Some challenges to make current data-driven (‘statistical’) models even more relevant to public health
Seminar Room 1, Newton Institute
AbstractThere 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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