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nature outlook article
OUTLOOK 18 September 2019
Real-time flu tracking
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By monitoring social media, scientists can monitor outbreaks as they happen.
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Conventional influenza surveillance describes outbreaks of flu that have already happened. It Subjects
is based on reports from doctors, and produces data that take weeks to process — often Diseases Epidemiology Health care
leaving the health authorities to chase the virus around, rather than get on top of it.
Computer science
But every day, thousands of unwell people pour details of their symptoms and, perhaps
unknowingly, locations into search engines and social media, creating a trove of real-time flu
data. If such data could be used to monitor flu outbreaks as they happen and to make accurate
predictions about its spread, that could transform public-health surveillance.
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social media, but also cloud-based electronic health records and
human mobility patterns inferred from census information —
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“We’re becoming much more comfortable with how these
models perform,” says Matthew Biggerstaff, an epidemiologist Sign up
Part of Nature Outlook:
who works on flu preparedness at the US Centers for Disease
Influenza
Control and Prevention (CDC) in Atlanta, Georgia.
In 2013–14, the CDC launched the FluSight Network, a website informed by digital modelling
that predicts the timing, peak and short-term intensity of the flu season in ten regions of the
United States and across the whole country. According to Biggerstaff, flu forecasting helps
responders to plan ahead, so they can be ready with vaccinations and communication
strategies to limit the effects of the virus. Encouraged by progress in the field, the CDC
announced in January 2019 that it will spend US$17.5 million to create a network of influenza-
forecasting centres of excellence, each tasked with improving the accuracy and
communication of real-time forecasts.
The CDC is leading the way on digital flu surveillance, but health agencies elsewhere are
following suit. “We’ve been working to develop and apply these models with collaborators
using a range of data sources,” says Richard Pebody, a consultant epidemiologist at Public
Health England in London. The capacity to predict flu trajectories two to three weeks in
advance, Pebody says, “will be very valuable for health-service planning.”
Spread betting
Digital flu surveillance was transformed when Google turned its attention to flu forecasting in
2008. The company’s surveillance platform, called Google Flu Trends, used machine learning
to fit flu-related searches together with time-series data gathered by the CDC’s US Outpatient
Influenza-like Illness Surveillance Network (ILINet). With 3,500 participating clinics — each
counting how many people show up with sore throats, coughs and fevers higher than 37.8 °C
with no cause other than influenza — ILINet is the benchmark for flu monitoring in the United
States. The aim of Google Flu Trends was to estimate flu prevalence sooner than the ILINet
data could.
But two high-profile failures belied the media fanfare of its launch. First, Google Flu Trends
missed a spring pandemic of H1N1 flu in 2009. Then it overestimated the magnitude of the
2012–13 flu season by 140%.
According to Mauricio Santillana, a computational scientist at Harvard Medical School in
Boston, Massachusetts, the system failed because many of the selected search terms were
only seasonal, with limited relevance to flu activity, making the predictions noisy and
inaccurate. After the H1N1 debacle, Google revised its flu-tracking algorithm. But the
algorithm was not routinely recalibrated when the company’s search-engine software was
upgraded, and that created additional problems. In 2015, Google dropped the platform
altogether, although it still makes some of its anonymized data available for flu tracking by
researchers.
The demise of Google Flu Trends raised concerns about the role of big data in tracking
diseases. But according to Vasileios Lampos, a computer scientist at University College
London, the accuracy of flu forecasting is improving. “We have a lot more data and the
computational tools have improved,” he says. “We’ve had a lot of time to work on them.”
Santillana points out that machine learning has markedly improved in the years since Google
Flu Trends folded. “With more sophisticated approaches, it’s possible to automatically ignore
spuriously correlated terms, so the predictions are more robust,” he says.
Competitive advantage
The proving ground for new approaches to modelling is an annual forecasting challenge
hosted by the CDC. About 20 teams participate every year, and the winners are those that
perform best relative to the ILINet benchmark. In the absence of these models, the CDC’s
approach has been to estimate future trends based on what ILINet data gathered from
previous flu seasons would predict for each region and for the United States as a whole. But
during the 2017–18 flu season, most of the models in the challenge generated predictions
more accurate than those using ILINet’s historical baseline. The CDC now incorporates
several of the challenge’s top-performing models into its FluSight system.
A person stands on the left in front of a screen giving a presentation to five people
The Delphi research group at Carnegie Mellon University forecasts the spread of influenza. Credit:
Carnegie Mellon Univ.
For the past four years, the winner of the CDC’s challenge has been a team led by computer
scientist Roni Rosenfeld of Carnegie Mellon University in Pittsburgh, Pennsylvania.
