Comparing Effectiveness of Top-Down and Bottom-Up Strategies in Containing Influenza

1. 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 PLoS ONE, Volume 6, Issue 9, e25149 September 2011. Network Dynamics & Simulation Science Laboratory

2. Outline • Motivation for the study • Experiment settings • Experiment results

3. 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

4. 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

5. 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!)

6. 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

7. Attack Rate: Moderate Flu with Various Interventions

8. Intervention Coverage: Moderate Flu with Various Interventions

9. Attack Rate: Catastrophic Flu with Various Interventions

10. Intervention Coverage: Catastrophic Flu with Various Interventions

11. 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

12. 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

13. 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

14. Closer look at an interesting setting… (catastrophic flu, high observability, low threshold, vaccines available)

17. Day-by-day Epidemic Evolution vs. Intervention Epidemic Intervention coevolution Catastrophic flu, 100% diagnosis, 1% threshold, 50% compliance; error bars at peak of each curve show standard deviation over 25 replicates

18. Cumulative Epidemic Evolution vs. Intervention Catastrophic flu, 100% diagnosis, 1% threshold, 50% compliance

19. 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