Quantifying the impact of human mobility on malaria

By A. Wesolowski, Nathan Eagle, Andrew J. Tatem, David L. Smith, Abdisalan M. Noor, Robert W. Snow, C. Buckee. Science, 2012

973 citations recorded by the source corpus.

Mobile Phone “Hot Spots” An obstacle to developing effective national malaria control programs is a lack of understanding of human movements, which are an important component of disease transmission. As mobile phones have become increasingly ubiquitous, it is now possible to collect individual-level, longitudinal data on human movements on a massive scale. Wesolowski et al. (p. 267) analyzed mobile phone call data records representing the travel patterns of 15 million mobile phone owners in Kenya over the course of a year. This was combined with a detailed malaria risk map, to estimate malaria parasite movements across the country that could be caused by human movement. This information enabled detailed analysis of parasite sources and sinks between hundreds of local settlements. Estimates were compared with hospital data from Nairobi to show that local pockets of transmission likely occur around the periphery of Nairobi, accounting for locally acquired cases, contrary to the accepted idea that there is no transmission in the capital. Geographical information in mobile phone records for 15 million Kenyans is linked to malaria prevalence estimates. Human movements contribute to the transmission of malaria on spatial scales that exceed the limits of mosquito dispersal. Identifying the sources and sinks of imported infections due to human travel and locating high-risk sites of parasite importation could greatly improve malaria control programs. Here, we use spatially explicit mobile phone data and malaria prevalence information from Kenya to identify the dynamics of human carriers that drive parasite importation between regions. Our analysis identifies importation routes that contribute to malaria epidemiology on regional spatial scales.

Subfields: Information Systems, Modeling and Simulation, Transportation

View Paper: https://www.semanticscholar.org/paper/2d345a375744d4d0dae25d56309a1097de3a20b4

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Citations in the corpus

On the Use of Human Mobility Proxies for Modeling Epidemics (2013, background): “If mobility models often require aggregated input data from national statistics on movement habits [18] or the full mobility census database [19] for the fitting procedure, mobile phone data may be thought as an ideal alternative candidate for a proxy of human movements in absence of (complete and/o”
Once Upon a Crime: Towards Crime Prediction from Demographics and Mobile Data (2014, background): “…and urban interactions [21, 48], to understand individual spending behaviors [33], to infer people’s traits [13, 37] and states [3], to map and model the spreading of diseases such as malaria [47] and H1N1 ﬂu [20], and to predict and understand socio-economic indicators of territories [15, 36, 34].”
Modeling infectious disease dynamics in the complex landscape of global health (2015, positive): “, mobile phone records provide unique opportunities to understand disease source-sink dynamics (52).”
The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good (2016, background): “In the rest of this chapter, we provide the readers with a compendium of the issues arising from current big data approaches, with a particular focus on specific use cases that have been carried out to date, including urban crime prediction [10], inferring socioeconomic status of countries and indiv”
Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data (2013, background): “ power law P(Δd)~exp(-kΔd)Δd-β (González et al. 2008, Liu et al. 2012). The parameters in the above distributions are critical in applications such as epidemic or virus diffusion (Balcan et al. 2009, Wesolowski et al. 2012). Particularly, when P(Δd) follows a power law distribution in which 1<β&l”
Statistical physics of vaccination (2016, methodology): “For example approaches informed with data from CDRs have been used to map and control the spreading of Malaria [762, 763], Ebola [764], and ILIs [250, 765].”
Mobile Crowd Sensing and Computing (2015, background): “Wesolowski et al. [2012] reported the use of large-scale mobile phone location data and malaria prevalence information from Kenya to study their implicit links.”
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).”
A Survey on Trajectory Data Management, Analytics, and Learning (2020, background): “• Trajectory analytic for public health: Human movement data recorded by mobile phones has been analyzed to control Malaria in Africa [179] and dengue epidemics in Pakistan [180].”