Final Report Summary - EPIFOR (Complexity and predictability of epidemics: toward a computational infrastructure for epidemic forecasts)
New advances in science and medicine help us gain ground against certain infectious diseases, yet new infections continue to emerge that spread rapidly into the population and frequently reach pandemic proportions causing significant human and economic costs. Computational epidemiology, as an interdisciplinary field integrating complex systems with statistical physics approaches, computational sciences, mathematical epidemiology, Information Communication Technologies (ICT) and Geographic Information Systems, can help confronting this reality by offering new tools as important as medical, clinical, genetic or molecular diagnosis tools – namely, computational models. Within this framework, EpiFor has collected and analyzed massive datasets describing hosts behavior, interactions and movements, available from top-down traditional sources (e.g. national statistics and surveys) and from bottom-up alternative digital means (e.g. mobile phone data, RFID technology). Thanks to increasingly powerful CPU capabilities, the project allowed the development of sophisticated intensive algorithms to describe complex spreading processes integrating such data, and the assessment of their role on disease propagation. Most importantly, the development of realistic computational models for the simulation of infectious disease spread offered the possibility to provide a synthetic framework where to conduct experiments not feasible in the real world, aimed at preparedness and control of epidemics. Confronted with two emerging infectious disease epidemics with pandemic potential during the lifetime of the project – namely the 2009 H1N1 pandemic and the current MERS-CoV epidemic originated in the Arabic Peninsula – EpiFor had the possibility to concretely test the novel approaches developed in the project in a real-life situation. Our results showed and proved the ability of our approaches to assess an ongoing epidemic emergency and to provide useful predictions characterizing the future spread of the disease in the population, and informing public health policies ahead of time.
With less than 10 years since the first publications, models have offered an additional insight in response planning. The progress has been dramatic. As a by-product, however, such progress has also created an increased demand for quantitative, realistic, detailed and reliable data-driven computational models for the simulation of epidemic spread to guide decision-making processes. Used for the first time during an influenza pandemic event in the 2009 H1N1 case, models have indeed also uncovered their current limits.