Abstract

For most of human history, populations have been relatively isolated from each other, and only recently has there been extensive contact between peoples, flora and fauna from both old and new worlds. The reach, volume and speed of modern travel are unprecedented, with human mobility increasing in high income countries by over 1000-fold since 1800. This growth is putting people at risk from the emergence of new strains of familiar diseases, and from completely new diseases, while ever more cases of the movement of both disease vectors and the diseases they carry are being seen. Pathogens and their vectors can now move further, faster and in greater numbers than ever before. Equally however, we now have access to the most detailed and comprehensive datasets on human mobility and pathogen distributions ever assembled, in order to combat these threats. This short review paper provides an overview of these datasets, with a particular focus on low income regions, and covers briefly approaches used to combine them to help us understand and control some of the negative effects of population and pathogen movements.

Introduction

Human movement often has a central role in economics and development, the delivery of services, and the spread of infectious diseases. The progression of epidemics and maintenance of endemic diseases are strongly linked to human movement patterns,1,2 while access to markets and efficient transportation to increase workforce mobility and the flow of goods can drive economic development.3 Planning, intervention, mitigation, and development policies can be better informed through the incorporation of spatial information on human movement across spatiotemporal scales, but reliable data for human mobility mapping have often been lacking, particularly in resource-poor settings.

Attempts to quantify and map human movements are nothing new.1,47 Travel history surveys, road traffic counts, border crossing questionnaires, shipping schedules and census migration questions have long been used to provide data on how people move (Figure 1a); but each of these data types represents a snapshot of a small area, subpopulation or time period, with limits on how much can be inferred beyond the data collected.2,8 Recent years have, however, seen the advent of large digital datasets from which unprecedented details on human movements across a range of spatiotemporal scales can be quantified (Figure 1b). In this short review article, a brief overview of the sources and features of many of these datasets is provided, as well as the potential for combining them with spatial epidemiological data to infer pathogen movement patterns globally and in some of the highest disease burden areas of the world.

Figure 1.

The spatial (x-axis) and temporal (y-axis) scales of human movements and the datasets available to quantify them (a) pre-21st century and (b) today.

Figure 1.

The spatial (x-axis) and temporal (y-axis) scales of human movements and the datasets available to quantify them (a) pre-21st century and (b) today.

Materials and methods

Paper selection criteria

Literature was selected for inclusion in this short review through searches on PubMed (http://www.ncbi.nlm.nih.gov/pubmed) and Web of Knowledge (http://wok.mimas.ac.uk/) for combinations of select keywords, such as ‘human migration/mobility/movement mapping/measurement’ and ‘disease/pathogen/parasite dispersal/movement/mobility’. Given the broad range of the subject area, many more papers, books and reports were identified than the limits for review articles allowed. Therefore, the most highly cited and recent literature found was prioritised for inclusion to provide an overview of the subject area.

Mapping human movements

Air and sea travel data records

Until the last few decades, shipping was the principal method for international travel, but little data on numbers of ships or people moving were kept in centralized locations. Nowadays, each ship is tracked via GPS, while detailed records on passenger numbers are maintained. (www.lr.org)9,10 Similarly, detailed records are now maintained on almost every ticket and flight operated on the global airline network, which has become the dominant mode of international passenger travel (www.iata.org).11,12 The expansion of commercial air travel over the past 80 years (Figure 2) has transformed global connectivity, enabling people to move from one side of the planet to the other in less than 24 hours. Comprehensive datasets on passenger movements are often expensive however, and confidentiality agreements make them difficult to share, therefore efforts have been made to produce open access modelled versions.13,14

Figure 2.

Changing global connectivity and mobility through air travel. The international commercial air network in (a) 1933 (adapted from Massey6), and (b) 2010.

Figure 2.

Changing global connectivity and mobility through air travel. The international commercial air network in (a) 1933 (adapted from Massey6), and (b) 2010.

