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

Abstract

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...

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

“…In the context of infectious disease spread in developing countries, this new source of information enables previously unseen kinds of analyses. Examples are the derivation of magnitude and destination of population fluxes following a sudden outbreak (25,31), and the quantification of the importance of human mobility and its seasonal variations on the spread of disease in terms of increased outbreak risk in and infectious pressure on connected areas (5,30,(32)(33)(34).…”

Challenges and Potential Opportunities of Mobile Phone Call Detail Records in Health Research: Review (Preprint)

“…Differential ownership of mobile phones among different sectors, particularly in low and middle income countries where much of CDR research takes place, also calls into question the representativeness of the data and thus the findings that ensue [72]. However, it has also been observed that despite biases, there a few, if any, data sources that can provide such rich spatial and temporal movement data, particularly for much-needed research in LMICs [61,64].…”

Seasonal Population Movements and the Surveillance and Control of Infectious Diseases

“…Where this data is available, and particularly where it can be combined with data on seasonal disease incidence, there is potential for considerable advances in understanding the role played by human movement in shaping seasonal disease dynamics. Previous work has suggested, for example, that seasonal mobility in Kenya shapes the magnitude of transmission of rubella [50], which has implications in turn for spatial dynamics of the infection, and can determine the burden of Congenital Rubella Syndrome [51]. Likewise, predictive frameworks for dengue outbreaks are more accurate when they incorporate both climatic variations and human movements measured by mobile phones [52], allowing for vector control and hospital preparation in advance of epidemics.…”