Epidemics and population structure: One step forward, and two steps back
Seminar Room 1, Newton Institute
AbstractIn general, the incorporation of population structure into epidemic models creates problems of dimensionality for prediction (the `forward problem'). Even for the `simple epidemic' / SI model, complete individual heterogeneity of n individuals leads to a dynamical system whose size grows like 2^n.
There are, however, two `inverse problems' where this curse becomes a blessing: for statistical inference, flat directions in parameter space can become identifiable once more stratification of data is available; and the presence of population structure allows a far wider range of control and mitigation strategies to be compared than are possible in a homogeneous system.
This talk will consider: (i) the generation of predictions from heterogeneous epidemic models without excessive dimensionality; (ii) the use of multiple stratified data sources to resolve statistical questions about the otherwise unidentifiable but epidemiologically important quantities; (iii) informing public health policy on the basis of these considerations. Real-world examples will come from the 2009 H1N1 influenza pandemic.
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