The following study was conducted by Scientists from Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA; Google Inc., Mountain View, CA, USA; Argonne National Laboratory, Lemont, IL, USA; Department of Computer Science, University of Virginia, Charlottesville, VA, USA; Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA; Department of Statistics, Virginia Tech, Blacksburg, VA, USA; Torc Robotics, Blacksburg, VA, USA. Study is published in Nature Communications Journal as detailed below.
Nature Communications; Volume 12, Article Number: 726 (2021)
Forecasting Influenza Activity Using Machine-Learned Mobility Map
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.
Source:
Nature Communications
URL: https://www.nature.com/articles/s41467-021-21018-5
Citation:
Venkatramanan, S., Sadilek, A., Fadikar, A. et al. Forecasting influenza activity using machine-learned mobility map. Nat Commun 12, 726 (2021). https://doi.org/10.1038/s41467-021-21018-5
