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, e25149 September 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 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 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: 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

Day-by-day Epidemic Evolution vs. Intervention EpidemicIntervention coevolution Catastrophic 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