Census, household survey and displacement survey migration data

Information on population migrations have long been provided through population censuses, where questions on place of residence a year or more prior to the survey can yield valuable data on long term migration patterns.15–17 Increasingly, such data are being made available aggregated by administrative units through national statistical offices, or for samples of individuals through census microdata (e.g. https://international.ipums.org/international/). Figure 3 shows an example of migration flows mapped out for Senegal, highlighting the regional variations in connectivity that can now be quantified through such information. Small-scale travel history surveys can be cheap and easy to implement to obtain migration and mobility sample data, but they can often also be difficult to share due to confidentiality agreements and suffer from recall biases. The rise in numbers of nationally representative household surveys conducted in low income countries, however, is providing new sources of migration information between census years.8,18 Recent work has shown how these measures of migration over long time scales show strong correlations with the relative flows between locations over short temporal scales,19 thereby increasing the utility of the data in the inferences that can be drawn. Finally, a variety of survey sources often feed into estimates of mass population displacements following conflicts or natural disasters, and are becoming more widely available through international agencies (e.g. reliefweb.int, www.internal-displacement.org), and through crowdsourcing efforts.20

Figure 3.

Levels of migration between districts in Senegal 1997–2002, derived from census microdata. The thickness of each migration line represents the total number of migrants between districts, with migration quantified by a change of residence within the 1997–2002 period.

Figure 3.

Levels of migration between districts in Senegal 1997–2002, derived from census microdata. The thickness of each migration line represents the total number of migrants between districts, with migration quantified by a change of residence within the 1997–2002 period.

Satellite nightlight data

Substantial seasonal migrations and movements occur in many parts of the world.1 These include large numbers of people travelling home for Chinese new year or for the annual Hajj pilgrimage. Moreover, agricultural seasons drive significant seasonal migrations across the Sahel region of Africa, whereby conditions in the dry season mean that land becomes unproductive, prompting thousands to move to cities for a few months each year to seek alternative work. Such large migrations have only been described in small scale anthropological studies previously, with no methods or data available for quantifying the timing or magnitude of them. While previous work has shown the value of nighttime satellite images for inferring population distributions,21 recent work has highlighted the potential of sequences of these images.22 With thousands of Sahelian seasonal migrants moving into cities at the start of the dry season and using electric lighting or fires for cooking, these increases in anthropogenic sources of light can be detected from space,22 providing valuable data on the timings and magnitudes of seasonal movements into and out of cities in some of the world's poorest countries.

Mobile phone usage data

Perhaps the most promising new source of data on population movement patterns is that derived from anonimized mobile phone call detail records. The time of each call or text made by an individual and the location of the tower it is routed through are recorded by mobile phone network operators for billing purposes. Through analyzing sequences of calls/texts and their locations, the movement patterns of an anonimized individual can be inferred.23 Thus, across the full set of phone users subscribed to the network, the movement patterns of millions of individuals across time periods of years can be quantified to the spatial scale of phone tower reception areas. With estimates suggesting that there are now more mobile phones than people on the planet and high ownership levels, even in some of the poorest and most remote places,24 such data offer an unprecedented source of information on human mobility. For example, such data have been recently used to demonstrate the predictability of human movement patterns25–27 and their utility in disaster situations.28–30

Geotweets and other social media

The rise of social media and particularly the incorporation of locational information are presenting new opportunities for measuring mobility. Location based applications such as Foursquare have been shown to be capable of providing detailed data on movement patterns in small areas (e.g. http://www.huffingtonpost.com/2013/03/21/foursquare-check-ins-visualization-video_n_2923908.html), but the limited usage of these applications limits abilities to draw general conclusions in many parts of the world or for many demographic groups. In contrast, Twitter (www.twitter.com) has significant uptake in many countries of the World, with high usage in countries such as Indonesia and Saudi Arabia, but its usage for measuring population movements has yet to be fully exploited. With a rising percentage of tweets containing geographic information,31 the potential exists to utilise this information source for mapping human movement patterns in novel ways (e.g., http://www.mapbox.com/labs/twitter-gnip/locals/). Finally, efforts have recently been made to assess the feasibility of using Facebook data as an indicator of coordinated migrations: https://www.facebook.com/notes/facebook-data-science/coordinated-migration/10151930946453859.

