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

Indicator 10 - Day Consecutive trips generated by a Proxy for daily mobility Origin Destination sequence of records, counting across regions plus the Matrix the number of times each pair approximate duration of appears and time spent at both stay in a location. origin and destination regions.

a Source: Authors’ adaptation based on the World Bank COVID19 Mobility Task Force repository on GitHub.

# **C. Application in The Gambia**

This paper makes use of the proposed indicators from the World Bank COVID-19 Mobility Task Force and applies them to the country context of The Gambia to analyze patterns of human mobility during COVID-19. More specifically:

- Indicator 3 shows changes in the population distribution over time. As subscribers are accounted for in every region in which they use their phones, it overestimates subscribers who visit multiple regions in a day when computed at the administrative unit level. The value of this indicator can be also affected by load sharing where several cell towers jointly cover a certain area due to network optimization. This impact is mitigated by the cell-tower clustering, which was described as part of data pre-processing in the previous section. We compute this indicator at the national level for examining how the number of active subscribers as a whole country changes over time and for adjusting the result of other indicators.

- Indicator 6 illustrates changes in the location of residency, which could infer the incidence of migration over the data period. For mapping the residential distribution, there are various methodologies and algorithms (Deville et al., 2014) (Ahas, Silm, Järv, Saluveer, & Tiru, 2010), which provide more accurate estimates compared to the proposed method. We consider it still useful for detecting a flexible home location reflecting weekly changes as the estimation result is used at the administrative unit level. In addition, the proposed method is relevant under resource-scarce settings as it is not computationally intensive.

- Indicator 7 demonstrates changes in levels of mobility over the data period. The value of this indicator is defined as the average distance traveled per person residing in a region. This indicator has limitations in detecting mobility particularly in rural areas due to lower cell-tower density. Mean values for regions in rural areas tend to be affected by extreme values generated from distant-cell towers, which are much longer distance than that can be traveled. In addition, median values for rural areas tend to be zero because short-distance travel is not detected when a wide area is covered by a cell tower. We use the value of the 75th percentile. It results in representing the mobility patterns of people whose mobility is relatively higher. However, the value enables us to capture changes without being affected by extreme values.

- Indicator 10 describes the sizes of population inflow and outflow. We use this indicator for examining changes in population inflows. This indicator can be used for constructing OriginDestination (OD) matrices but has limitation in capturing long-distance trips. This is because a trip for constructing an OD matrix is defined by each consecutive pair of records, meaning that a long-distance trip is transformed into a set of several short trips, and thereby a link between the origin and destination of the long trip is missed.

The four indicators above are selected for the application in The Gambia to inform COVID-19 responses. These indicators are useful for capturing changes in mobility patterns, occurring in relatively

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