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Forecasting influenza activity using machine-learned global mobility map

Srinivasan Venkatramanan
Adam Sadilek
Arindam Fadikar
Christopher L. Barrett
Matthew Biggerstaff
Jiangzhuo Chen
Xerxes Dotiwalla
Paul Eastham
Dave Higdon
Onur Kucuktunc
Allison Lieber
Bryan L. Lewis
Zane Reynolds
Anil K. Vullikanti
Lijing Wang
Madhav Marathe
Nature Communications, vol. https://www.nature.com/articles/s41467-021-21018-5 (2021)


Human mobility is a primary driver of infectious disease spread. However, existing data is limited in availability, coverage, granularity, and timeliness. Data-driven forecasts of disease dynamics are crucial for decision-making by health officials and private citizens alike. In this work, we focus on a machine-learned anonymized mobility map (hereon referred to as AMM) aggregated over hundreds of millions of smartphones and evaluate its utility in forecasting epidemics. We factor AMM into a metapopulation model to retrospectively forecast influenza in the USA and Australia. We show that the AMM model performs on-par with those based on commuter surveys, which are sparsely available and expensive. We also compare it with gravity and radiation based models of mobility, and find that the radiation model’s performance is quite similar to AMM and commuter flows. Additionally, we demonstrate our model’s ability to predict disease spread even across state boundaries. Our work contributes towards developing timely infectious disease forecasting at a global scale using human mobility datasets expanding their applications in the area of infectious disease epidemiology.