Comparing Effectiveness of Top-Down and Bottom-Up Strategies
in Containing Influenza
Achla Marathe, Bryan Lewis, Christopher Barrett, Jiangzhuo Chen, Madhav Marathe, Stephen Eubank,
and Yifei Ma
Network Dynamics & Simulation Science Laboratory
PLoS ONE, Volume 6, Issue 9, e25149September 2011.
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Outline
• Motivation for the study• Experiment settings• Experiment results
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Comparison: Obvious Pros and Cons
• Individual behavioral interventions – bottom-up– D1 (distance-1) intervention: each person takes intervention action when he
observes outbreak among his direct contacts Self motivated, prompt action Better accuracy in observation (based on symptoms)? Lack of global knowledge; un-planned and un-targeted
• Public health interventions – top-down– Block intervention: take action on all people residing in a census block if an
outbreak is observed in the block– School intervention: take action on all students in a school if an outbreak is
observed in the school Planned/optimized based on global epidemic dynamics Targeted (circumvent “hot-spots”)? More noise in observation (based on diagnosis); delay in case
identifying/reporting? Mass action, delay in implementation, low compliance? Administration cost
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Comparison: Effectiveness and Cost
• Effectiveness of intervention:– Reduce attack rate (morbidity and mortality,
productivity loss)– Delay outbreak/peak
• Cost– Number of people involved in intervention
• Pharmaceutical: consumption of antiviral or vaccines, which often have limited supply
• Non-Pharmaceutical (social distancing): loss of productivity
– Other cost: e.g. administration of a mass vaccination campaign
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Experiment: A Factorial Design• Simulate epidemics in a US urban region with 3 different intervention
strategies: D1, Block, School• 2 flu models: moderate flu with ~20% attack rate without intervention;
catastrophic flu with ~40% attack rate without intervention• Probability of a sick case being observed (diagnosed and reported for top-
down interventions): 2 observability values 1.0 and 0.3• 2 threshold values for taking actions: 0.01 and 0.05
– Fraction of direct contacts found to be sick: D1 intervention– Fraction of block (school) subpopulation found to be sick: block (school) intervention
• 2 compliance rates: 1.0 and 0.5• 2 pharmaceutical actions
– Antiviral administration (AV): usually available– Vaccination (VAX): delayed availability for new flu strains
• Delay in implementing interventions (from deciding to take action): 2 values for Block and School, 1 day and 5 days; no delay for D1
• 2 x 2 x 2 x 2 x 2 x ( 2 + 2 + 1) = 160 cells• 25 replicates per cell (4000 simulation runs!)
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Experiment: Other Settings
• SEIR disease model: heterogeneous PTTS (probabilistic timed transition system) for each individual
• Between-host propagation through social contact network on a synthetic population
– Miami network: 2 million people, 100 million people-people contacts
• Assume unlimited supply of AV or VAX– One course of AV is effective immediately for 10 days: reduce
incoming transmissibility by 80% and outgoing by 87%– VAX is effective after 2 weeks but remains effective for the season
• Simulation tools: EpiFast and Indemics developed in our group
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Attack Rate: Moderate Flu with Various Interventions
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Intervention Coverage: Moderate Flu with Various Interventions
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Attack Rate: Catastrophic Flu with Various Interventions
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Intervention Coverage: Catastrophic Flu with Various Interventions
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Experiment Results: Antiviral
• AV is very effective under D1– Moderate flu: attack rate drops from 20% to <1%;
catastrophic flu: from 40% to <1%• AV has almost no effect under two top-down
strategies• Performance of bottom-up AV strategy is robust
to delay in implementation, drop in compliance rate and increase in threshold value
• High depletion of AV under top-down strategies– Top-down interventions avert <1 case per drug course– Bottom-up intervention averts up to 10 cases per drug
course
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Experiment Results: Vaccination
• VAX performs best under Block strategy if sufficient number of vaccines were available– 2-week delay for becoming effective -> cases in
one's immediate neighborhood become less relevant
– decrease in attack rate: Block > D1 > School– (moderate flu) cases averted per drug course: D1
> School > Block• Performance of top-down strategies is not
sensitive to 1 day or 5 day delay
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Policy Implications
Depending on public health policy goals and availability of antivirals and vaccines:•If disease is highly infectious and vaccines are available in abundant supply: Block strategy seems the best choice•If only antivirals are available and only in limited amount: maybe distribute them to private citizens on-demand or over-the-counter•If antivirals and vaccines are both available only in limited quantities, identification of infectious cases is administratively expensive, and compliance with a public policy is an issue: best to motivate individuals to self-intervene by applying D1
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Closer look at an interesting setting…(catastrophic flu, high observability, low
threshold, vaccines available)
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Day-by-day Epidemic Evolution vs. Intervention
Epidemic Intervention
coevolutionCatastrophic flu, 100% diagnosis, 1% threshold, 50% compliance; error bars at peak of each curve show standard deviation over 25 replicates
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Cumulative Epidemic Evolution vs. Intervention
Catastrophic flu, 100% diagnosis, 1% threshold, 50% compliance
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Summary
• An interesting comparison study– Individual behavioral vs. public health level
interventions– Simulations policy implications
• Unique capability to run such complex, realistic studies– Behavioral adaptation (endogenous and exogenous) +
network model (individual level details)– Fast simulation tools