Forecasting influenza activity using machine-learned mobility map

Srinivasan Venkatramanan, Adam Sadilek (), Arindam Fadikar, Christopher L. Barrett, Matthew Biggerstaff, Jiangzhuo Chen, Xerxes Dotiwalla, Paul Eastham, Bryant Gipson, Dave Higdon, Onur Kucuktunc, Allison Lieber, Bryan L. Lewis, Zane Reynolds, Anil K. Vullikanti, Lijing Wang and Madhav Marathe
Additional contact information
Srinivasan Venkatramanan: University of Virginia
Adam Sadilek: Google Inc.
Arindam Fadikar: Argonne National Laboratory
Christopher L. Barrett: University of Virginia
Matthew Biggerstaff: Centers for Disease Control and Prevention
Jiangzhuo Chen: University of Virginia
Xerxes Dotiwalla: Google Inc.
Paul Eastham: Google Inc.
Bryant Gipson: Google Inc.
Dave Higdon: Virginia Tech
Onur Kucuktunc: Google Inc.
Allison Lieber: Google Inc.
Bryan L. Lewis: University of Virginia
Zane Reynolds: Torc Robotics
Anil K. Vullikanti: University of Virginia
Lijing Wang: University of Virginia
Madhav Marathe: University of Virginia

Nature Communications, 2021, vol. 12, issue 1, 1-12

Abstract: Abstract 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.

Date: 2021
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DOI: 10.1038/s41467-021-21018-5

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