Paul Birrell|Richard Pebody|André Charlett|Xu?Sheng Zhang|Daniela De Angelis
Real-time modelling of a pandemic influenza outbreak
MRC Biostatistics Unit|Medical Research Council|University of Cambridge|Public Health England|Public Health England|Public Health England|MRC Biostatistics Unit|Medical Research Council|University of Cambridge|Public Health England
realtime modelling essential component public health response outbreak pandemic influenza uk model epidemic reconstruction based realistic epidemic surveillance data developed model needs enhancing provide spatially disaggregated epidemic estimates ensuring realtime implementation feasibleto advance stateoftheart realtime pandemic modelling 1 developing existing epidemic model capture spatial variation transmission 2 devising efficient computational algorithms provision timely statistical analysis 3 incorporating freely available softwaremarkov chain monte carlo mcmc sampling used derive bayesian statistical inference using 2009 pandemic data two candidate modelling approaches 1 parallelregion pr approach splitting pandemic noninteracting epidemics occurring spatially disjoint regions 2 metaregion mr approach treating country single metapopulation longrange contact rates informed census data commuting model discrimination performed posterior mean deviance statistics alongside practical considerations realtime context use sequential monte carlo smc algorithms carry realtime analyses investigated alternative mcmc using simulated data designed sternly test algorithms smcderived analyses compared goldstandard mcmcderived inferences terms estimation quality computational burdenthe pr approach provides better timely fit epidemic data estimates pandemic quantities interest consistent across approaches pr approach across regions eg r0 consistently estimated 176180 dropping 4350 oversummer school holiday smc approach developed required tailoring tackle sudden shock data resulting pandemic intervention semiautomated smc algorithm outperforms mcmc terms precision estimates timely provision software implementing findings developed installed within public health england phe key staff trained usethe pr model lacks predictive power forecast spread infection early stages pandemic whereas mr model may limited dependence commuting data describe transmission routes demand resources increases severe pandemic data general practices hospitalisations may become unreliable biased smc algorithm developed semiautomated therefore statistical literacy required achieve optimal performancefollowing objectives study found timely spatially disaggregate realtime pandemic inference feasible system assumes data per pandemic preparedness plans developed rapid implementationmodelling studies investigating impact pandemic interventions eg vaccination school closure utility alternative data sources eg internet searches augment traditional surveillance correct handling test sensitivity specificity serological data propagating uncertainty realtime modellingcurrent controlled trials isrctn40334843this project funded national institute health research nihr health technology programme published full health technology assessment vol 21 58 see nihr journals library website project information daniela de angelis supported uk medical research council unit programme number u105260566 phe received funding nihr grant 10 time rest salary provided mrc phe jointly