日期:
2020 年 — 2026 年
2020
2021
2022
2023
2024
2025
2026
影响因子:

A BAYESIAN HIERARCHICAL SMALL AREA POPULATION MODEL ACCOUNTING FOR DATA SOURCE SPECIFIC METHODOLOGIES FROM AMERICAN COMMUNITY SURVEY, POPULATION ESTIMATES PROGRAM, AND DECENNIAL CENSUS DATA

基于贝叶斯分层小区域人口模型,考虑了来自美国社区调查、人口估计计划和十年一次人口普查数据的数据源特定方法

Peterson, Emily N; Nethery, Rachel C; Padellini, Tullia; Chen, Jarvis T; Coull, Brent A; Piel, Frédéric B; Wakefield, Jon; Blangiardo, Marta; Waller, Lance A

Interoperability of statistical models in pandemic preparedness: principles and reality

疫情防范中统计模型的互操作性:原则与现实

Nicholson, George; Blangiardo, Marta; Briers, Mark; Diggle, Peter J; Fjelde, Tor Erlend; Ge, Hong; Goudie, Robert J B; Jersakova, Radka; King, Ruairidh E; Lehmann, Brieuc C L; Mallon, Ann-Marie; Padellini, Tullia; Teh, Yee Whye; Holmes, Chris; Richardson, Sylvia

Long-term exposure to air-pollution and COVID-19 mortality in England: A hierarchical spatial analysis

长期暴露于空气污染与英格兰新冠肺炎死亡率:一项分层空间分析

Konstantinoudis, Garyfallos; Padellini, Tullia; Bennett, James; Davies, Bethan; Ezzati, Majid; Blangiardo, Marta

Response to "Re: Long-term exposure to air-pollution and COVID-19 mortality in England: A hierarchical spatial analysis"

对“关于:长期暴露于空气污染与英格兰新冠肺炎死亡率:分层空间分析”的回应

Konstantinoudis, Garyfallos; Padellini, Tullia; Bennett, James; Davies, Bethan; Ezzati, Majid; Blangiardo, Marta