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, 112 (35), 11114-9

Quantifying Seasonal Population Fluxes Driving Rubella Transmission Dynamics Using Mobile Phone Data

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Quantifying Seasonal Population Fluxes Driving Rubella Transmission Dynamics Using Mobile Phone Data

Amy Wesolowski et al. Proc Natl Acad Sci U S A.

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 cross-sectional 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.

Keywords: Kenya; mobile phones; population mobility; rubella; seasonality.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
The rubella patterns in Kenya. (A) Over the course of the rubella dataset (January 2008 to January 2012), the biweekly number of reported rubella cases is shown. (B) A map of Kenya with provinces outlined in red along with the rubella case data per province. (C) The monthly transmission (beta) estimates per province. In the majority of provinces, there are two pronounced peaks in transmission during September and January–March with a number of locations also peaking in May–June (see Central province in red, for example). The peaks in transmission varied geographically with the Nyanza province peaking in February, for example.
Fig. S1.
Fig. S1.
The trajectory estimates for each province. Simulations correspond to trajectory matches for each of the eight provinces; seasonality is as shown in Fig. 1C. Other parameters estimated include starting number of infected individuals (ranges from 155 to 3,215), starting proportion susceptible (0.18–30%), and reporting rate (7e-5 to 9e-4).
Fig. S2.
Fig. S2.
A simulated seasonal trajectory of incidence per province. Simulations correspond to underlying seasonality in transmission to that observed for rubella in Kenya but with the transmission rate set to 30 in every month to reflect more measles-like dynamics. Multiannual peaks in every year disappear, replaced by annual dynamics, in line with what has been observed for measles in an array of different contexts.
Fig. 2.
Fig. 2.
The relationship between population flux, rainfall, school terms, and rubella transmission per province. (A) The normalized population fluxes measured from the mobile phone data per month in each province with months ordered from the start of the dataset (June) until the following May. In nearly all provinces, travel peaked in December and July/August, although this varied by location. (B) Monthly mean values from provinces for transmission and the three covariates (rainfall, school terms, and population flux). The time series is shown highlighting the variability in these estimates over the course of the year. We would expect that transmission would decrease during school breaks (white area) and population fluxes would increase during these times (white area). (C) Using the province level covariates shown in B, we constructed a number of models using various subsets of the covariates to predict transmission values. The estimated (from the TSIR model) versus predicted values for transmission (to aid with comparison a black xy line is drawn) from the minimum adequate model (black, includes population flux and school terms as covariates) and a model based on only school terms (red) or rainfall (blue). The model based on movement and school terms (adjusted R2 = 0.19, P < 0.001) provides the best estimates (only rainfall, adjusted R2 = 0.03, P = 0.056; only school terms, adjusted R2 = 0.09, P = 0.003). In comparison, the model based only on school terms only produces two transmission values, whereas the model based only on rainfall produces estimates over a smaller range than the model including population flux.
Fig. S3.
Fig. S3.
The population flux values for each province and relationship with rainfall and rubella transmission. (A) For each province, the population flux values over the course of a year. (B) The correlation between population fluxes with school terms and rainfall per province. Each province is colored according to the population density. School terms were negatively correlated with population fluxes, implying that there was more travel during school holidays (see main text). The relationship between rainfall and population fluxes was less clear with a number of provinces positively correlated.
Fig. S4.
Fig. S4.
The relationship between population flux and covariates on the district level. (A and B) For each province, we compared the population flux trends for districts located in that province such as Central or Eastern provinces. (C) In general, district-level trends were similar to province-level trends, although this varied geographically. (D) The relationship between district-level population flux and school terms and rainfall.
Fig. S5.
Fig. S5.
The relationship between transmission and population flux. The normalized population flux in the previous month is compared with the transmission rate for each province. Points representing months during which school terms are occurring (filled points) and not (empty points) showing that the population flux data are able to describe more variability in population dynamics than the binary school term measure.
Fig. 3.
Fig. 3.
The seasonal variability in the risk of importation. We analyzed the average amount of population flux per district during (A) the major holiday and a school term break (December) and (B) during a school term (March). As highlighted in Fig. 1, there are large amounts of population flux and consequently the risk of importation during school breaks (A and B). In contrast, there is a decrease in the risk of importation during the school term. During the course of the year, the districts with the largest risks vary with higher risks to western Kenya during school breaks. However, Nairobi (shown in red in both maps) consistently remains at a high risk of importation from the large population fluxes.
Fig. S6.
Fig. S6.
Seasonal fluctuations in births in Kenya. (A) For each month and each province, total numbers of births for children born after 1990 reported by mothers from the 2008–2009 Kenya DHS survey. (B) The correlation between seasonal birth fluctuations and other variables explored here, indicating few significant relationships. (C) Simulation of age structured dynamics of rubella in a Kenya-like population. The pattern of contact over age is set to reflect POLYMOD (37); seasonality in transmission reflects estimates from Nairobi (e.g., as in Fig. 1, with R0 = 4) with constant annual births (yellow), births fluctuating according to magnitude estimates from the DHS survey in Nairobi (orange), and the same pattern but with the magnitude tripled (red); fluctuations in births are depicted in E. The average age of infection is 6 y, resulting in annual dynamics with the expected three peaks every year. (D) Associated TSIR estimates of seasonal fluctuations in transmission, showing little effect, as expected given the relatively high average age of infection (27).

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