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Computational Modeling of Pandemic Influenza Control Strategies N.M. Ferguson, D.A. Cummings, C. Fraser, S. Cauchemez, S. Riley, A. Meeyai, S. Iamsirithaworn, W.D. Wheaton, P.C. Cooley, D.S. Burke Presented by Donald S. Burke, M.D. Workshop on Pandemic Influenza Vaccines: Building a Platform For Global Collaboration, Beijing, China, January 28-30, 2007 National Institutes of Health National Institute of General Medical Sciences Models of Infectious Disease Agent Studies University Of Washington & Los Alamos National Lab University of Pittsburgh & Imperial College Virginia Tech University Containment – what does it take (in theory)? • The spread of an infectious pathogen is characterised the basic reproduction number, R0 – the average number of secondary cases generated by a single case in an entirely susceptible population. • Control policies optimally reduce transmission so that R0 <1 – since at that level an epidemic cannot sustain itself. • Hence control policies need to eliminate a fraction 1-1/ R0 of transmission – i.e. 33% for R0 =1.5, 50% for R0 =2, 75% for R0 =4. • This can be achieved by: Reducing contact (quarantine, increasing social distance). Reducing susceptibility (vaccination, antiviral prophylaxis). Reducing infectiousness (antiviral treatment). • Key issues are who is targeted, how much effort is needed, and how fast do we need to act. Large Scale Model: 85+ million individuals Key Modeling Research Partner: Thailand Dr. Kumnuan and colleagues of the Thai Bureau of Epidemiology and the Field Epidemiology Training Program Population Density Social Contact Processes Individuals in households assigned to schools/workplaces with distance function based on data household workplace elementary school secondary school workplace SE Asia Movies Applying SE Asian containment policy to US? 1000000 • 1 billion course stockpile runs out on day 130 – overwhelmed by new introductions from overseas. • Other options, but comparably draconian. 100000 Daily cases • Social + 5km radial prophylaxis plus 100% school and 50% workplace reactive closure. 10000 1000 100 10 1 0 30 60 90 Day 120 150 180 Realistic Objectives for USA Pandemic Control 1. Diminish overall disease and death 2. Delay epidemic peak ( buy time) 3. Flatten epidemic peak ( limit surge burden on healthcare infrastructure ) #2 Pandemic outbreak: No intervention #3 Daily Cases Pandemic outbreak: With intervention #1 Days since First Case Possible mitigation measures Aim: minimize morbidity/mortality until vaccine available, using: 1. Antivirals 2. Case isolation 3. Household quarantine. 4. ‘Social distancing’ – e.g. school closure 5. Travel restrictions 6. Vaccines Pre-vaccination: A poorly matched vaccine • Assume availability of a low-efficacy pre-pandemic vaccine, given as soon as pandemic recognized. • Assume 30% reduction in susceptibility. If infected, 50% reduced chance of being a clinical ‘case’ and 30% additional reduction in infectiousness (matched vaccine would be expected to be 70-90% protective in healthy adults) • A 10% stockpile of pre-pandemic vaccine would reduce attack rates from 29% to 25% for next day treatment + school closure policy [R0=2]. • A 20% stockpile of pre-pandemic vaccine would reduce attack rates to 20%. • Targeting children <16 is best way of reducing transmission, given limited stocks of limited efficacy vaccine. Targeting >60s has worst impact. Impact of mass vaccination: a well matched vaccine • Examine scenarios of vaccine being available in US from day 30, 60 or 90 of the global epidemic (1st US case on day 47, peak or epidemic on day 113). • Doses for 1% of population are manufactured per day – v. optimistic. • Assume vaccine takes 21 days to confer 70% reduction in susceptibility. 