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 Action December 10, 2010 Network Dynamics & Simulation Science Laboratory 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 Network Dynamics & Simulation Science Laboratory Talk Outline Motivation for the study Experiment settings Experiment results Network Dynamics & Simulation Science Laboratory 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 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 public health interventions): 2 observability values 1.0 and 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 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 ( ) = 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 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