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Telecommunications data are being explored by many countries as a new source of data that can be incorporated into their national statistical systems. In particular, “mobile positioning data” are increasingly being used to study population movements and population distributions. However, the legal, ethical, and technical complexities of working with this type of data often pose many barriers, which can prevent the data from being used at the times when it is most urgently needed. We demonstrate how having a robust public–private partnership framework, a privacy-preserving technical setup, and a communications strategy already in place, prior to an emergency, can enable governments to harness the advantages of telecommunications data at the times when it is most valuable. However, even once these foundations are in place, the challenges of competing priorities, managing expectations, and maintaining communication with data consumers during a pandemic mean that the potential of the data is not automatically translated into direct impact. This highlights the importance of sensitisation exercises, targeted at potential data users, to make clear the potential and limitations of the data, as well as the importance of being able to maintain direct communication with data users. The views expressed in this work belong solely to the authors and should not be interpreted as the views of their institutions.

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This paper demonstrates how two different methods used to calculate population-level mobility from Call Detail Records (CDR) produce varying predictions of the spread of epidemics informed by these data. Our findings are based on one CDR dataset describing inter-district movement in Ghana in 2021, produced using two different aggregation methodologies. One methodology, “all pairs,” is designed to retain long distance network connections while the other, “sequential” methodology is designed to accurately reflect the volume of travel between locations. We show how the choice of methodology feeds through models of human mobility to the predictions of a metapopulation SEIR model of disease transmission. We also show that this impact varies depending on the location of pathogen introduction and the transmissibility of infections. For central locations or highly transmissible diseases, we do not observe significant differences between aggregation methodologies on the predicted spread of disease. For less transmissible diseases or those introduced into remote locations, we find that the choice of aggregation methodology influences the speed of spatial spread as well as the size of the peak number of infections in individual districts. Our findings can help researchers and users of epidemiological models to understand how methodological choices at the level of model inputs may influence the results of models of infectious disease transmission, as well as the circumstances in which these choices do not alter model predictions.