Quantifying travel behavior for infectious disease research: a comparison of data from surveys and mobile phones

Wesolowski, Amy; Stresman, Gillian; Eagle, Nathan; Stevenson, Jennifer; Owaga, Chrispin; Marube, Elizabeth; Bousema, Teun; Drakeley, Christopher J.; Cox, Jonathan; Buckee, Caroline O.

doi:10.1038/srep05678

Cited by 127 publications

(107 citation statements)

References 30 publications

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“…However, this supports previous evidence that children rarely travel without adults and also underscores the importance of these population-scale movements for dynamics of infections like rubella (31). Although mobile phone data are inherently biased by ownership, there exist few data sources that can directly record daily movement patterns over the range of spatial (the dynamics of an entire country) and temporal (12 months of data) scale.…”

Section: Discussionsupporting

confidence: 85%

Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data

Wesolowski

Metcalf

Eagle

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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132

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Changing patterns of human aggregation are thought to drive annual and multiannual outbreaks of infectious diseases, but the paucity of data about travel behavior and population flux over time has made this idea difficult to test quantitatively. Current measures of human mobility, especially in low-income settings, are often static, relying on approximate travel times, road networks, or crosssectional surveys. Mobile phone data provide a unique source of information about human travel, but the power of these data to describe epidemiologically relevant changes in population density remains unclear. Here we quantify seasonal travel patterns using mobile phone data from nearly 15 million anonymous subscribers in Kenya. Using a rich data source of rubella incidence, we show that patterns of population travel (fluxes) inferred from mobile phone data are predictive of disease transmission and improve significantly on standard school term time and weather covariates. Further, combining seasonal and spatial data on travel from mobile phone data allows us to characterize seasonal fluctuations in risk across Kenya and produce dynamic importation risk maps for rubella. Mobile phone data therefore offer a valuable previously unidentified source of data for measuring key drivers of seasonal epidemics.rubella | mobile phones | population mobility | Kenya | seasonality S easonal variation in infectious disease incidence is a ubiquitous phenomenon observed for a range of pathogens such as malaria, measles, and influenza (1-7). Understanding and quantifying key mechanisms that drive seasonal variability such as climatic conditions (malaria and influenza) or patterns of human aggregation (measles and influenza) contribute to our fundamental understanding of epidemic dynamics; they also have important implications for evaluating public health measures that may reduce transmission such as vaccination and school closures (8-11).The effectiveness of any public health measure designed to reduce seasonal transmission by modifying patterns of human aggregation and travel will depend on the degree to which transmission depends on population density and movement. Direct measures of population travel are rare (2, 4, 12, 13). As a result, proxy measures such as school terms and rainfall patterns have been used (1,9,(13)(14)(15). Term time forcing, where school-driven aggregation leads to seasonal peaks of transmission for directly transmitted immunizing infections such as measles, mumps, and rubella, has been observed in many high-income countries (8,16,17) [England and Wales (8), Peru (15), and Denmark (17)]. On the other end of the spectrum in the low-income, predominantly agricultural context of Niger (13), analysis of night lights indicates that peaks in transmission reflect population changes resulting from annual mass migrations of individuals between agricultural areas to cities in the dry season (1). School terms and holidays versus agricultural movements likely represent the extremes in terms of density-related drivers of transmiss...

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Section: Discussionsupporting

confidence: 85%

Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data

Wesolowski

Metcalf

Eagle

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

132

116

View full text Add to dashboard Cite

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“…Others compare the movement patterns extracted from mobile records to traditional data sources such as censuses (28) and surveys (29). Several studies deal with the comparison with human mobility models (21,30).…”

mentioning

confidence: 99%

Mobile phone data highlights the role of mass gatherings in the spreading of cholera outbreaks

Finger

Genolet

Mari

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

156

120

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The spatiotemporal evolution of human mobility and the related fluctuations of population density are known to be key drivers of the dynamics of infectious disease outbreaks. These factors are particularly relevant in the case of mass gatherings, which may act as hotspots of disease transmission and spread. Understanding these dynamics, however, is usually limited by the lack of accurate data, especially in developing countries. Mobile phone call data provide a new, first-order source of information that allows the tracking of the evolution of mobility fluxes with high resolution in space and time. Here, we analyze a dataset of mobile phone records of ∼150,000 users in Senegal to extract human mobility fluxes and directly incorporate them into a spatially explicit, dynamic epidemiological framework. Our model, which also takes into account other drivers of disease transmission such as rainfall, is applied to the 2005 cholera outbreak in Senegal, which totaled more than 30,000 reported cases. Our findings highlight the major influence that a mass gathering, which took place during the initial phase of the outbreak, had on the course of the epidemic. Such an effect could not be explained by classic, static approaches describing human mobility. Model results also show how concentrated efforts toward disease control in a transmission hotspot could have an important effect on the large-scale progression of an outbreak. mobile phone call data | cholera epidemics | spatially explicit epidemiological models | waterborne disease

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“…In particular, patient travel history data, containing detailed demographic information and travel motivations, are traditionally used to understand malaria parasite importation patterns 48–50 . Recently, mobile phone call detail records (CDRs) have been increasingly used for measuring short-term human movements 51,52 and thus, either alone 38,53,54 or in combination with travel history data 55 and malaria case data, for supporting malaria control and elimination strategic planning.…”

Section: Background and Summarymentioning

confidence: 99%

Mapping internal connectivity through human migration in malaria endemic countries

et al. 2016

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Human mobility continues to increase in terms of volumes and reach, producing growing global connectivity. This connectivity hampers efforts to eliminate infectious diseases such as malaria through reintroductions of pathogens, and thus accounting for it becomes important in designing global, continental, regional, and national strategies. Recent works have shown that census-derived migration data provides a good proxy for internal connectivity, in terms of relative strengths of movement between administrative units, across temporal scales. To support global malaria eradication strategy efforts, here we describe the construction of an open access archive of estimated internal migration flows in endemic countries built through pooling of census microdata. These connectivity datasets, described here along with the approaches and methods used to create and validate them, are available both through the WorldPop website and the WorldPop Dataverse Repository.

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Quantifying travel behavior for infectious disease research: a comparison of data from surveys and mobile phones

Cited by 127 publications

References 30 publications

Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data

Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data

Mobile phone data highlights the role of mass gatherings in the spreading of cholera outbreaks

Mapping internal connectivity through human migration in malaria endemic countries

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