• Social media data could help predict the

    From ScienceDaily@1:317/3 to All on Tue Mar 22 22:30:48 2022
    Social media data could help predict the next COVID surge
    Research suggests a new forecasting approach using machine learning and anonymized datasets could revolutionize infectious disease tracking

    Date:
    March 22, 2022
    Source:
    University of Colorado at Boulder
    Summary:
    New research suggests that a novel, short-term forecasting method,
    using machine learning and vast, anonymized datasets from social
    media accounts, significantly outperforms conventional models for
    projecting COVID trends at the county level.



    FULL STORY ==========================================================================
    In the summer of 2021, as the third wave of the COVID-19 pandemic wore
    on in the United States, infectious disease forecasters began to call
    attention to a disturbing trend.


    ==========================================================================
    The previous January, as models warned that U.S. infections would
    continue to rise, cases plummeted instead. In July, as forecasts
    predicted infections would flatten, the Delta variant soared, leaving
    public health agencies scrambling to reinstate mask mandates and social distancing measures.

    "Existing forecast models generally did not predict the big surges and
    peaks," said geospatial data scientist Morteza Karimzadeh, an assistant professor of geography at CU Boulder. "They failed when we needed them
    most." New research from Karimzadeh and his colleagues suggests a new approach, using artificial intelligence and vast, anonymized datasets
    from Facebook could not only yield more accurate COVID-19 forecasts,
    but also revolutionize the way we track other infectious diseases,
    including the flu.

    Their findings, published in the International Journal of Data Science
    and Analytics, conclude this short-term forecasting method significantly outperforms conventional models for projecting COVID trends at the
    county level.

    Karimzadeh's team is now one of about a dozen, including those from
    Columbia University and the Massachusetts Institute of Technology (MIT), submitting weekly projections to the COVID-19 Forecast Hub, a repository
    that aggregates the best data possible to create an "ensemble forecast"
    for the Centers for Disease Control. Their forecasts generally rank in
    the top two for accuracy each week.



    ========================================================================== "When it comes to forecasting at the county level, we are finding that
    our models perform, hands-down, better than most models out there,"
    Karimzadeh said.

    Analyzing friendships to predict viral spread Most COVID-forecasting
    techniques in use today hinge on what is known as a "compartmental
    model." Simply put, modelers take the latest numbers they can get
    about infected and susceptible populations (based on weekly reports of infections, hospitalizations, deaths and vaccinations), plug them into
    a mathematical model and crunch the numbers to predict what happens next.

    These methods have been used for decades with reasonable success but they
    have fallen short when predicting local COVID surges, in part because
    they can't easily take into account how people move around.

    That's where Facebook data comes in.



    ========================================================================== Karimzadeh's team draws from data generated by Facebook and derived from
    mobile devices to get a sense of how much people travel from county
    to county and to what degree people in different counties are friends
    on social media. That matters because people behave differently around
    friends.

    "People may mask up and social distance when they go to work or shop,
    but they may not adhere to social distancing or masking when spending
    time with friends," Karimzadeh said.

    All this could influence how much, for instance, an outbreak in Denver
    County might spread to Boulder County. Often, counties that are not next
    to each other can heavily influence each other.

    In a previous paper in Nature Communications, the team found that social
    media data was a better tool for predicting viral spread than simply
    monitoring people's movement via their cell phones. With 2 billion
    Facebook users worldwide, there is abundant data to draw from, even in
    remote regions of the world where cell phone data is not available.

    Notably, the data is privacy-protected, stressed Karimzadeh.

    "We are not individually tracking anyone." The promise of AI The model
    itself is also novel, in that it builds on established machine- learning techniques to improve itself in real-time, capturing shifting trends in
    the numbers that reflect things like new lockdowns, waning immunity or
    masking policies.

    Over a four-week forecast horizon, the model was on average 50 cases
    per county more accurate than the ensemble forecast from the COViD-19
    Forecast Hub.

    "The model learns from past circumstances to forecast the future and it
    is constantly improving itself," he said.

    Thoai Ngo, vice president of social and behavioral science research for
    the nonprofit Population Council, which helped fund the research, said
    accurate forecasting is critical to engender public trust, assure that communities have enough tests and hospital beds for surges, and enable
    policy makers to implement things like mask mandates before it's too
    late."The world has been playing catch-up with COVID-19. We are always
    10 steps behind," Ngo said.

    Ngo said that traditional models undoubtedly have their strengths, but,
    in the future, he'd like to see them combined with newer AI methods to
    reap the unique benefits of both.

    He and Karimzadeh are now applying their novel forecast techniques to predicting hospitalization rates, which they say will be more useful to
    watch as the virus becomes endemic.

    "AI has revolutionized everything, from the way we interact with our
    phones to the development of autonomous vehicles, but we really have not
    taken advantage of it all that much when it comes to disease forecasting,"
    said Karimzadeh.

    "There is a lot of untapped potential there." Other contributors to this research include: Benjamin Lucas, postdoctoral research associate in the Department of Geography, Behzad Vahedi, Phd student in the Department
    of Geography, and Hamidreza Zoraghein, research associate with the
    Population Council.


    ========================================================================== Story Source: Materials provided by
    University_of_Colorado_at_Boulder. Original written by Lisa
    Marshall. Note: Content may be edited for style and length.


    ========================================================================== Journal Reference:
    1. Benjamin Lucas, Behzad Vahedi, Morteza Karimzadeh. A spatiotemporal
    machine learning approach to forecasting COVID-19 incidence at
    the county level in the USA. International Journal of Data Science
    and Analytics, 2022; DOI: 10.1007/S41060-021-00295-9 ==========================================================================

    Link to news story: https://www.sciencedaily.com/releases/2022/03/220322150900.htm

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