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Some challenges to make current data-driven (‘statistical’) models even more relevant to public health

Presented by: 
Ottar Nordal Bjornstad Pennsylvania State University
Tuesday 20th August 2013 - 14:30 to 15:00
INI Seminar Room 1
Session Title: 
Data & Statistics: New Methods

There has been enormous progress in parameterizing epidemic models using incidence data in the 20 years since the Newton meeting on Epidemic models. This came about through a combination of computational innovations, model development to embrace critical biological realism, and increasingly resolved incidence data with respect to age, time and space. I will highlight what I think are key challenges to data-driven epidemic modeling to advice future intervention policies. Some critical issues are (i) robust forecasting in the face of rapidly changing demographies and vaccination schedules; (ii) probabilistically projecting possible/probable build-up of ‘susceptible pockets’ in the face of imperfect vaccination programs; and (iii) use nonlinear stochastic modeling to identify all potentially undesirable side-effects of intervention-induced reduction in circulation.

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Presentation Material: 
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons