Using mobile phone data to predict the spatial spread of cholera.
Using mobile phone data to predict the spatial spread of cholera.
复制标题
利用手机数据预测霍乱的空间传播
DOI：
10.1038/srep08923
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发表时间：
2015-03-09
影响因子：
4.6
通讯作者：
Piarroux R
中科院分区：
文献类型：
--
作者：
Bengtsson L;Gaudart J;Lu X;Moore S;Wetter E;Sallah K;Rebaudet S;Piarroux R
要有效应对传染病流行，就必须在预计新疫情高风险地区采取重点控制措施。我们的目的是测试是否移动的运营商的数据可以预测2010年海地霍乱疫情的早期空间演变。分析了2010年10月16日至12月16日78个研究区域的每日病例数据。290万张匿名移动的手机SIM卡的移动被用于创建一个全国移动网络。实施了两个人口流动重力模型进行比较。两者都是基于完整的回顾性流行病数据进行优化的，只有在流行病传播结束后才可用。一个地区在7天内发生疫情的风险与基于移动的电话的传染压力估计值显示出很强的剂量-反应关系。基于移动的手机的模型（AUC 0.79）比回顾性优化的重力模型（AUC分别为0.66和0.74）表现更好。疫情暴发时的传染压力与疫情暴发前10天报告的霍乱病例数显著相关（p < 0.05）。移动的运营商数据是一个非常有前途的数据来源，可用于改进霍乱爆发期间的准备和应对工作。这些发现对于遏制包括高死亡率流感病毒株在内的新出现的传染病的努力可能特别重要。
Effective response to infectious disease epidemics requires focused control measures in areas predicted to be at high risk of new outbreaks. We aimed to test whether mobile operator data could predict the early spatial evolution of the 2010 Haiti cholera epidemic. Daily case data were analysed for 78 study areas from October 16 to December 16, 2010. Movements of 2.9 million anonymous mobile phone SIM cards were used to create a national mobility network. Two gravity models of population mobility were implemented for comparison. Both were optimized based on the complete retrospective epidemic data, available only after the end of the epidemic spread. Risk of an area experiencing an outbreak within seven days showed strong dose-response relationship with the mobile phone-based infectious pressure estimates. The mobile phone-based model performed better (AUC 0.79) than the retrospectively optimized gravity models (AUC 0.66 and 0.74, respectively). Infectious pressure at outbreak onset was significantly correlated with reported cholera cases during the first ten days of the epidemic (p < 0.05). Mobile operator data is a highly promising data source for improving preparedness and response efforts during cholera outbreaks. Findings may be particularly important for containment efforts of emerging infectious diseases, including high-mortality influenza strains.
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DOI：
10.1073/pnas.0906910106
发表时间：
2009-12-22
影响因子：
11.1
作者：
Balcan, Duygu;Colizza, Vittoria;Vespignani, Alessandro
通讯作者：
Vespignani, Alessandro
DOI：
10.1126/science.1210554
发表时间：
2011-12-09
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Bharti N;Tatem AJ;Ferrari MJ;Grais RF;Djibo A;Grenfell BT
通讯作者：
Grenfell BT
影响因子：
4.6
作者：
Le Menach, Arnaud;Tatem, Andrew J.;Cohen, Justin M.;Hay, Simon I.;Randell, Heather;Patil, Anand P.;Smith, David L.
通讯作者：
Smith, David L.
DOI：
10.1073/pnas.1102149108
发表时间：
2011-04-26
影响因子：
11.1
作者：
Chao, Dennis L.;Halloran, M. Elizabeth;Longini, Ira M., Jr.
通讯作者：
Longini, Ira M., Jr.
DOI：
10.1073/pnas.1203882109
发表时间：
2012-07-17
影响因子：
11.1
作者：
Lu, Xin;Bengtsson, Linus;Holme, Petter
通讯作者：
Holme, Petter