Bhatt S, Ferguson N, Flaxman S, Gandy A, Mishra S, Scott Ja
Abstract We propose a general Bayesian approach to modeling epidemics such as COVID-19. The approach grew out of specific analyses conducted during the pandemic, in particular an analysis concerning the effects of non-pharmaceutical interventions (NPIs) in reducing COVID-19 transmission in 11 European countries (Flaxman et al., 2020b). The model parameterizes the time varying reproduction number Rt through a multilevel regression framework in which covariates can be governmental interventions, changes in mobility patterns, or other behavioural measures. Bayesian multilevel modelling allows a joint fit across regions, with partial pooling to share strength. This innovation was critical to our timely estimates of the impact of lockdown and other NPIs in the European epidemics: estimates from countries at later stages in their epidemics informed those of countries at earlier stages. Originally released as Imperial College Report 13 Flaxman et al. (2020a) on 30 March 2020, the validity of this approach was borne out by the subsequent course of the epidemic. Our framework provides a fully generative model for latent infections and derived observations, including deaths, cases, hospitalizations, ICU admissions and seroprevalence surveys. One issue surrounding our model’s use during the COVID-19 pandemic is the confounded nature of NPIs and mobility. We explore this issue using our R package epidemia which implements the approach in Stan. Versions of our model were used in an ongoing way by New York State, Tennessee and Scotland to estimate the current epidemic situation and make policy decisions.
{"title":"Semi-Mechanistic Bayesian modeling of COVID-19 with Renewal Processes","authors":"Bhatt S, Ferguson N, Flaxman S, Gandy A, Mishra S, Scott Ja","doi":"10.1093/jrsssa/qnad030","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad030","url":null,"abstract":"Abstract We propose a general Bayesian approach to modeling epidemics such as COVID-19. The approach grew out of specific analyses conducted during the pandemic, in particular an analysis concerning the effects of non-pharmaceutical interventions (NPIs) in reducing COVID-19 transmission in 11 European countries (Flaxman et al., 2020b). The model parameterizes the time varying reproduction number Rt through a multilevel regression framework in which covariates can be governmental interventions, changes in mobility patterns, or other behavioural measures. Bayesian multilevel modelling allows a joint fit across regions, with partial pooling to share strength. This innovation was critical to our timely estimates of the impact of lockdown and other NPIs in the European epidemics: estimates from countries at later stages in their epidemics informed those of countries at earlier stages. Originally released as Imperial College Report 13 Flaxman et al. (2020a) on 30 March 2020, the validity of this approach was borne out by the subsequent course of the epidemic. Our framework provides a fully generative model for latent infections and derived observations, including deaths, cases, hospitalizations, ICU admissions and seroprevalence surveys. One issue surrounding our model’s use during the COVID-19 pandemic is the confounded nature of NPIs and mobility. We explore this issue using our R package epidemia which implements the approach in Stan. Versions of our model were used in an ongoing way by New York State, Tennessee and Scotland to estimate the current epidemic situation and make policy decisions.","PeriodicalId":49985,"journal":{"name":"Journal of the Royal Statistical Society","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135533623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaojing Zhu, Cantay Caliskan, Dino P Christenson, Konstantinos Spiliopoulos, Dylan Walker, Eric D Kolaczyk
Abstract We develop a broadly applicable class of coevolving latent space network with attractors (CLSNA) models, where nodes represent individual social actors assumed to lie in an unknown latent space, edges represent the presence of a specified interaction between actors, and attractors are added in the latent level to capture the notion of attractive and repulsive forces. We apply the CLSNA models to understand the dynamics of partisan polarization in US politics on social media, where we expect Republicans and Democrats to increasingly interact with their own party and disengage with the opposing party. Using longitudinal social networks from the social media platforms Twitter and Reddit, we quantify the relative contributions of positive (attractive) and negative (repulsive) forces among political elites and the public, respectively.
{"title":"Disentangling positive and negative partisanship in social media interactions using a coevolving latent space network with attractors model","authors":"Xiaojing Zhu, Cantay Caliskan, Dino P Christenson, Konstantinos Spiliopoulos, Dylan Walker, Eric D Kolaczyk","doi":"10.1093/jrsssa/qnad008","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad008","url":null,"abstract":"Abstract We develop a broadly applicable class of coevolving latent space network with attractors (CLSNA) models, where nodes represent individual social actors assumed to lie in an unknown latent space, edges represent the presence of a specified interaction between actors, and attractors are added in the latent level to capture the notion of attractive and repulsive forces. We apply the CLSNA models to understand the dynamics of partisan polarization in US politics on social media, where we expect Republicans and Democrats to increasingly interact with their own party and disengage with the opposing party. Using longitudinal social networks from the social media platforms Twitter and Reddit, we quantify the relative contributions of positive (attractive) and negative (repulsive) forces among political elites and the public, respectively.","PeriodicalId":49985,"journal":{"name":"Journal of the Royal Statistical Society","volume":"486 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136081690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Information about the share of total income held by the richest 1%, or other top income groups, is increasingly used to discuss inequality levels and trends within and between nations. A top income share is the ratio of the total income held by the top income group divided by total personal income (the ‘income control total’). We compare two approaches to estimating income control totals: the ‘external’ approach used by the World Inequality Database, and an augmented ‘internal’ approach. We argue in favour of the latter, with reference to five desirable properties that a top share series would ideally possess. The choice matters: our augmented ‘internal’ approach yields estimates of the UK top 1% share that are around 2% points higher than the ‘external’ approach.
