Being prepared for a pandemic, like COVID-19, depends on the ability to predict the course of the pandemic and the human behaviour that drives spread in the event of an outbreak. Cell phone metadata that is routinely collected by telecommunications providers can reveal changes of behavior in people who are diagnosed with a flu-like illness, while also protecting their anonymity, a new study has found. The research, led by Emory University and devised by the University of Bristol, is based on data drawn from a 2009 outbreak of H1N1 influenza in Iceland and published in Proceedings of the National Academy of Sciences (PNAS).

Dr Ymir Vigfusson, Assistant Professor in the Department of Computer Science at Emory University and first author of the study, said: "To our knowledge, our project is the first major, rigorous study to individually link passively-collected cell phone metadata with actual public health data, says. Weve shown that its possible to do so without compromising privacy and that our method could potentially provide a useful tool to help monitor and control infectious disease outbreaks."

The research team collaborated with a major cell phone service provider in Iceland, along with public health officials on the island. They analyzed metadata for over 90,000 encrypted cell phone numbers, which represents over a quarter of Icelands population. They were able to link the encrypted cell phone metadata to 1,400 anonymous individuals who received a clinical diagnosis of a flu-like illness during the H1N1 outbreak, while preserving privacy at all stages. The study, which began long before the COVID-19 pandemic, took ten years to complete.

Dr Vigfusson stated: "The individual linkage is key. Many public-health applications for smartphone data have emerged during the COVID-19 pandemic but tend to be based around correlations. In contrast, we can definitively measure the differences in routine behavior between the diagnosed group and the rest of the population."

Dr Leon Danon, Associate Professor in Infectious Disease Modelling and Data Analytics from the University of Bristol, and the Alan Turing Institute, senior author and who designed the study, added: "This careful study was designed to do two things: inform mathematical models that seldom take into account behaviour change due to infection and provide evidence for infectious disease surveillance through mobile phone data. The result that behaviour change is clearly observable in our study points to the tantalising possibility that infectious disease burden is measurable through routinely collected data, our future direction. This work required close collaboration between a government health department and a mobile phone operator and highlights the power of close ties between academic efforts, government and industry."

The results showed that, on average, those who received a flu-like diagnosis changed their cell phone usage behavior a day before their diagnosis and the two-to-four days afterward: They made fewer calls, from fewer unique locations. On average, they also spent longer time than usual on the calls that they made on the day following their diagnosis.

Dr Vigfusson added: "We were going into new territory and we wanted to make sure we were doing good science, not just fast science. We worked hard and carefully to develop protocols to protect privacy and conducted rigorous statistical analyses of the data.

"While only about 40 per cent of humanity has access to the Internet, cell phone ownership is universal, even in lower and middle-income countries, and cell phone service providers routinely collect billing data that provide insights into the routine behaviors of a population", he explained.

Dr Vigfusson added: "The COVID pandemic has raised awareness of the importance of monitoring and measuring the progression of an infectious disease outbreak, and how it is essentially a race against time.

"More people also realise that there will likely be more epidemics during our lifetimes. It is vital to have the right tools to give us the best possible information quickly about the state of an epidemic outbreak."

Privacy concerns are a major reason why cell phone data has not been linked to public health data in the past. For the PNAS paper, the researchers developed a painstaking protocol to minimise these concerns.

The cell phone numbers were encrypted, and their owners were not identified by name, but by a unique numerical identifier not revealed to the researchers. These unique identifiers were used to link the cell phone data o deidentified health records.

Dr Vigfusson said: "We were able to maintain anonymity for individuals throughout the process. The cell phone provider did not learn about any individuals health diagnosis and the health department did not learn about any individuals phone behaviors."

The study encompassed 1.5 billion call record data points including calls made, the dates of the calls, the cell tower location where the calls originated, and the duration of the calls. The researchers linked this data to clinical diagnoses of a flu-like illness made by health providers in a central database. Laboratory confirmation of influenza was not required.

The analyses of the data focused on 29 days surrounding each clinical diagnosis, and looked at changes in mobility, the number of calls made and the duration of the calls. They measured these same factors during the same time-period for location-matched controls.

Dr Vigfusson added: "Even though individual cell phones generated only a few data points per day, we were able to see a pattern where the population was behaving differently near the time they were diagnosed with a flu-like illness."

While the findings are significant, they represent only a first step for the possible broader use of the method. Specifically, if an emerging disease displays a sufficiently distinct signature of behavioral changes, the methodology could prove useful to augment monitoring efforts.

The current work was limited to the unique environment of Iceland: an island with only one port of entry, with a similar, affluent, and small population. It was also limited to a single infectious disease, H1N1, and those who received a clinical diagnosis for a flu-like illness.

Dr Vigfusson concluded: "Our work contributes to the discussion of what kinds of anonymous data linkages might be useful for public health monitoring purposes. We hope that others will build on our efforts and study whether our method can be adapted for use in other places and for other infectious diseases."

Paper

'Cell-phone traces reveal infection-associated behavioral change' by Ymir Vigfusson et al in Proceedings of the National Academy of Sciences (PNAS)

Dr Ymir Vigfusson, Assistant Professor at Emory University, USA, is an expert on data security and developing software and algorithms that work at scale.

Dr Vigfusson is first author of the study with two of his former graduate students: Thorgeir Karlsson, a graduate student at Reykjavik University who spent a year at Emory working on the project, and Derek Onken, a PhD student in the Computer Science department.

Dr Leon Danon, Associate Professor in Infectious Disease Modelling and Data Analytics from the University of Bristol, and the Alan Turing Institute of the British Library, devised the study.

Co-authors include the late Gudrun Sigmundsdottir, Directorate of Health and Icelands Center for Health Security and Communicable Disease Control; Congzheng Song (Cornell University); Atli F. Einarsson (Reykjavik University); Nishant Kishore (Harvard University); Rebecca M. Mitchell (formerly with Emorys Nell Hodgson Woodruff School of Nursing); and Ellen Brooks-Pollock (University of Bristol).

The work was funded by the Icelandic Center for Research, Emory University, the National Science Foundation, the Leverhulme Trust, the Alan Turing Institute, the Medical Research Council, and a hardware donation from NVIDIA Corporation.

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