What We Can Learn from the H1N1 Flu Pandemic
David Kalat
As the world grapples with COVID-19, we can draw lessons from steps taken to combat the H1N1 flu pandemic
When the 39-year-old woman in Oaxaca, Mexico, died, doctors chalked it up to severe atypical pneumonia. But the lab tests later confirmed she was the victim of a new, unknown coronavirus.
This might sound like something from today’s headlines, but the coronavirus that killed Adela Maria Gutierrez in April 2009 was not COVID-19 but a new strain of swine flu. Also known as H1N1, that epidemic resulted in the deaths of hundreds of thousands of people.
Today, as the world struggles with the almost unthinkable effects of another novel coronavirus, COVID-19, authorities should draw lessons from history. The specific behaviors and treatments of H1N1 represented uncharted territory, and to conquer it, the world needed to first understand how many people were infected, how quickly the virus spread and whether countermeasures like social distancing and closing schools were effective.
That probably sounds familiar.
What the H1N1 flu pandemic can teach us about COVID-19
In 2009, disease control specialists found a powerful new tool in the form of internet-enabled devices. At the time, the relatively new phenomenon of people habitually carrying smartphones on their persons and integrating them into their daily lives was a sort of revelation.
Researchers in Mexico selected a random sample of almost 1 million cellular telephone numbers from the pool of 18 million numbers in the nationwide network of prepaid phones. The scientists texted to this random sample an SMS message containing a survey prepared by the Ministry of Health. They asked for information about flu-like illnesses—“When had the fever onset?” “How high a fever?” “How many people in the household?” “What is their vaccination history?” And so on.
The questions were a form of “syndromic surveillance,” a technique developed by the U.S. Centers for Disease Control and Prevention (CDC) in the late 1990s as part of a larger antiterrorist preparedness plan. The task of diagnosing confirmed instances of a specific disease or pathogen is time consuming and consumptive of limited resources, such as physicians’ time, and this delay can be catastrophic when a rapid response is needed to contain an outbreak. Syndromic surveillance focuses instead on the collection of data about symptoms, which can be self-reported, anecdotal and independent of licensed health providers.
Survey respondents provided critical data that helped show that the disease appeared to peak in late April 2009 and that incidences of infection dropped noticeably when schools were on vacation or closed. This helped track the effectiveness of the countermeasures being used to slow the virus’s spread.
The spread (of innovation) outside Mexico
Other countries experimented with the tools of mobile messaging as well. In the United Kingdom, health officials deployed an internet-based syndromic surveillance study to solicit data from the public. The researchers generally found the internet-derived data to be better at estimating the overall arc of the pandemic in the UK than the estimates derived from physician-recorded diagnoses.
Meanwhile, Chinese authorities used internet-based communications to send rather than collect data. The Chinese trial used SMS messages to transmit information about the pandemic and found it to be an effective tool for informing the public, shaping attitudes and encouraging vaccinations.
Technology deployed to fight COVID-19
Facebook has started sharing certain data with researchers studying the current COVID-19 crisis. The data allows scientists to visualize directly how people are moving, how closely packed they are and how they are encountering each other. It is anonymized and aggregated—no individual user’s name or identification is disclosed. Furthermore, only users who voluntarily set their accounts to share their location data with Facebook are even included in the aggregated data.
“The data allows scientists to visualize directly how people are moving, how closely packed they are and how they are encountering each other.”
Along the same lines, Unacast is collating GPS data from smartphone users to track the movement and density of people in the US. Unacast’s interactive map uses color codes to graphically represent where social distancing is being practiced, and how that correlates to new cases of infection.
Facebook and Unacast claim that their data collection operations described above comply with the various data privacy regulations, like the European Union's General Data Protection Regulation and the California Consumer Privacy Act, and that the use of anonymized, aggregated data is consistent with that claim. Still, other emergent ways of using smartphone data exist to track the pandemic that implicate individual privacy more directly.
For example, several countries have deployed “digital tracking” measures to monitor the movements of individuals believed to be infected—and to collect information about their contacts. In some instances, individuals have received an SMS message from their government(s) informing them that they came into contact with a confirmed coronavirus case and should self-quarantine.
The recently passed CARES Act includes a provision that sets aside $500 million for the CDC to develop a “public health surveillance and data collection system for coronavirus within 30 days of enactment of this Act.” Time will tell what form that takes and how much that surveillance system owes to the pioneering experiments in 2009 surrounding the H1N1 crisis.