1.8 1% 13% 31% 16% R0 1.5 Day 30, children first Day 60, children first Day 90, children first Day 60, random 0.0% 0.4% 1.6% 0.6% 0% 0.0% 2% 0.0% 19% 4% 0% 10% 20% 30% 40% Cumulative attack rate (%) 0.7% 0.1% 0.0% 1.0% 2.0% Peak daily attack rate (%) Strain-specific vaccination has v. limited impact on first wave of pandemic unless available within 2 months. Vaccinating children first gives best impact. Conclusions Computational modelling and simulation can be a useful tool to evaluate complex policy issues, such as timing and impact of vaccines Initial modelling results suggest that well matched vaccines must be available within two months to have an appreciable effect on the pandemic course Key sensitivities Many unknowns (transmissibility, natural history of infection, severity of disease, compliance with treatment/controls…) ! We will need to rapidly collect data and refine model projections of spread and effect of controls during the first few weeks of a pandemic END Nature 437: 209-214 (8 September 2005) Strategies for containing an emerging influenza pandemic in Southeast Asia Neil M. Ferguson, Derek A.T. Cummings, Simon Cauchemez, Christophe Fraser, Steven Riley, Aronrag Meeyai, Sopon Iamsirithaworn and Donald S. Burke Synthesis of an artificial population and using it to evaluate intervention strategies: “epidemiology in silicon” Age Distribution of Thai population Distribution of Household Sizes Distribution of School Sizes Probability of traveling over a certain distance to work Assumptions about influenza biology and treatment 16 8 6 4 2 . 5 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0 2 5000 Days 4000 1.5 3000 1 2000 0.5 1000 0 0 0 2 4 6 8 Days post infection 10 Viral load (data) • …but also can be used in uninfected people to reduce chance of being infected (=prophylaxis). 10 0 • Antiviral drugs reduce symptoms and infectiousness… Frequency • People most infectious very soon after symptoms. 12 . • Short incubation period – 1-2 days. 14 Infectiousness • New analysis of best available data on pandemic and inter-pandemic flu. Mitigating the effects of a pandemic on the USA Can we contain a pandemic? • Contain = eliminate virus before spread is extensive. • So long as stockpile is 3m courses or larger, - can contain R0~1.8. • Need to detect outbreak at <50 cases, react to new cases in 2 days. • Needs work to make feasible – but far more effective than anything else USA model data: demography Proportion 0.1 model 0.08 data 0.06 0.04 0.02 0 0 10 20 30 40 50 60 70 80 Age category 0.4 Proportion • Model uses Landscan population density data, and matches US census data on age & household structure. model 0.3 data 0.2 0.1 0 1 2 3 4 5 Household size 6 7 8 USA model: School/workplace data Using a data on all schools in US, plus data on workplace size/location. Frequency . 1000 Data Model 100 10 1 10 100 1000 10000 Fit of Zipf model 15000000 10000000 5000000 . 20000000 1 Number of workers 0.1 Probability 25000000 0.01 data model 0.001 0.0001 0.00001 0 15 610 11 -2 0 21 -5 51 0 -1 10 00 12 25 5 0 1 50 -50 1- 0 10 0 10 0 00 + Number of workers Size Workplace size category 0.000001 1 10 100 1000 Distance (km) 10000 Model validation • Validation difficult – no pandemic for 40 years. • We do try and match past pandemics’ rate of spread, proportion affected etc. • But data limited. • Key result: R0 for pandemic flu in 1.5-2.0 range. • Try to adjust for changes in populations, travel etc. R0 = Basic reproduction number = number of secondary cases per case at start of epidemic Model of a USA pandemic • Large urban centres affected first, followed by spread to less densely populated areas. R0 = 1.8 / 1.5 Daily cases 5,000,000 4,000,000 3,000,000 First US case 2,000,000 1,000,000 0 0 30 60 90 120 150 180 Day of global outbreak Up to 12% absenteeism at peak Baseline epidemic • ‘Realistic’ seeding using expected number of imported infections estimated from simple global model and travel data. • Peak ~65 days after first case for R0=1.8. • Epidemic growth rate matches peak 1918 growth rate for R0=1.8. • Timing may be pessimistic (no account of seasonal variation in transmission). Daily cases 5,000,000 4,000,000 3,000,000 First US case 2,000,000 1,000,000 R0=1.8 / 1.5 0 0 30 60 90 120 150 180 Day of global outbreak • Indirect effect relies on treatment in <24h since infectiousness peaks soon after symptoms start. • With a 24h delay, treatment of 90% of cases reduces attack rate from 34% to 29% for HT scenario. • 48h delay gives almost no reduction in transmission and poorer clinical benefit. . 