Personal devices

GPS devices and other types of sensors, either within smartphones or carried as standalone units, represent a new source of detailed mobility data measured at finer spatial and temporal scales than ever before. With the cost of such devices continuing to drop, more and more studies are making use of them to provide insights into aspects of movement such as routines,32,33 contact patterns33,34 and activity levels.35 Further, when combined with travel journals, better information on the types of locations visited and motivations for travel can be ascertained. Radio Frequency Identification Devices (RFIDs) also represent a valuable alternative source to GPS for tracking movements to/from predefined locations through the installations of antennas at these locations and providing participants with detectable tags. Moreover, WiFi and Bluetooth applications for tracking devices being carried have also been used to derive high resolution mobility estimates, moving towards the concept of ‘smart cities’.36

Mapping pathogen movements

Human movement is a critical behavioural factor underlying observed patterns of disease transmission. Heterogeneities in patterns of contact between infectious agents and susceptible hosts can amplify or dampen rates of transmission, and different types of movements across spatial and temporal scales (Figure 1) can have varying relevance to public health.1,2 Here we briefly outline examples of the application of the movement datasets documented above to pathogen mobility issues.

Directly-transmitted infectious diseases

Numerous approaches have been developed which attempt to model the past and possible future movements of newly-emergent communicable diseases through global and local transport networks.10,37–39 While the movements of pandemics are notoriously unpredictable,38 those models that can be calibrated using both data from previous epidemic events and building on contemporary population and pathogen movement pattern data are perhaps the ones that stand the best chance of being used to predict the spread of communicable diseases in the future, enabling the construction of early warning systems and forming a basis for the planning of control strategies.37 Given the vast range of complicating factors, no dataset or model can be relied upon to predict the spread of an infectious disease with complete accuracy. Modelling can, however, identify possible efficient interventions from a range of available scenarios, taking into account the range of uncertainties of key epidemiological parameters and the mobility data upon which they rely. Examples of the use of the types of novel digital mobility datasets outlined above in informing directly-transmitted disease modelling studies are growing. These include the use of airline passenger data in modelling the global spread of influenza, smallpox and SARS,40–45 or airline data combined with commuting data.46 Moreover, satellite nightlight data has been applied to model measles dynamics,22 while mobile phone data has been used to quantify human contact networks47 and model the effects of heterogeneity in population mobility on influenza spread.48

Vector-borne diseases

The addition of a vector to the disease transmission equation complicates the dynamics of pathogen movements and has meant that studies on the movement of vector-borne diseases have been few and far between compared to directly-transmitted pathogen spread. However, spatial data on the distributions of vector-borne pathogens49,50 their vectors51,52 and human populations53,54 are providing the underlying evidence-bases for linkage with the mobility datasets outlined above to better infer pathogen mobility rates and facilitate the construction of mobility models. Moreover, as with the analyses of directly-transmitted diseases, phylogenetic39,55 and imported case data are providing test datasets for deriving parameters and validating such models. Examples of digital mobility dataset applications include the linkage of flight and shipping traffic data to estimate and model global vector-borne disease and vector mobility.14,56–59 Figure 4 presents an example of this for the Vector-borne Disease Airport Importation Risk (VBD-Air: www.vbd-air.com) tool, highlighting incoming direct flight routes to London Heathrow airport from Plasmodium falciparum malaria endemic areas with flight volumes scales by prevalence at origin to provide a metric of relative importation risk. Moreover, the application of mobile phone usage data for estimating malaria parasite movements has also been demonstrated,60–62 and is a growing field.

Figure 4.

A screenshot taken from the Vector-borne Disease Airport Importation Risk tool (VBD-Air: www.vbd-air.com), highlighting direct flight routes to London Heathrow from Plasmodium falciparum endemic areas, with each route shaded by the amount of air traffic. The underlying map shows the distribution of P. falciparum prevalence. The output of the tool highlights how well connected ‘malaria-free’ countries such as the UK are to endemic regions through air passengers. The UK receives around 2000 notified imported malaria cases a year.

Figure 4.

A screenshot taken from the Vector-borne Disease Airport Importation Risk tool (VBD-Air: www.vbd-air.com), highlighting direct flight routes to London Heathrow from Plasmodium falciparum endemic areas, with each route shaded by the amount of air traffic. The underlying map shows the distribution of P. falciparum prevalence. The output of the tool highlights how well connected ‘malaria-free’ countries such as the UK are to endemic regions through air passengers. The UK receives around 2000 notified imported malaria cases a year.

