Countrywide population movement monitoring using mobile devices generated (big) data during the COVID-19 crisis

Abstract

Mobile phones have been used to monitor mobility changes during the COVID-19 pandemic but surprisingly few studies addressed in detail the implementation of practical applications involving whole populations. We report a method of generating a "mobility-index" and a "stay-at-home/resting-index" based on aggregated anonymous Call Detail Records of almost all subscribers in Hungary, which tracks all phones, examining their strengths and weaknesses, comparing it with Community Mobility Reports from Google, limited to smartphone data. The impact of policy changes, such as school closures, could be identified with sufficient granularity to capture a rush to shops prior to imposition of restrictions. Anecdotal reports of large scale movement of Hungarians to holiday homes were confirmed. At the national level, our results correlated well with Google mobility data, but there were some differences at weekends and national holidays, which can be explained by methodological differences. Mobile phones offer a means to analyse population movement but there are several technical and privacy issues. Overcoming these, our method is a practical and inexpensive way forward, achieving high levels of accuracy and resolution, especially where uptake of smartphones is modest, although it is not an alternative to smartphone-based solutions used for contact tracing and quarantine monitoring.

Figure 1

Schematic presentation of the methodology.

Figure 2

Daily device activity in Hungary for February and March of 2020.

Figure 3

The mobility and stay-at-home (resting) indices for Hungary, showing the civil response to government measures.

Figure 4

The changes in the mobility index in the settlements (each represented by a shaded circle) around one favourite holiday place in Hungary, the lake Balaton on 5 April 2020. Lighter grey colouring represents a decrease in mobility compared to the reference day, while darker grey colouring represents an increase. (Created by Microsoft Power BI Pro software, Microsoft Corporation, 2020. Version: 2.85.985.0 64-bit https://powerbi.microsoft.com/hu-hu/power-bi-pro/).

Figure 5

Comparison of two methods for measuring the relative change from the baseline of "staying-at-home". Left (a) The residential index of Google (purple) and the CDR based stay-at-home index (green) plotted as a time series. Right (b) Scatter plot of the daily stay-at-home index as a function of the daily residential index of Google. Perfect fit would correspond to a set of points falling onto the diagonal line shown in purple. The weekends during the curfew seem to deviate from this to the left, whereas outliers to the right are corresponding to the national holidays.