Passively generated mobile phone data have emerged as a potentially valuable data source to infer human mobility and social interactions. Call Details Records (CDRs) are arguably the most researched type of mobile data in this context. CDRs are collected by mobile operators for billing purposes. Each record contains information about the time and the cell tower that the phone was connected to when the interaction took place. CDRs are event-driven records: in other words, the record only exists if the phone is actively in use. Additional information includes ‘sightings data’ obtained when a phone is seen on a network. There are, however, other types of mobile phone data used to study human mobility behaviors and interactions. X Data Records (XDRs) or network probes, can be thought as metadata about the phone’s data channel, capturing background actions of apps and the network. Routine information including highly accurate location data are also collected through mobile phone applications (Apps) at a large scale by location intelligence companies or by ad hoc applications. Additionally, proximity between mobile phone users can be detected via Bluetooth functionality on smartphones. Each of these data types requires different processing frameworks and raise complex ethical and political concerns that are discussed in the paper publicized in Science Advances and referenced below. In there, a group of scientists outline the ways in which different types of mobile phone data can help to better target and design measures to contain and slow the spread of the COVID-19 pandemic. They identify the key reasons why this is not happening on a much broader scale, and give recommendations on how to make mobile phone data work against the virus.