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Threshold, Amory Faulkner, oil on canvas The prospects of elimination of HIV with test and treat strategy Mirjam Kretzschmar 1,2 Maarten Schim van der Loeff Daniela de Angelis 4 Roel Coutinho 1,2 26July 2012 AIDS conference 1 Washington DC 1 2 3 4 3 Centre for Infectious Disease Control, RIVM University Medical Centre Utrecht, NL Municipal Health Service, Amsterdam, NL MRC Biostatistics Unit, Cambridge, UK Test and treat strategy Treatment and infectivity Cohen et al. NEJM 2011 Elimination possible? From Granich et al. Lancet 2009 Aims of our study: • Modify and generalize the model to include knowledge on natural history • Derive and analyze under what conditions elimination is possible Generalized model structure • Variable number of compartments with variable duration • Variable infectivity -> better description of natural history including variable infectivity Epidemic dynamics Epidemic growth and elimination are threshold phenomena => linear analysis Transient dynamics Exponential growth Elimination Exponential growth at the start of the epidemic, growth rate determined by R0 Exponential decay at elimination, decay rate determined by Re Analysis • • • • • • • Determine explicit expressions for R0 and Re from model equations Estimate disease progression parameters from data (CASCADE collaboration) Use information about distribution of infectivity (Hollingsworth et al. JID 2008) Relate infectivity to epidemic growth rate r and R0 via generation interval distribution (Wallinga & Lipsitch 2007) Estimate r from incidence or doubling time at onset of epidemic From R0 determine transmission parameter λ Elimination threshold is determined as function of coverage and adherence to treatment Assumption: populations and behaviors driving HIV transmission during growth phase also determine transmission dynamics during elimination . • • • • 3 infection stages Progression rates from CASCADE Infectivity from Hollingsworth 2008 Relationship r and R0: Elimination threshold Elimination threshold Re as function of transmission and intervention parameters Elimination possible if Re < 1 From incidence or doubling time during exponential growth phase estimate r From r compute R0 Determine elimination threshold for given R0 If more recent estimates of R0 are available these can be used (e.g. from genetic data) Elimination threshold 6.0 For R0 > 6 elimination not possible Elimination possible for low risk populations For the coverage and drop out rate assumed by Granich elimination is feasible if R0<5 4.6 4.2 3.9 3.5 3.0 2.5 R0=1.5 Estimates from literature Population r (1/yr) South Africa R0 d (yrs) 7.0 1.25 Data type Ref incidence Granich 2009 France 1.15 3.65 incidence Nishiura 2010 West Germany 2.15 4.08 incidence Nishiura 2010 UK 1.21 3.67 incidence Nishiura 2010 SSAC 0.273 2.536 genetic Walker 2005 High income countries 0.479 1.446 genetic Walker 2005 genetic Salemi 2008 incidence Gran 2008 genetic Bello 2007 incidence Bezemer 2008 Albania England & Wales Brazil MSM Netherlands 0.69 8.0 0.9049 10.05 0.55 1.25 2.39 Conclusions Elimination is a threshold phenomenon. Information about possible elimination can be obtained from epidemic growth rate and generation interval distribution. Elimination is only feasible for populations with low basic reproduction numbers or if the reproduction number is lowered significantly as a result of other additional interventions. High infectivity during primary infection significantly increases the elimination threshold. If reliable estimates for R0 could be obtained from phylogenetic analysis prospects of elimination could be quantified more reliably. Acknowledgements Co-authors: Maarten Schim van der Loeff Infectious Disease Unit, Municipal Health Service Amsterdam, The Netherlands Daniela de Angelis MRC Biostatistics Unit, Cambridge UK Roel Coutinho Center for Infectious Disease Control, RIVM and University Medical Center Utrecht, The Netherlands CASCADE Collaboration Paul Birrell, MRC Biostatistics Unit, Cambridge UK