jiangzhuo chen

Comparison of Individual Behavioral Interventions and Public Mitigation Strategies for Containing

Influenza Epidemic

Joint work with Chris Barrett, Stephen Eubank, Bryan Lewis, Yifei Ma, Achla Marathe, and Madhav Marathe

Jiangzhuo Chen

Network Dynamics & Simulation Science Laboratory

2010 Conference on Modeling for Public Health ActionDecember 10, 2010


Work funded in part by NIGMS, NIH MIDAS program, CDC, Center of Excellence in Medical Informatics, DTRA CNIMS, NSF, NeTs, NECO and OCI (Peta-apps) program, VT Foundation.

Our group members (NDSSL)

Acknowledgment


Talk Outline

• Motivation for the study• Experiment settings• Experiment results


Comparison: Obvious Pros and Cons

• Individual behavioral interventions:– D1 (distance-1) intervention: each person take 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:– Block intervention: take action on all people residing in a census block group

if an outbreak is observed in the block group– 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


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


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 public

health 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 group (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!)


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


Attack Rate: Moderate Flu with Various Interventions


Intervention Coverage: Moderate Flu with Various Interventions


Attack Rate: Catastrophic Flu with Various Interventions


Intervention Coverage: Catastrophic Flu with Various Interventions


Experiment Results

• Action effectiveness:– AV is very effective under D1; almost no effect under two

public strategies• No efficacy delay; protect people from sick contacts immediately• Efficacy expires after 10 days; hard to avoid transmissions from

farther-away nodes in the neighborhood• If only AV is available, should motivate people to take AV by

themselves– VAX performs best under Block, worst under School

• Two weeks efficacy delay; sick contacts become less relevant• Form larger “ring” around “hot-spots”• Large consumption under Block; little consumed under school

(school students <25% of whole population)• If sufficient vaccines are available, should apply Block

intervention strategy


Experiment Results

• Compliance: limited impact on intervention effectiveness; almost linearly determine drug consumption

– Higher compliance lower attack rate + more consumption– Double consumption ! twice reduction in attack rate– Larger impact under Block or School vaccination

• Implementation delay: little difference between 1 day or 5 days

• Nothing is useful with low observability + high action threshold

– Campaign to raise concern on epidemic– Increase diagnosis accuracy and enhance public health

surveillance


Supply of AV or VAX

• D1 intervention is effective with AV; Block intervention is effectve with VAX; both require large amount of the drug

• School intervention consumes little: may be most cost-effective– Suppose available VAX can only cover 10% of

populations, and fortunately we have efficient identification of sick cases. Which intervention strategy is the best? School intervention


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

threshold, vaccines available)


Day-by-day Epidemic Evolution vs. Intervention

Epidemic Intervention

coevolution


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


Thanks!

jiangzhuo chen

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