Individual decisions can have a large impact on society as a whole. Individuals decide how to vote, whether or not to stay at home when they feel sick, to drive or to take the bus. In isolation, these individual decisions have a negligible social outcome, but collectively they determine the results of an election and the start of an epidemic. For many years, studying these processes was limited to observing the outcomes or to analysing small samples. New data sources and data analysis tools have made it possible to start studying the behaviour of large numbers of individuals, enabling the emergence of large-scale quantitative social research.
Here, we present an application of these new datasets and methods to qualitatively gauge the probability of the onset of an epidemic, using influenza as a case study.
Every year, influenza epidemics affect millions of people and place a strong burden on health care services. A timely knowledge of the onset of the epidemic could allow these services to prepare for the peak. However, very few papers have approached the problem of onset identification, usually focusing on peak prediction. By combining official Influenza-Like Illness (ILI) incidence rates, searches for ILI-related terms on Google (such as flu or fever), and, in the case of Portugal, an on-call triage phone service, Saúde 24 (S24), we were able to reliably identify and signal the influenza outbreak in 8 European countries, anticipating current official alerts by several weeks. This work showed that it is possible to detect and consistently anticipate the onset of the flu season, regardless of the amplitude of the epidemic, with obvious advantages for health care authorities. This method is not limited to one country, specific region or language and, also due to both its simplicity and to the fact that it can be used with different input data, it can be used in early detection of other contagious or seasonal diseases. We also analysed a new and very useful data source: the on-call triage service S24, which receives and expertly responds to nationwide health-related phone calls, providing clinical advice. Being a triage system connected to the medical care network, it can serve to optimize services and direct patients to and away from emergency rooms. Such a system, when properly analysed, can be very useful for the quick identification of new epidemics or outbursts of diseases, whether contagious or not, local or global.