Real Time Numerical Forecast of Global Epidemic Spreading
Alessandro Vespignani, School of Informatics and Computing, Indiana University (March 19, 2012)
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Mathematical and computational models are increasingly used in support decisions in public health, however the perception of their reliability and the criteria for their uses is contrasted among domain experts. We consider the Global Epidemic and Mobility model that generates stochastic realizations of epidemic evolution worldwide from which we can gather information such as prevalence, morbidity, number of secondary cases and number and date of imported cases for 3,360 subpopulation in 220 countries with a time resolution of 1 day. GLEaM has been used to anticipate the geographical spreading for the 2009 H1N1 pandemic by estimating the transmission potential and the relevant model parameters with a Monte Carlo likelihood analysis of the arrival time distribution generated by 1 million computationally simulated epidemics. We present an extensive validation analysis of the obtained results from surveillance and virological sources collected in 46 countries of the Northern Hemisphere during the course of the pandemic. We focus on discussing the challenges posed by the real-time estimation of parameters, the different levels of data-integration and the validation through high quality data sets. In particular, data gathered during and after the 2009 H1N1 influenza crisis represent an unprecedented opportunity to i) test the robustness of the prediction intervals with respect to additional parameters unknown concurrently or before the end of the pandemic; ii) test the sensitivity of prediction intervals to the different levels of data integration by considering progressively increasing knowledge of socio-demographic and human mobility data.