The Reliability of Mobile Phone Data

listen on castbox.fmlisten on google podcastslisten on player.fmlisten on pocketcastslisten on podcast addictlisten on tuninlisten on Amazon Musiclisten on Stitcher


The Reliability of Mobile Data

Nishant Kishore, a PhD graduate in Infectious Disease Epidemiology from Harvard University, joins us today. Nishant discusses his research work on identifying, studying and incorporating novel data, particularly mobile phone data, in tackling the spread of infectious diseases.

Mobile phone data has become an alternative for gathering relevant information for public health. Nishant started by discussing the various ways mobile phones generate data. The conventional way is for phones to exchange data with cell towers during calls. But beyond that, installed applications also generate a massive amount of data which could also be useful in understanding public movement and planning public health policies. 

Nishant emphasized the need to understand the data generation pipeline to determine whether a mobile phone dataset is a valid representation of human mobility. In cases where the apps have a vague data generation pipeline, Nishant discussed how aggregation can help in confirming the integrity of the data. He pointed out some observed discrepancies between data output from different locations and why this happened.

Mobility data from mobile phones’ GPS were usually laced with noise and thus, difficult to interpret. Nishant discussed a few instances where data transformation helps to make more sense of the noisy GPS data.

Going forward, Nishant explained his model validation process. He explained other challenges with the data which make it difficult for policymakers to make quick inferences. This birthed the COVID-19 Mobility Data Network where researchers collectively study the data to generate simple feedback for local and state policy-makers. Nishant also discussed efforts to build public confidence in the data by creating data anonymity without causing intrinsic bias.

Wrapping up, Nishant highlighted the key takeaways from collecting and studying mobile data. He also pointed out prospective areas for future study. To learn more about public mobility in response to disasters and the data readiness methods, visit CrisisReady. You can directly follow Nishant on Twitter @nish_epi.

Nishant is a Steven M. Teutsch Public Health Analytics and Modeling Fellow working in the Global Immunization Division at the Centers for Disease Control and Prevention. The contents of this interview represent Nishant’s work during his doctoral studies and do not necessarily represent the views of the Centers for Disease Control and Prevention. Nishant earned a PhD in Epidemiology and SM in Biostatistics from the Harvard T.H. Chan School of Public Health in July 2020 where his research focused on incorporating mobility and remote sensing data into existing modeling frameworks. This included modeling SARS-CoV-2 transmission within and between populations, evaluating behavior change in response to lockdown notifications and building analytic pipelines to inform response efforts in acute natural disasters. In his spare time Nishant continues to provide research support to, 1) CrisisReady, an organization which assists agencies responding to disasters make use of novel data streams, and 2) EpiTech Consultants, an organization that specializes in the development of analytic pipelines for complex public health problems. In his free time, he enjoys biking, rowing, and exploring the food scene in Atlanta.

Thanks to our sponsors for their support

Log, store, query, display, organize, and compare all your model metadata in a single place