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Population flow drives spatio-temporal distribution of COVID-19 in China 期刊论文
NATURE, 2020
作者:  Fernandez, Diego Carlos;  Komal, Ruchi;  Langel, Jennifer;  Ma, Jun;  Duy, Phan Q.;  Penzo, Mario A.;  Zhao, Haiqing;  Hattar, Samer
收藏  |  浏览/下载:85/0  |  提交时间:2020/07/03

Sudden, large-scale and diffuse human migration can amplify localized outbreaks of disease into widespread epidemics(1-4). Rapid and accurate tracking of aggregate population flows may therefore be epidemiologically informative. Here we use 11,478,484 counts of mobile phone data from individuals leaving or transiting through the prefecture of Wuhan between 1 January and 24 January 2020 as they moved to 296 prefectures throughout mainland China. First, we document the efficacy of quarantine in ceasing movement. Second, we show that the distribution of population outflow from Wuhan accurately predicts the relative frequency and geographical distribution of infections with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) until 19 February 2020, across mainland China. Third, we develop a spatio-temporal '  risk source'  model that leverages population flow data (which operationalize the risk that emanates from epidemic epicentres) not only to forecast the distribution of confirmed cases, but also to identify regions that have a high risk of transmission at an early stage. Fourth, we use this risk source model to statistically derive the geographical spread of COVID-19 and the growth pattern based on the population outflow from Wuhan  the model yields a benchmark trend and an index for assessing the risk of community transmission of COVID-19 over time for different locations. This approach can be used by policy-makers in any nation with available data to make rapid and accurate risk assessments and to plan the allocation of limited resources ahead of ongoing outbreaks.


Modelling of population flows in China enables the forecasting of the distribution of confirmed cases of COVID-19 and the identification of areas at high risk of SARS-CoV-2 transmission at an early stage.


  
Rainfall monitoring network design using conditioned Latin hypercube sampling and satellite precipitation estimates: An application in the ungauged Ecuadorian Amazon 期刊论文
INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2019, 39 (4) : 2209-2226
作者:  Contreras, Juan;  Ballari, Daniela;  de Bruin, Sytze;  Samaniego, Esteban
收藏  |  浏览/下载:15/0  |  提交时间:2019/11/26
conditioned Latin hypercube sampling  Ecuadorian Amazon  poorly accessible areas  rain gauge network  satellite precipitation data  spatio-temporal monitoring  ungauged areas  
Multi-level emulation of complex climate model responses to boundary forcing data 期刊论文
CLIMATE DYNAMICS, 2019, 52: 1505-1531
作者:  Tran, Giang T.;  Oliver, Kevin I. C.;  Holden, Philip B.;  Edwards, Neil R.;  Sobester, Andras;  Challenor, Peter
收藏  |  浏览/下载:14/0  |  提交时间:2019/04/09
Probabilistic prediction  Multi-level emulators  Model hierarchy  Spatio-temporal data  Intermediate complexity model  
Long-term rainfall regression surfaces for the Kruger National Park, South Africa: a spatio-temporal review of patterns from 1981 to 2015 期刊论文
INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2018, 38 (5) : 2506-2519
作者:  MacFadyen, Sandra;  Zambatis, Nick;  Van Teeffelen, Astrid J. A.;  Hui, Cang
收藏  |  浏览/下载:14/0  |  提交时间:2019/04/09
rainfall  protected area  spatio-temporal  generalized additive mixed effects model  seasonality  change  free raster data set