2 5000 1.5 4000 3000 1 2000 0.5 1000 0 0 0 2 4 6 8 Days post infection 10 Viral load (data) • 25% stockpile is just enough assuming 50% of those infected seek treatment – but may be higher. Infectiousness • Reduce severity of cases, but can also reduce transmission (reducing attack rates from 34% to 28%. . Mitigation: case treatment School closure • Reactive policy: After the first case in a school, it is closed the next day for certain period. • After reopening, school closes again after further cases. • Children out of school have 50% increase in household contacts, 25% increase in community contacts. • Main effect is to reduce peak height (by ~ 40%). • Cumulative attack rates reduce from 32% to 29% when school closure added to nextday treatment policy and R0=2 scenario. Household prophylaxis/quarantine • Household prophylaxis= treatment of everyone in house of case, not just case herself. • Combined with school closure and next-day treatment can reduce clinical attack rate to 20% – but needs antiviral stockpile of 50% of population (for R0=2.0). • When 20% pre-vaccination (of <16’s) is added, attack rate drops to 14% • For R0=1.7, same policy can reduce attack rates to 7% (from 28% baseline), • Voluntary household quarantine potentially boosts effectiveness – but would need prophylaxis to be ethical. Impact of matched mass vaccination • Imagine vaccine available in US from day 0, 30, 60 or 90 of the global epidemic, and assume 1% of population vaccinated per day. Vaccination has very limited impact unless available within 2 months. So need to stockpile in advance, even if efficacy is limited because vaccine not perfectly matched. 33 36 38 30 32 34 25 30 30 22 28 29 36 37 38 34 35 36 32 31 31 30 29 29 36 37 38 34 35 36 30 30 31 28 5.8 6.4 6.7 5.8 6.4 6.8 5.8 6.1 6.5 6 6 6.5 6.1 6.3 6.4 6 6.2 6.3 5.9 6.2 6.5 5.8 6.2 6.4 6.1 6.2 6.4 6 6.2 6.3 5.7 6.2 6.5 5.7 19 17 16 19 17 16 15 14 14 16 14 14 13 13 13 13 13 13 14 14 14 14 14 14 13 13 13 13 13 13 15 14 14 15 8.7 8.1 7.7 8.7 8.1 7.7 8 7.9 7.7 8 7.8 7.7 7.3 7.2 7.1 7.3 7.2 7 7.7 7.8 7.6 7.7 7.8 7.6 7.3 7.2 7.1 7.3 7.2 7 7.8 7.8 7.7 7.9 55 55 56 55 55 56 58 59 58 58 59 59 60 59 59 61 60 59 60 59 58 60 59 59 60 59 59 61 60 59 59 59 58 59 12 13 14 12 13 14 13 13 13 13 13 13 13 14 15 13 14 15 13 13 13 13 13 13 13 14 15 13 14 15 12 13 13 12 % attack rate in 60-85 age band 21 20 20 22 21 20 17 19 17 15 19 17 22 20 19 21 20 19 21 19 17 21 19 17 22 20 19 21 20 19 21 19 17 20 % attack rate in 20-60 age band 16 14 13 16 14 13 2.6 0.9 0.3 2.2 1.3 0.3 0.4 0.3 0.2 0.4 0.3 0.2 1.2 0.6 0.4 1.3 0.7 0.4 0.4 0.3 0.3 0.5 0.3 0.3 2.5 0.8 0.4 2.9 % attack rate in 15-20 age band 30 30 30 33 33 32 45 50 52 47 51 54 41 43 43 44 45 45 45 49 51 47 51 53 41 43 43 44 45 45 45 50 52 47 % attack rate in 5-15 age band 0.1 0.1 0.1 0.1 0.1 0.1 9.4 0.7 0.2 13 1.3 0.2 0.2 0.1 0.1 0.2 0.1 0.1 0.7 0.3 0.1 1 0.4 0.2 0.2 0.1 0.1 0.2 0.1 0.1 2.2 0.4 0.2 3.5 % of infections in 20-60 age band % of infections in 60-85 age band 0 0 0 0 0 0 20.2 20.2 20.2 20.2 20.2 20.2 20.2 20.2 19.6 20.2 20.2 20.2 12.2 12.9 13.4 12.3 13.0 13.8 12.3 12.8 13.2 12.3 13.1 13.5 12.6 13.1 13.5 12.6 % attack rate in 0-5 age band 0.5 0.6 0.6 0.7 0.7 0.8 0.9 1.1 2.0 0.9 1.0 2.0 2.1 2.6 3.0 2.1 2.9 3.3 0.3 0.8 1.5 0.3 0.7 1.5 1.3 1.9 2.3 1.3 2.2 2.7 0.1 0.5 1.3 0.1 % of infections in 15-20 age band 87 74 70 92 75 71 61 122 128 50 122 130 115 80 70 126 93 70 89 98 84 88 104 99 116 82 67 130 95 75 98 138 104 91 % of infections in 5-15 age band 48 41 36 51 42 36 17 55 58 17 49 51 49 38 37 52 47 35 35 37 33 37 39 42 50 40 35 54 49 39 30 42 39 28 % of infections in 0-5 age band 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 % of infections in community 0 0 0 0 0 0 17 24 46 17 21 46 46 59 66 47 65 74 8 20 36 6 18 37 31 47 55 32 53 64 2 13 33 2 % of infections in workplaces 1.5 2.4 3.4 2.0 3.3 4.6 0.01 0.06 0.48 0.01 0.04 0.45 0.5 1.4 2.1 0.5 1.5 2.5 0.16 0.38 0.92 0.14 0.36 0.92 0.5 1.2 2.1 0.5 1.4 2.5 0.03 0.15 0.62 0.03 % of infections in schools Time to peak [NOT WELL (days) ESTIMATED] 32 39 44 42 52 59 0.3 4.3 18 0.3 2.9 19 17 27 34 19 