The spatial diffusion of information is a process governed by the flow of interpersonal communication.The emergence of the Internet and especially social media platforms has reshaped this process and previous research...The spatial diffusion of information is a process governed by the flow of interpersonal communication.The emergence of the Internet and especially social media platforms has reshaped this process and previous research has studied how online social networks contribute to the diffusion of information.Understanding such processes can help devise methods to maximize or control the reach of information or even identify upcoming events and social movements.Yet activities in cyberspace are still confined to physical locations and this geographic connection tends to be overlooked.In this research,we focus on geographic regions instead of individuals and study how the underlying hierarchical structure of regions relates to their response to the information.We examined the top 30 populated cities and metropolitan areas in the U.S.and retrieved Twitter data related to two selected topics from these regions,the 2015 Nepal Earthquake and the#JesuisCharlie hashtag in response to the Paris attacks on the Charlie Hebdo offices.We analyzed the similarity among regions of their response using multiple statistical methods and three urban classifications.Our results indicate that the diffusion of information is impacted by the hierarchy of urban regions and that the Twitter responses act more similar when the populated regions are positioned at the same level in the urban hierarchy.展开更多
The transmission dynamics of an epidemic are rarely homogeneous.Super-spreading events and super-spreading individuals are two types of heterogeneous transmissibility.Inference of super-spreading is commonly carried o...The transmission dynamics of an epidemic are rarely homogeneous.Super-spreading events and super-spreading individuals are two types of heterogeneous transmissibility.Inference of super-spreading is commonly carried out on secondary case data,the expected distribution of which is known as the offspring distribution.However,this data is seldom available.Here we introduce a multi-model framework fit to incidence time-series,data that is much more readily available.The framework consists of five discrete-time,stochastic,branching-process models of epidemics spread through a susceptible population.The framework includes a baseline model of homogeneous transmission,a unimodal and a bimodal model for super-spreading events,as well as a unimodal and a bimodal model for super-spreading individuals.Bayesian statistics is used to infer model parameters using Markov Chain Monte-Carlo methods.Model comparison is conducted by computing Bayes factors,with importance sampling used to estimate the marginal likelihood of each model.This estimator is selected for its consistency and lower variance compared to alternatives.Application to simulated data from each model identifies the correct model for the majority of simulations and accurately infers the true parameters,such as the basic reproduction number.We also apply our methods to incidence data from the 2003 SARS outbreak and the Covid-19 pandemic caused by SARS-CoV-2.Model selection consistently identifies the same model and mechanism for a given disease,even when using different time series.Our estimates are consistent with previous studies based on secondary case data.Quantifying the contribution of super-spreading to disease transmission has important implications for infectious disease management and control.Our modelling framework is disease-agnostic and implemented as an R package,with potential to be a valuable tool for public health.展开更多
A novel dual-mode optical vector spectrum analyzer is demonstrated that is suitable for the characterization of both passive devices as well as active laser sources.It can measure loss,phase response,and dispersion pr...A novel dual-mode optical vector spectrum analyzer is demonstrated that is suitable for the characterization of both passive devices as well as active laser sources.It can measure loss,phase response,and dispersion properties over a broad bandwidth,with high resolution and dynamic range.展开更多
文摘The spatial diffusion of information is a process governed by the flow of interpersonal communication.The emergence of the Internet and especially social media platforms has reshaped this process and previous research has studied how online social networks contribute to the diffusion of information.Understanding such processes can help devise methods to maximize or control the reach of information or even identify upcoming events and social movements.Yet activities in cyberspace are still confined to physical locations and this geographic connection tends to be overlooked.In this research,we focus on geographic regions instead of individuals and study how the underlying hierarchical structure of regions relates to their response to the information.We examined the top 30 populated cities and metropolitan areas in the U.S.and retrieved Twitter data related to two selected topics from these regions,the 2015 Nepal Earthquake and the#JesuisCharlie hashtag in response to the Paris attacks on the Charlie Hebdo offices.We analyzed the similarity among regions of their response using multiple statistical methods and three urban classifications.Our results indicate that the diffusion of information is impacted by the hierarchy of urban regions and that the Twitter responses act more similar when the populated regions are positioned at the same level in the urban hierarchy.
基金funding from the Engineering and Physical Sciences Research Council(EPSRC grant number EP/W006790/1)the Medical Research Council(MRC grant number MR/N010760/1)the National Institute for Health Research(NIHR grant number NIHR200892).
文摘The transmission dynamics of an epidemic are rarely homogeneous.Super-spreading events and super-spreading individuals are two types of heterogeneous transmissibility.Inference of super-spreading is commonly carried out on secondary case data,the expected distribution of which is known as the offspring distribution.However,this data is seldom available.Here we introduce a multi-model framework fit to incidence time-series,data that is much more readily available.The framework consists of five discrete-time,stochastic,branching-process models of epidemics spread through a susceptible population.The framework includes a baseline model of homogeneous transmission,a unimodal and a bimodal model for super-spreading events,as well as a unimodal and a bimodal model for super-spreading individuals.Bayesian statistics is used to infer model parameters using Markov Chain Monte-Carlo methods.Model comparison is conducted by computing Bayes factors,with importance sampling used to estimate the marginal likelihood of each model.This estimator is selected for its consistency and lower variance compared to alternatives.Application to simulated data from each model identifies the correct model for the majority of simulations and accurately infers the true parameters,such as the basic reproduction number.We also apply our methods to incidence data from the 2003 SARS outbreak and the Covid-19 pandemic caused by SARS-CoV-2.Model selection consistently identifies the same model and mechanism for a given disease,even when using different time series.Our estimates are consistent with previous studies based on secondary case data.Quantifying the contribution of super-spreading to disease transmission has important implications for infectious disease management and control.Our modelling framework is disease-agnostic and implemented as an R package,with potential to be a valuable tool for public health.
文摘A novel dual-mode optical vector spectrum analyzer is demonstrated that is suitable for the characterization of both passive devices as well as active laser sources.It can measure loss,phase response,and dispersion properties over a broad bandwidth,with high resolution and dynamic range.