{"title":"Measuring top income shares in the UK","authors":"Arun Advani, Andy Summers, Hannah Tarrant","doi":"10.1093/jrsssa/qnac008","DOIUrl":"https://doi.org/10.1093/jrsssa/qnac008","url":null,"abstract":"Abstract Information about the share of total income held by the richest 1%, or other top income groups, is increasingly used to discuss inequality levels and trends within and between nations. A top income share is the ratio of the total income held by the top income group divided by total personal income (the ‘income control total’). We compare two approaches to estimating income control totals: the ‘external’ approach used by the World Inequality Database, and an augmented ‘internal’ approach. We argue in favour of the latter, with reference to five desirable properties that a top share series would ideally possess. The choice matters: our augmented ‘internal’ approach yields estimates of the UK top 1% share that are around 2% points higher than the ‘external’ approach.","PeriodicalId":49985,"journal":{"name":"Journal of the Royal Statistical Society","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135284878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Written contribution to the Discussion of “Experimental Evaluation of Algorithm-Assisted Human Decision-Making: Application to Pretrial Public Safety Assessment” by Kosuke Imai, Zhichao Jiang, D. James Greiner, Ryan Halen and Sooahn Shin","authors":"J L Hutton","doi":"10.1093/jrsssa/qnad019","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad019","url":null,"abstract":"","PeriodicalId":49985,"journal":{"name":"Journal of the Royal Statistical Society","volume":"539 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135727309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Discussion of: “Experimental Evaluation of Algorithm-Assisted Human Decision-Making: Application to Pretrial Public Safety Assessment”","authors":"Maria Cuellar","doi":"10.1093/jrsssa/qnad011","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad011","url":null,"abstract":"","PeriodicalId":49985,"journal":{"name":"Journal of the Royal Statistical Society","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135727308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Targeting uplift: An Introduction to Net Scores","authors":"Paul Hewson","doi":"10.1093/jrsssa/qnad003","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad003","url":null,"abstract":"","PeriodicalId":49985,"journal":{"name":"Journal of the Royal Statistical Society","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136175411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Paolo Giudici, Paolo Pagnottoni, Alessandro Spelta
Abstract The assessment of the health impacts of the COVID-19 pandemic requires the consideration of mobility networks. To this aim, we propose to augment spatio-temporal point process models with mobility network covariates. We show how the resulting model can be employed to predict contagion patterns and to help in important decisions such as the distribution of vaccines. The application of the proposed methodology to 27 European countries shows that human mobility, along with vaccine doses and government policies, are significant predictors of the number of new COVID-19 reported infections and are therefore key variables for decision-making.
{"title":"Network self-exciting point processes to measure health impacts of COVID-19","authors":"Paolo Giudici, Paolo Pagnottoni, Alessandro Spelta","doi":"10.1093/jrsssa/qnac006","DOIUrl":"https://doi.org/10.1093/jrsssa/qnac006","url":null,"abstract":"Abstract The assessment of the health impacts of the COVID-19 pandemic requires the consideration of mobility networks. To this aim, we propose to augment spatio-temporal point process models with mobility network covariates. We show how the resulting model can be employed to predict contagion patterns and to help in important decisions such as the distribution of vaccines. The application of the proposed methodology to 27 European countries shows that human mobility, along with vaccine doses and government policies, are significant predictors of the number of new COVID-19 reported infections and are therefore key variables for decision-making.","PeriodicalId":49985,"journal":{"name":"Journal of the Royal Statistical Society","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135743682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Social network data are relational data recorded among a group of actors, interacting in different contexts. Often, the same set of actors can be characterised by multiple social relations, captured by a multidimensional network. A common situation is that of colleagues working in the same institution, whose social interactions can be defined on professional and personal levels. In addition, individuals in a network tend to interact more frequently with similar others, naturally creating communities. Latent space models for network data are useful to recover clustering of the actors, as they allow to represent similarities between them by their positions and relative distances in an interpretable low-dimensional social space. We propose the infinite mixture latent position cluster model for multidimensional network data, which enables model-based clustering of actors interacting across multiple social dimensions. The model is based on a Bayesian non-parametric framework that allows to perform automatic inference on the clustering allocations, the number of clusters, and the latent social space. The method is tested on extensive simulated data experiments. It is also employed to investigate the presence of communities in two multidimensional workplace social networks recording relations of different types among colleagues.
