Ever-growing populations mean constant new sources of infection, while modern connectivity ensures very rapid spread. The combination raises the possibility of pandemics, yet the new field of computational epidemiology offers tools for predicting the spread. The EU-funded EPIFOR (Complexity and predictability of epidemics: Toward a computational infrastructure for epidemic forecasts) project conducted fundamental studies of the models presently used. Research questions addressed the theoretical realism of current models, and the limits of epidemiological predictability using such models. EPIFOR was one of few groups to collate and analyse various huge epidemiological data sets. From such sources, the team developed new algorithms. Researchers tested the results on data from the 2009 H1N1 pandemic and the more recent MERS-CoV epidemic. Outcomes demonstrated an ability to assess ongoing epidemic emergencies and to predict spread of diseases. The EPIFOR project's advancement of the field of computational epidemiology has resulted in new predictive abilities. Such results help inform public health response to infectious disease emergencies.
Epidemic, H1NI, MERS-CoV, pandemics, computational epidemiology, EPIFOR