Estimating intervention effects on infectious disease control: The effect of community mobility reduction on Coronavirus spread [0.03%]
估计干预措施对传染病控制的影响：社区活动减少对于控制冠状病毒传播的作用估算
Andrew Giffin,Wenlong Gong,Suman Majumder et al.
Andrew Giffin et al.
Understanding the effects of interventions, such as restrictions on community and large group gatherings, is critical to controlling the spread of COVID-19. Susceptible-Infectious-Recovered (SIR) models are traditionally used to forecast th...
Bayesian negative binomial regression with spatially varying dispersion: Modeling COVID-19 incidence in Georgia [0.03%]
具有空间异质离散的贝叶斯负二项回归：佐治亚州COVID-19发病情况模型
Fedelis Mutiso,John L Pearce,Sara E Benjamin-Neelon et al.
Fedelis Mutiso et al.
Overdispersed count data arise commonly in disease mapping and infectious disease studies. Typically, the level of overdispersion is assumed to be constant over time and space. In some applications, however, this assumption is violated, and...
Modeling infectious disease dynamics: Integrating contact tracing-based stochastic compartment and spatio-temporal risk models [0.03%]
传染病动力学建模：基于接触追踪的随机隔室模型与时空风险模型集成
Mateen Mahmood,André Victor Ribeiro Amaral,Jorge Mateu et al.
Mateen Mahmood et al.
Major infectious diseases such as COVID-19 have a significant impact on population lives and put enormous pressure on healthcare systems globally. Strong interventions, such as lockdowns and social distancing measures, imposed to prevent th...
Combining school-catchment area models with geostatistical models for analysing school survey data from low-resource settings: Inferential benefits and limitations [0.03%]
结合学校招生区模型和地统计学模型分析低资源环境下学校调查数据：推断益处与局限性
Peter M Macharia,Nicolas Ray,Caroline W Gitonga et al.
Peter M Macharia et al.
School-based sampling has been used to inform targeted responses for malaria and neglected tropical diseases. Standard geostatistical methods for mapping disease prevalence use the school location to model spatial correlation, which is ques...
Community mobility in the European regions during COVID-19 pandemic: A partitioning around medoids with noise cluster based on space-time autoregressive models [0.03%]
基于时空自回归模型的噪声聚类PAM在新冠肺炎疫情期间欧洲区域社区活动中的应用研究
Pierpaolo D&#x;Urso,Massimo Mucciardi,Edoardo Otranto et al.
Pierpaolo D&#x;Urso et al.
In this paper we propose a robust fuzzy clustering model, the STAR-based Fuzzy C-Medoids Clustering model with Noise Cluster, to define territorial partitions of the European regions (NUTS2) according to the workplaces mobility trends for p...
Alfred Stein,Alan Gelfand
Alfred Stein
The Publisher regrets that this article is an accidental duplication of an article that has already been published, http://dx.doi.org/10.1016/j.spasta.2021.100588. The duplicate article has therefore been withdrawn. The...
Modeling Massive Spatial Datasets Using a Conjugate Bayesian Linear Modeling Framework [0.03%]
基于共轭贝叶斯线性模型框架的大规模空间数据建模
Sudipto Banerjee
Sudipto Banerjee
Geographic Information Systems (GIS) and related technologies have generated substantial interest among statisticians with regard to scalable methodologies for analyzing large spatial datasets. A variety of scalable spatial process models h...
Ying C MacNab
Ying C MacNab
On the occasion of the Spatial Statistics' 10th Anniversary, I reflect on the past and present of Bayesian disease mapping and look into its future. I focus on some key developments of models, and on recent evolution of multivariate and ada...
Pierpaolo D&#x;Urso,Sujit Sahu,Alfred Stein
Pierpaolo D&#x;Urso
A D-vine copula-based quantile regression model with spatial dependence for COVID-19 infection rate in Italy [0.03%]
基于Dvinecopula的考虑空间自相关的COVID-19感染率分位数回归模型
Pierpaolo D&#x;Urso,Livia De Giovanni,Vincenzina Vitale
Pierpaolo D&#x;Urso
The main determinants of COVID-19 spread in Italy are investigated, in this work, by means of a D-vine copula based quantile regression. The outcome is the COVID-19 cumulative infection rate registered on October 30th 2020, with reference t...