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.

Network Dynamics & Simulation Science Laboratory

Outline

• Motivation for the study• Experiment settings• Experiment results

Network Dynamics & Simulation Science Laboratory

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

Network Dynamics & Simulation Science Laboratory

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

Network Dynamics & Simulation Science Laboratory

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!)

Network Dynamics & Simulation Science Laboratory

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

Network Dynamics & Simulation Science Laboratory

Attack Rate: Moderate Flu with Various Interventions

Network Dynamics & Simulation Science Laboratory

Intervention Coverage: Moderate Flu with Various Interventions

Network Dynamics & Simulation Science Laboratory

Attack Rate: Catastrophic Flu with Various Interventions

Network Dynamics & Simulation Science Laboratory

Intervention Coverage: Catastrophic Flu with Various Interventions

Network Dynamics & Simulation Science Laboratory

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

Network Dynamics & Simulation Science Laboratory

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

Network Dynamics & Simulation Science Laboratory

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

Network Dynamics & Simulation Science Laboratory

Closer look at an interesting setting…(catastrophic flu, high observability, low

threshold, vaccines available)

Network Dynamics & Simulation Science Laboratory

Network Dynamics & Simulation Science Laboratory

Network Dynamics & Simulation Science Laboratory

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

Network Dynamics & Simulation Science Laboratory

Cumulative Epidemic Evolution vs. Intervention

Catastrophic flu, 100% diagnosis, 1% threshold, 50% compliance

Network Dynamics & Simulation Science Laboratory

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