{"title":"Model-based clustering for multidimensional social networks","authors":"Silvia D’Angelo, Marco Alfò, Michael Fop","doi":"10.1093/jrsssa/qnac011","DOIUrl":"https://doi.org/10.1093/jrsssa/qnac011","url":null,"abstract":"Abstract Social network data are relational data recorded among a group of actors, interacting in different contexts. Often, the same set of actors can be characterised by multiple social relations, captured by a multidimensional network. A common situation is that of colleagues working in the same institution, whose social interactions can be defined on professional and personal levels. In addition, individuals in a network tend to interact more frequently with similar others, naturally creating communities. Latent space models for network data are useful to recover clustering of the actors, as they allow to represent similarities between them by their positions and relative distances in an interpretable low-dimensional social space. We propose the infinite mixture latent position cluster model for multidimensional network data, which enables model-based clustering of actors interacting across multiple social dimensions. The model is based on a Bayesian non-parametric framework that allows to perform automatic inference on the clustering allocations, the number of clusters, and the latent social space. The method is tested on extensive simulated data experiments. It is also employed to investigate the presence of communities in two multidimensional workplace social networks recording relations of different types among colleagues.","PeriodicalId":49985,"journal":{"name":"Journal of the Royal Statistical Society","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136117185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract We present an integrating decision support system designed to aid security analysts’ monitoring of terrorist groups. The system comprises of (i) a dynamic network model of the level of bilateral communications between individuals and (ii) dynamic graphical models of those individual’s latent threat states. These component models are combined in a statistically coherent manner to provide measures of the imminence of an attack by the terrorist group. Domain knowledge provides the structures of the models, values of parameters and prior distributions over latent variables. Inference of the values is performed using time-series of observed data and the statistical dependencies assumed between said data and model variables. The work draws on social network and graphical models used in sociological, military, and medical fields.
{"title":"A Bayesian decision support system for counteracting activities of terrorist groups","authors":"Aditi Shenvi, Francis Oliver Bunnin, Jim Q Smith","doi":"10.1093/jrsssa/qnac019","DOIUrl":"https://doi.org/10.1093/jrsssa/qnac019","url":null,"abstract":"Abstract We present an integrating decision support system designed to aid security analysts’ monitoring of terrorist groups. The system comprises of (i) a dynamic network model of the level of bilateral communications between individuals and (ii) dynamic graphical models of those individual’s latent threat states. These component models are combined in a statistically coherent manner to provide measures of the imminence of an attack by the terrorist group. Domain knowledge provides the structures of the models, values of parameters and prior distributions over latent variables. Inference of the values is performed using time-series of observed data and the statistical dependencies assumed between said data and model variables. The work draws on social network and graphical models used in sociological, military, and medical fields.","PeriodicalId":49985,"journal":{"name":"Journal of the Royal Statistical Society","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136117187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract We examine economic mobility in India while accounting for misclassification to better understand the welfare effects of the rise in inequality. To proceed, we extend recently developed methods on the partial identification of transition matrices. Allowing for modest misclassification, we find overall mobility has been remarkably low: at least 65% of poor households remained poor or at-risk of being poor between 2005 and 2012. We also find Muslims, lower caste groups, and rural households are in a more disadvantageous position compared to Hindus, upper caste groups, and urban households. These findings cast doubt on the conventional wisdom that marginalized households in India are catching up.
{"title":"Measuring economic mobility in India using noisy data: a partial identification approach","authors":"Hao Li, Daniel Millimet, Punarjit Roychowdhury","doi":"10.1093/jrsssa/qnac005","DOIUrl":"https://doi.org/10.1093/jrsssa/qnac005","url":null,"abstract":"Abstract We examine economic mobility in India while accounting for misclassification to better understand the welfare effects of the rise in inequality. To proceed, we extend recently developed methods on the partial identification of transition matrices. Allowing for modest misclassification, we find overall mobility has been remarkably low: at least 65% of poor households remained poor or at-risk of being poor between 2005 and 2012. We also find Muslims, lower caste groups, and rural households are in a more disadvantageous position compared to Hindus, upper caste groups, and urban households. These findings cast doubt on the conventional wisdom that marginalized households in India are catching up.","PeriodicalId":49985,"journal":{"name":"Journal of the Royal Statistical Society","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135948410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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