Arai et al. (2020) : The Hidden Potential of Call Detail Records in The Gambia.

# **C. Roadmap**

Section 2 positions our paper in the literature and describes the country context. Next, section 3 outlines the engagement model to work with the PURA and the GBoS on access to CDR data and producing statistical information from CDR data in The Gambia. Section 4 presents the data and defines the methodology to analyze the CDR data, and Section 5 showcases key results. Section 6 summarizes lessons learned from the ongoing engagement in The Gambia, and Section 7 concludes and outlines the next steps.

# **2. Context and literature review**

# **A. Policy relevance of CDR data**

There is a rich and growing literature seeking to leverage the potential of CDR data to inform policy making. With mobile penetration rising in developing countries (GSM Association, 2020), researchers have demonstrated the use of CDR data to create poverty maps (J. Blumenstock, Cadamuro, & On, 2015), understand migration patterns, and estimate a household’s economic characteristics (J. E. Blumenstock, 2018).There have been sustained improvements in forecasting population density by combining high-resolution satellite data with powerful algorithms (Stevens, Gaughan, Linard, & Tatem, 2015). WorldPop trains its algorithms on historical census data and projects annual population density at 100-meter resolution in a publicly available data set (Stevens et al., 2015). However, these data sets rely on slow-moving indicators and are computationally intensive. Forward-looking projections are based on linear extrapolation and do not account for short- or medium-term population movement dynamics.

Researchers have harnessed mobile phone data to map population movements. Deville et al. (Deville et al., 2014) showed that the density of unique users in a cell tower’s catchment area scales with population density, and can be plotted on a logarithmic curve. Researchers can therefore extrapolate shifts in the number of unique users to predict shifts in population densities over time, day by day or week by week. Accordingly, CDR aggregates provide insights on population movements, which is useful for estimating regional connectivity and the impact of mobility restrictions (Wesolowski, Buckee, Engø-Monsen, & Metcalf, 2016). It also helps identify areas with higher risks of importation due to population flows from other regions, and develop spatial epidemiological models (Aledort et al., 2007) (Wesolowski et al., 2012).

Combining CDR data with administrative and survey data offers insights on fast-moving health and well-being indicators. Drawing from survey data on the incidence of poverty in Rwanda, Blumenstock et al. use machine learning algorithms to predict poverty outcomes based solely on patterns in mobile network data (J. Blumenstock et al., 2015). Erbach-Schoenberg et al. combine CDR data with public health data sets in Namibia to link mobility and malaria incidence (Zu Erbach-Schoenberg et al., 2016). When compared with estimates using static maps, this leads to discrepancies of up to 30 percent. These applications showcase the value-added of high resolution, high-frequency proxy data like CDR in the context of an epidemic such as COVID-19.

CDR data can also be used to rapidly update estimates of population distribution when a natural disaster leads to widespread displacement. Bengtsson et al. used CDR data from a major telecommunications company to track displacement in Haiti after the 2010 earthquake (Bengtsson, Lu, Thorson, Garfield, & von Schreeb, 2011). This allowed them to track shifts in population distribution and estimate that up to 20 percent of the population of the capital city left in the 19 days after the earthquake. Lu et al. used

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