Discussion

The availability of new datastreams over the past decade offers huge potential in our abilities to quantify and understand human mobility patterns over multiple spatial and temporal scales. Moreover, the expansion in data on pathogen distributions and mobility, improved transmission modelling, and linkage with these human travel data, continues to present new insights into the patterns, processes and predictability of disease movements.63

The novel mobility datasources described here offer great possibilities for understanding the dynamics and demographics of human and pathogen mobility, particularly when combined with more ‘traditional’ data sources (e.g., see Figure 1a), but a variety of drawbacks and weaknesses still remain. Firstly, deficiencies in representation exist, whereby no single type of data covers the full diversity seen in both human and pathogen mobility (e.g., see Figure 1b), and often the better the data that exists, the more specific to a location, demographic group or spatiotemporal scale it is. At the largest scale, the flight and shipping datasets represent comprehensive sources of information on global movement patterns, but they often remain held by private companies and remain prohibitively expensive. While modelled datasets have aimed to overcome these limitations,13,14 significant uncertainties remain within them. Further, information on the demographics of travellers and motivations for moving are generally unknown, while the movements of pathogens and vectors on these global networks are reliant on sparse and limited surveillance and sample data. Census and household survey data represent a converse problem: detailed data on the demographics and motivations of those moving are collected, but the time scales of mobility (>1 year), time between censuses (>10 years) and spatial scales of mobility mapping achievable (often district level or coarser) represent substantial limitations in terms of inferring relevant movement parameters for modelling pathogen spread. Many of these limitations are overcome through anonimized mobile phone call record data, but as with flight and shipping data, the demographics of travellers remains unknown, as well as potential biases incurred by certain demographic groups being more frequent phone users, or geographical biases in network coverages and usage. Recent work has suggested that these features may not bias significantly mobility estimates,64 but additional limitations remain: such data cannot provide information on cross-border movements due to phone network providers being limited to national-level operations, while data sharing remains exceptionally difficult due to the sensitive nature of such information. Finally, the potential of satellite nightlights, social media and GPS data remain relatively untested in terms of mapping human and pathogen mobility, and require further research.

All of the dataset drawbacks outlined above highlight the need to consider data combinations in obtaining a more comprehensive picture of human mobility in an area, and the development and usage of a single, or set of, ‘universal movement model(s)’. For most diseases, specific demographic groups are more likely to be susceptible, more likely to be carriers and more likely to become ill than others, and so identifying these groups becomes important. Only through linking, for example, phone usage data with household survey data can both detailed movement patterns across spatiotemporal scales and demographic heterogeneities be accounted for.64 The potential of such integrated approaches is clear. For example, in mapping hotspots of malaria transmission in elimination settings and the mobile demographic groups that catalyse transmission elsewhere,65 or devising targeted dengue control strategies based on mobility patterns.66 The increasing collection and availability of mobility datasets, and efforts to build and share movement models based on such data (e.g., www.thummp.org) will continue to increase our understanding of human mobility patterns and, in turn, pathogen movements.67–69 Inevitably, we will never have data to cover all spatiotemporal scales of mobility (Figure 1), for all demographic groups and pathogens, and for all locations. The continued recent development of human and pathogen mobility models built around activity-space, radiation, gravity-type spatial interaction and motif concepts16,67–69 put forward previously,70,71 therefore remains an important field of research, particularly for aiding in the translation of mobility features observed at one location, timescale or group, to another.

Conclusions

With no apparent end in sight to the continued growth in human mobility, we must expect the continued appearance of communicable disease pandemics, disease vector invasions and vector-borne pathogen dispersion. Increases in human mobility and global travel are happening simultaneously with many other processes that favour the emergence and spread of disease. Approaches that can model, predict and explain such events can be used to focus surveillance and control efforts more efficiently. The development and application of statistical and mathematical models based on spatially-explicit data, with a particular focus on mobility have the potential to aid in moving towards real-time adaptive management and surveillance, and ultimately help in achieving disease control and elimination.

Author's contributions: AJT has undertaken all the duties of authorship and is guarantor of the paper.

Acknowledgements: This paper forms part of the outputs of the Human Mobility Mapping (www.thummp.org), Vector-borne Disease Airport Importation Risk (www.vbd-air.com) and WorldPop (www.worldpop.org.uk) projects. Thanks to Dr Andres Garcia for creating the image used in Figure 2.

Funding: This work was supported by the RAPIDD program of the Science and Technology Directorate, Department of Homeland Security, and the Fogarty International Center, National Institutes of Health, and is also supported by grants from NIH/NIAID [U19AI089674] and the Bill and Melinda Gates Foundation [#49446 and #1032350].

Competing interests: None declared.

Ethical approval: Not required.

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