Rosenfeld’s team, called the Delphi Research Group, bases its predictions on two
complementary systems. One is an online crowdsourcing website called Epicast that allows
people to express their opinions about how the current flu season might play out. “Epicast
exploits the wisdom of the crowds,” Rosenfeld says. “The opinion of any one person who
responds isn’t as accurate as the aggregated opinions of all the responders together.”
The team’s second system relies on machine-learning algorithms that repeatedly compare
trends observed during the current flu season with those seen in previous decades. The
algorithm draws on historical ILINet data as well as data from search engines and social media
to assemble a distribution of all possible seasonal trajectories. It then models how the current
season differs at the moment, and how it is likely to differ as it continues.
As well as machine learning, researchers also rely on mechanistic models that work in a
fundamentally different way. Machine learning merely looks for patterns in data, whereas
mechanistic approaches depend on specific assumptions about how a flu virus moves
through the population. “This often requires biological and sociological understanding
about the way disease transmission really works,” says Nicholas Reich, a biostatistician at the
School of Public Health and Health Sciences at the University of Massachusetts Amherst. “For
instance, mechanistic models take into account the susceptible fraction of the population,
the transmissibility of a particular virus, and social-mixing patterns among infected and non-
infected people.”
At Northeastern University in Boston, Massachusetts, Alessandro Vespignani, a
computational scientist who models epidemics, has been forecasting flu by using agent-
based approaches that he describes as “mechanistic modelling on steroids”. Agents are
simply interacting entities, including people, and Vespignani has modelled 300 million
individuals, representing the US population, in various settings, and simulated how the flu
virus moves among them in workplaces, homes and schools. The agent-based approach
allows researchers to zoom in on disease transmission patterns with high spatial resolution.
The downside is that these models require high-performance computing, Vespignani says,
“and they’re also data-hungry, in that they require very detailed societal descriptions.”
Vespignani and Santillana are now collaborating on ways to combine machine learning with
the agent-based approach to create what they claim would be an even stronger flu-forecasting
model.
Strength in numbers
Researchers have started to combine models into ‘ensembles’ that have more forecasting
power than the constituent models alone. “This is something we’ve learned from the
challenges,” Biggerstaff says. “Combinations work better.” That has certainly been the
experience of the FluSight Network, which is a consortium of four independent research
teams that collaborate on a multimodel ensemble. The ensemble links 21 models — some that
use machine learning and others that are mechanistic — into a single composite model that
took second place in the latest CDC flu-forecasting challenge, just behind Rosenfeld’s team.
The models in this case are combined using a method called stacking, which weighs their
contributions based on how well they each performed during previous flu seasons. According
to Reich, who directs one of the FluSight Network’s four participating teams, the ensemble
approaches make optimal use of the component models’ idiosyncrasies. The stacking
approach, he says “is like conducting them in a symphony. You want each model at its
appropriate volume.”
Modelled flu forecasts, however, face a series of hurdles before
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they can be factored routinely into public-health preparedness
in the way that, for instance, weather forecasts are used to plan
for storms. To be truly effective, even the best model needs to be
paired with policy measures that take into account the trends
revealed by the software. But Vespignani says it is not entirely
clear how confident policymakers and health officials are when
More from Nature Outlooks
it comes to using modelled flu forecasts in real-world settings.
Many of these individuals have a poor understanding of how the
computational models work, he says, and the models are most accurate at forecasting flu two
to four weeks in advance, which does not really provide enough time to allocate resources
where they are most needed. Vespignani says that models that could reliably predict the peak
and intensity of the flu season six to eight weeks in advance would be more useful.
Santillana says that more research is needed into how social behaviour, vaccination
programmes, strain composition, population immunity and other factors affect the models’
accuracy. But researchers also need to understand how spatial scales factor into forecasting.
For example, the CDC’s forecasts are limited to national and regional levels but investigators
have begun to consider the prospects for city-scale forecasts, as well as forecasting across
global hemispheres.
Meanwhile, work is under way to provide machine-learning-enabled forecasting in
developing countries that lack surveillance data. Lampos trained a model using surveillance
data from the United States, and reported that it was accurate at forecasting flu in France,
Spain and Australia without drawing on historical data from any of those countries. He says
this approach could work in poorer locations that lack comparable surveillance
infrastructure by analysing the frequency of search queries for flu on mobile phones and
other devices. Lampos now plans to test his model in countries in Africa.
There is still a long way to go before flu forecasting becomes as routine and widely accepted
as weather forecasting. But Santillana says that progress is advancing rapidly. “The
predictions,” he says, “are getting better and better.
Nature 573, S58-S59 (2019)
doi: https://doi.org/10.1038/d41586-019-02755-6
This article is part of Nature Outlook: Influenza, an editorially independent supplement
produced with the financial support of third parties. About this content.
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