Challenges and opportunities in accessing mobile phone data for COVID-19 response in developing countries

Abstract

Anonymous and aggregated statistics derived from mobile phone data have proven efficacy as a proxy for human mobility in international development work and as inputs to epidemiological modeling of the spread of infectious diseases such as COVID-19. Despite the widely accepted promise of such data for better development outcomes, challenges persist in their systematic use across countries. This is not only the case for steady-state development use cases such as in the transport or urban development sectors, but also for sudden-onset emergencies such as epidemics in the health sector or natural disasters in the environment sector. This article documents an effort to gain systematized access to and use of anonymized, aggregated mobile phone data across 41 countries, leading to fruitful collaborations in nine developing countries over the course of one year. The research identifies recurring roadblocks and replicable successes, offers lessons learned, and calls for a bold vision for future successes. An emerging model for a future that enables steady-state access to insights derived from mobile big data - such that they are available over time for development use cases - will require investments in coalition building across multiple stakeholders, including local researchers and organizations, awareness raising of various key players, demand generation and capacity building, creation and adoption of standards to facilitate access to data and their ethical use, an enabling regulatory environment and long-term financing schemes to fund these activities.

Program Targeting with Machine Learning and Mobile Phone Data: Evidence from an Anti-Poverty Intervention in Afghanistan

“…CDR have also been shared with the public sector for public health and humanitarian aid applications (Milusheva et al, 2021). Access issues aside, CDR contain private and sensitive data, including phone numbers and location traces.…”

“…Our paper connects these two literatures by rigorously assessing the extent to which phone-based estimates of poverty can help with program targeting (Blumenstock, 2020). We believe the analysis will be especially relevant to the increasing number of interventions that rely on mobile money to distribute cash payments (Gentilini et al, 2020), and the growing number of contexts where mobile phone data are being made available for humanitarian purposes (Milusheva et al, 2021). For example, in just the past few years, mobile money was used to make cash transfer payments in countries including Bangladesh (Ali & May, 2021), Ghana (Karlan et al, 2021), Liberia (USAID, 2021), and Malawi (Paul et al, 2021).…”

“…For phone-based targeting, we associate no cost with the collection and analysis of phone data. While in some cases phone data may require purchase from the operator, partnerships between mobile network operators and governments for social protection and public health applications have not, to date, involved payment (Milusheva et al, 2021). The fixed cost of mobile data analysis is non-negligible but its contribution to marginal cost is close to zero as the number of screened households increases.…”

“…Second, the CDR-based method requires data from mobile network operators. Data sharing agreements with mobile network operators take at minimum a few weeks to arrange, and substantially longer in the worst case (Milusheva et al, 2021). Third, and finally, training a CDR-based poverty prediction model is expensive in terms of memory, computing power, and human capacity, and will likely take several weeks to implement.…”

Mobile Big Data in the fight against COVID-19

“…This paper shows that it is possible to form a mutually beneficial collaboration between a telecom operator and a public institution, and to make use of mobility data in evidence-based policymaking without compromising applicable personal data protection law. Milusheva et al (2021) note the proven efficacy of statistics derived from aggregated, anonymized mobile phone data as a proxy for mobility. They focus on drawing lessons learned and providing recommendations to unlock systematic use, based on their experiences of implementing collaborative projects around the world.…”