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.
- Authors:
-
[2];
[1];
[1];
- Univ. of Virginia, Charlottesville, VA (United States)
- Google Inc., Mountain View, CA (United States)
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Centers for Disease Control and Prevention (CDC), Atlanta, GA (United States)
- Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States)
- Torc Robotics, Blacksburg, VA (United States)
- Publication Date:
- Research Org.:
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Sponsoring Org.:
- USDOE; Defense Threat Reduction Agency (DTRA); National Institutes of Health (NIH); National Science Foundation (NSF)
- OSTI Identifier:
- 1773047
- Grant/Contract Number:
- AC02-06CH11357; HDTRA1-11-D-0016-0001; 5U01GM070694; 1R01GM109718; ACI-1443054; CMMI-1745207; IIS-1633028
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Nature Communications
- Additional Journal Information:
- Journal Volume: 12; Journal Issue: 1; Journal ID: ISSN 2041-1723
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 60 APPLIED LIFE SCIENCES; computational models; data integration; influenza virus; machine learning; network topology
Citation Formats
Venkatramanan, Srinivasan, Sadilek, Adam, Fadikar, Arindam, Barrett, Christopher L., Biggerstaff, Matthew, Chen, Jiangzhuo, Dotiwalla, Xerxes, Eastham, Paul, Gipson, Bryant, Higdon, Dave, Kucuktunc, Onur, Lieber, Allison, Lewis, Bryan L., Reynolds, Zane, Vullikanti, Anil K., Wang, Lijing, and Marathe, Madhav. Forecasting influenza activity using machine-learned mobility map. United States: N. p., 2021.
Web. doi:10.1038/s41467-021-21018-5.
Venkatramanan, Srinivasan, Sadilek, Adam, Fadikar, Arindam, Barrett, Christopher L., Biggerstaff, Matthew, Chen, Jiangzhuo, Dotiwalla, Xerxes, Eastham, Paul, Gipson, Bryant, Higdon, Dave, Kucuktunc, Onur, Lieber, Allison, Lewis, Bryan L., Reynolds, Zane, Vullikanti, Anil K., Wang, Lijing, & Marathe, Madhav. Forecasting influenza activity using machine-learned mobility map. United States. https://doi.org/10.1038/s41467-021-21018-5
Venkatramanan, Srinivasan, Sadilek, Adam, Fadikar, Arindam, Barrett, Christopher L., Biggerstaff, Matthew, Chen, Jiangzhuo, Dotiwalla, Xerxes, Eastham, Paul, Gipson, Bryant, Higdon, Dave, Kucuktunc, Onur, Lieber, Allison, Lewis, Bryan L., Reynolds, Zane, Vullikanti, Anil K., Wang, Lijing, and Marathe, Madhav. Tue .
"Forecasting influenza activity using machine-learned mobility map". United States. https://doi.org/10.1038/s41467-021-21018-5. https://www.osti.gov/servlets/purl/1773047.
@article{osti_1773047,
title = {Forecasting influenza activity using machine-learned mobility map},
author = {Venkatramanan, Srinivasan and Sadilek, Adam and Fadikar, Arindam and Barrett, Christopher L. and Biggerstaff, Matthew and Chen, Jiangzhuo and Dotiwalla, Xerxes and Eastham, Paul and Gipson, Bryant and Higdon, Dave and Kucuktunc, Onur and Lieber, Allison and Lewis, Bryan L. and Reynolds, Zane and Vullikanti, Anil K. and Wang, Lijing and Marathe, Madhav},
abstractNote = {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.},
doi = {10.1038/s41467-021-21018-5},
journal = {Nature Communications},
number = 1,
volume = 12,
place = {United States},
year = {Tue Feb 09 00:00:00 EST 2021},
month = {Tue Feb 09 00:00:00 EST 2021}
}
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