32 41 4.2 11 21 3.7 10 23 17 27 34 19 32 41 1.3 7 19 1.1 % of infections in households Cum. non-flu ILI attack rate (%) 50 50 50 67 67 67 50 50 50 67 67 67 50 50 50 67 67 67 50 50 50 67 67 67 50 50 50 67 67 67 50 50 50 67 % of infections due to seeding Antiviral usage (%) 1.9 2.4 3 1.9 2.4 3 1.9 2.4 3 1.9 2.4 3 1.9 2.4 3 1.9 2.4 3 1.9 2.4 3 1.9 2.4 3 1.9 2.4 3 1.9 2.4 3 1.9 2.4 3 1.9 Average absenteeism due to place closure (%) Peak attack rate (%) N/A N/A N/A N/A N/A N/A Base Base Base Base Base Base Base Base Base Base Base Base Base Base Base Base Base Base Base Base Base Base Base Base 0.01% 0.01% 0.01% 0.01% Duration of [NOT WELL epidemic (days) ESTIMATED] Average absenteeism due to case withdrawal or quarantine (%) Cumul. attack rate by day 220 of global epidemic (~day 180 of US epidemic) (%) None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None Proportion of infections becoming cases (%) N/A N/A N/A N/A N/A N/A Area Area Area Area Area Area Area Area Area Area Area Area Area Area Area Area Area Area Area Area Area Area Area Area Area Area Area Area Other controls School closure type Treat all ILI as flu N/A N/A N/A N/A N/A N/A Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No No No No No No No No No No No No No No No No R0 N/A N/A N/A N/A N/A N/A Yes Yes Yes Yes Yes Yes No No No No No No Yes Yes Yes Yes Yes Yes No No No No No No Yes Yes Yes Yes Threshold for closure and social distancing 7 7 7 7 7 7 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 Generic/workplace social distancing Scenario Combined strategies! 26 36 42 35 48 56 0.3 3.7 16 0.2 2.5 17 15 24 30 16 29 37 3.5 9.6 19 3 9.1 21 15 24 30 16 29 37 1.1 6.1 17 0.9 42 47 49 57 63 65 0.3 4.3 18 0.3 2.9 19 15 25 31 17 29 38 4.1 11 21 3.6 10 23 16 25 31 17 30 38 1.3 7 19 1.1 38 44 47 51 59 63 0.3 4.7 19 0.3 3.1 20 17 27 33 19 32 41 4.5 12 22 3.9 11 24 17 27 33 19 32 41 1.4 7.6 20 1.2 31 39 44 42 52 59 0.3 4.5 19 0.3 3.1 20 19 29 35 20 35 44 4.5 12 22 3.9 11 24 19 29 35 20 35 44 1.4 7.4 20 1.1 24 32 39 31 42 51 0.2 3.4 14 0.2 2.3 15 14 24 31 16 28 38 3.3 8.7 17 2.9 8.2 19 14 24 31 16 28 38 1 5.5 15 0.8 Working Conclusions • Vaccine needed at start of pandemic to have significant impact. • Treatment-only’ policy needs to be delivered very rapidly for optimal effect. • School closure potentially effective at reducing peak of epidemic. • Household prophylaxis can reduce attack rates by 1/3. • Adding pre-pandemic vaccine with 30% efficacy to these policies substantially increases impact – possible to reduce attack rate by 67-75%. • Social distancing (namely reductions in non-household contact rates) can (of course) be highly effective at controlling disease transmission, if high degree of contact rate reduction assumed. TALK OUTLINE 1. 2. 3. 4. The “MIDAS” group Prevention of Avian Influenza Emergence in SE Asia Mitigation of a Pandemic Impact in the USA Discussion Modeling and Simulation to Guide Policy Decisions in the USA “MIDAS” Models of Infectious Disease Agents Studies A collaborative network of scientists who conduct research on the use of computational and mathematical models to prepare the United States and the World to respond to outbreaks of infectious diseases. The University of Pittsburgh Models of Infectious Diseases Agents Study (MIDAS) Team Pittsburgh Don Burke Derek Cummings Imperial Neil Ferguson Probability of eliminating an otherwise large epidemic ( using an idealized policy of socially targeted antiviral prophylaxis ): Impact of epidemic size when policy is implemented 1 Effect on epidemic timing • Most policies do not slow epidemic substantially, but just reduce magnitude – so need to be maintained throughout pandemic. • Very intensive prophylaxis &/or social distancing measures can (in theory) dramatically reduce attack rates – but feasibility a key issue. • e.g. for R0=2.0 (in US): None Daily cases 5,000,000 Policy 4 4,000,000 Policy 13 3,000,000 Policy 19 Policy 21 2,000,000 Policy 27 1,000,000 0 0 30 60 90 120 150 180 Day of US outbreak Simulated daily incidence (complete absence of controls) Blue line = mean; gray zone = 95% envelope; colored lines = seven stochastic realizations