed additional structure well-behaved. Likewise, lack projectivity non-likelihood-based inference, likelihood-based inference does not require projectivity. Here, review along inference. We first clarify core notions “sample” “population” ERGM framework, separating process generates population graph from observation process. then finite, super infinite scenarios. conclude consistency results, an application human brain networks.

Язык: Английский

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Understanding the structure and determinants of intercity carbon emissions association network in China

Journal of Cleaner Production, Год журнала: 2022, Номер 352, С. 131535 - 131535

Опубликована: Март 26, 2022

Язык: Английский

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The structural change and influencing factors of carbon transfer network in global value chains

Journal of Environmental Management, Год журнала: 2022, Номер 318, С. 115558 - 115558

Опубликована: Июль 2, 2022

Язык: Английский

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A theoretical and empirical comparison of the temporal exponential random graph model and the stochastic actor-oriented model

Network Science, Год журнала: 2019, Номер 7(1), С. 20 - 51

Опубликована: Март 1, 2019

Abstract The temporal exponential random graph model (TERGM) and the stochastic actor-oriented (SAOM, e.g., SIENA) are popular models for longitudinal network analysis. We compare these theoretically, via simulation, through a real-data example in order to assess their relative strengths weaknesses. Though we do not aim make general claim about either being superior other across all specifications, highlight several theoretical differences analyst might consider find that with some two behave very similarly, while each out-predicts one more specific assumptions of respective met.

Язык: Английский

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Evolution of structural properties and its determinants of global waste paper trade network based on temporal exponential random graph models

Renewable and Sustainable Energy Reviews, Год журнала: 2021, Номер 149, С. 111402 - 111402

Опубликована: Июль 6, 2021

Язык: Английский

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The impact of the Belt and Road Initiative on the natural gas trade: A network structure dependence perspective

Energy, Год журнала: 2022, Номер 263, С. 125912 - 125912

Опубликована: Ноя. 2, 2022

Язык: Английский

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Evolutionary analysis of the global rare earth trade networks

Applied Mathematics and Computation, Год журнала: 2022, Номер 430, С. 127249 - 127249

Опубликована: Май 25, 2022

Язык: Английский

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Topological analysis, endogenous mechanisms, and supply risk propagation in the polycrystalline silicon trade dependency network

Journal of Cleaner Production, Год журнала: 2024, Номер 439, С. 140657 - 140657

Опубликована: Янв. 11, 2024

Язык: Английский

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Evolution of structural properties of the global strategic emerging industries' trade network and its determinants: An TERGM analysis

Industrial Marketing Management, Год журнала: 2024, Номер 118, С. 78 - 92

Опубликована: Фев. 23, 2024

Язык: Английский

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