Mobility data shows effectiveness of control strategies for COVID-19 in remote, sparse and diffuse populations - Berman, Yuval; Algar, Shannon D.; Walker, David M.; Small, Michael (Frontiers in Epidemiology, 2023)
Data that is collected at the individual-level from mobile phones is typically aggregated to the population-level for privacy reasons. If we are interested in answering questions regarding the mean, or working with groups appropriately modeled by a continuum, then this data is immediately informative. However, coupling such data regarding a population to a model that requires information at the individual-level raises a number of complexities. This is the case if we aim to characterize human mobility and simulate the spatial and geographical spread of a disease by dealing in discrete, absolute numbers. In this work, we highlight the hurdles faced and outline how they can be overcome to effectively leverage the specific dataset: Google COVID-19 Aggregated Mobility Research Dataset (GAMRD). Using a case study of Western Australia, which has many sparsely populated regions with incomplete data, we demonstrate how to estimate absolute flow of people around a transport network from the aggregated data. Overlaying this evolving mobility network with a compartmental model for disease that incorporated vaccination status we draw meaningful conclusions about the spread of COVID-19 throughout the state without de-anonymizing the data.