Table 1.

Traditional and innovative data sources for measuring human movements

Data type	Description	Strengths	Challenges
Traditional data source
Population and housing census	Assembly of population and housing census data on place of residence 1–5 years ago.	Primary source for migration statistics; Global extent, consistent measure for complete population; Shows strong correlations to shorter scale domestic and international movements; Of value for global, continental, regional connectivity assessments.	Long-term movements and permanent migrations only; Coarse spatial scale, bias to longer spatial scales; Lack of census data in countries affected by conflicts; Normally collected once every decade.
Travel history surveys	Travel log collected at health facilities, or through active surveillance/surveys.	Valuable data on relevant population pathogen movements; High value for measuring temporal trends in domestic and international travel; Important data for refining and validating models.	Not collected in many settings; Sample a small proportion of population; Selection and recall biases; Difficult to access, inconsistent coverage/quality.
Cross-border and traffic surveys	Counting the number of cars and people that are crossing a border.	Cross-border movements; Measuring seasonal patterns by multiple cross-sectional surveys	Difficult to obtain the origins and destination locations of travel; Difficult to capture the whole picture of movements in where there are porous borders.
Novel data source—mobile phone
CDRs	Individual-level records routinely collected by mobile phone operators for billing purposes, located to cell towers.	Cover large population of mobile users, potential to track hard-to-reach populations; Rich spatiotemporal data on individual, fine-scale movements; Capture long time series and seasonality with timely information; Of value for national-scale analyses, assessing population distributions, disease connectivity, and the parameterization of mobility models.	Difficult to access and share; Ownership biases; Privacy issues and loss of information due to anonymization; Difficult to capture international movements.
Smartphone-based internet/social media location histories	Geolocated data on use of internet/social-media-connected devices, integrating online media content.	Timely, spatially precise positioning data on users’ locations; Long time series to capture seasonal domestic and international travel of users; Rapidly increasing penetration, potential to track hard-to-reach populations; Richness of information to understanding social connections and behaviours.	Ownership and selection biases, changing sample over time; Data availability and loss of information due to anonymization; Privacy and ethical issues; Additional logistical, technical issues for analysis.
mHealth apps data	Individual travel history and health risk monitoring data collected by the mobile applications for mHealth.	Timely information on users’ location; High value in real-time individual travel patterns, environmental exposure monitoring and health risk assessment during travel; Improving healthcare access for travel medicine and public health interventions; Of value for the individual-level quantitative research on travel-related risk exposure and health outcome.	Reliability of self-reported information; Selection bias and small sample size; Indicators for measuring the risk and exposure; Privacy and ethical issues.
Novel data source—other
Air travel data	Route aggregated statistics of flight passengers and air transportation network data.	Includes the origins, stops and destinations at airport or city level; Captures seasonality in long time series; High value in route-scale analyses, assessing international connectivity and modelling the risk of pathogen spread.	Incomplete picture of population movements; Difficult to access travel itinerary data, and lacks demographic data; Coarse spatial scale and difficult to capture the origins and destinations beyond airports.
Infrastructure	Georeferenced data on transport links that form the basis of regional mobility.	Global coverage, consistent data; Useful proxy indicative of mobility, connectivity and healthcare accessibility.	Based on an assumption that those travel times influence how population’s move; no measure of actual movements; Few time series; Validation.
Earth observation data	Data collected via remote-sensing technologies to monitor and assess the status of and changes in environments, e.g. satellite nightlight imagery	Proxy measures of population movements; Global coverage and high spatial resolution; High comparability and timely information.	No actual movements with unknown origins and destinations; Methodological and technical issues; Continuity and validation.