Notwithstanding a marked recent increase in systematic surveillance by national and international organizations, and the advent of real time reporting of many public health indicators (syndromics) [24] , the epidemiological regime of incipient but evolving transmission has received little attention in terms of quantitative modelling [22, [25] [26] [27] [28] . The main difficulty is that data in these circumstances tend to be very stochastic, involve small case numbers and may be plagued by uncertainties and inconsistent reporting. As an example, we contrast in Figure 1 the time series of confirmed new human cases of H5N1 avian influenza in Vietnam, reported by the World Health Organization (WHO), with weekly isolate numbers for seasonal H3N2 influenza in the USA during 2004-2005 (see Methods for ''Data Sources''). The ultimate objective of this paper is to propose a methodology to extract quantitative inferences and generate epidemiological outlook in real time from time series like that of Figure 1a .