Improved Response to Disasters and Outbreaks by Tracking Population Movements with Mobile Phone Network Data: A Post-Earthquake Geospatial Study in Haiti

By Linus Bengtsson, Xin Lu, A. Thorson, R. Garfield, J. von Schreeb. PLoS Medicine, 2011

615 citations recorded by the source corpus.

Linus Bengtsson and colleagues examine the use of mobile phone positioning data to monitor population movements during disasters and outbreaks, finding that reports on population movements can be generated within twelve hours of receiving data.

Subfields: Epidemiology, Organizational Behavior and Human Resource Management, Transportation

View Paper: https://www.semanticscholar.org/paper/2554d4350313a3d5106f482b94f7a18be83aebeb

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In Epidemiology

In Organizational Behavior and Human Resource Management

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Citations in the corpus

Digital Epidemiology (2012, methodology): “The observation of the spatiotemporal movements of millions of people during disease outbreaks [4], the rapid detection of an unusual respiratory illness in a remote village anywhere on the globe [5], the near real-time estimation of influenza activity levels [6,7], and the assessment of vaccination”
Mobile Crowd Sensing and Computing (2015, background): “A powerful demonstration of MCSC to disasters came after the January 2010 earthquake in Haiti [Bengtsson et al. 2011].”
On the Use of Human Mobility Proxies for Modeling Epidemics (2013, background): “They were indeed used to shed light on malaria diffusion and identify hotspot areas [24,26,27], to monitor human displacements in case of natural disasters [25,28] and to study disease containment strategies in Ivory Coast [29].”
Everything you need to know about agent-based modelling and simulation (2016, background): “Data streams from social media and sensor networks offer the possibility of extracting agent behaviours in real time (Bengtsson et al , 2011) and dynamically aligning agent states to real populations.”
The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good (2016, background): “The characterization of individuals and entire populations’ mobility is of paramount importance for public health [57]: for example, it is key to predict the spatial and temporal risk of diseases [35, 82, 94], to quantify exposure to air pollution [48], to understand human migrations after natural d”
Approaching the Limit of Predictability in Human Mobility (2013, neutral): “3A, and 1,2,17 .”
Big Data for Development: A Review of Promises and Challenges (2016, positive): “, 2014); to understand population movements following an earthquake and cholera outbreak (Bengtsson et al., 2011;, Lu et al., 2012); to study social responses to urban earthquakes in Mexico (Moumni et al.”
Mobile Health (mHealth) Approaches and Lessons for Increased Performance and Retention of Community Health Workers in Low- and Middle-Income Countries: A Review (2013, background): “Reports have described mobile technology use during natural disasters, including the earthquakes in China in 2008 and Haiti in 2010 [38,39].”
Dynamic population mapping using mobile phone data (2014, background): “Population movement analyses based on MP data are particularly promising for improving responses to disasters (47, 48) and for planning malaria elimination strategies (49 – 51).”
Applications of artificial intelligence for disaster management (2020, methodology): “Social media data embed emotional text and images, time and geo-location information, which as useful to identify the spatial and temporal evolution of public behaviors and population mobility, as well as psychological and healthcare needs (Bengtsson et al. 2011; Caragea et al. 2014; Ukkusuri et al.”
Predictability of population displacement after the 2010 Haiti earthquake (2012, background): “We have, however, shown very promising results in this area in earlier research (13).”