1. Coevolution of Epidemics, Social Networks, and Individual Behavior: A Case Study Joint work with Achla Marathe, and Madhav Marathe Jiangzhuo Chen Network Dynamics & Simulation Science Laboratory 2010 International Conference on Social Computing, Behavioral Modeling, and Prediction (SBP10) March 31 st, 2010 NDSSL Technical Report 10-042

2. 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)

3. Network Dynamics & Simulation Science Laboratory Talk Outline Motivation for the case study. Major contributions. Background: –propagation of infectious disease on social contact networks; – intervention measures. Simulation results.

4. Network Dynamics & Simulation Science Laboratory Co-evolution of Behavior, Epidemics and Social Networks Policy questions: Is there an optimum AV allocation strategy between the market-based availability and public distribution via clinics that minimizes the attack rate and recovers the cost of AV through markets? Does “contextual behavioral adaptation” by individuals help in controlling the epidemic? Price/ Inventory Demand/BehaviorSusceptibility Transmission Network Disease Dynamics/ Prevalence

5. Network Dynamics & Simulation Science Laboratory Our Contributions We have developed a methodology and an associated modeling environment to study the co-evolution of individual behavior, disease dynamics and social networks.

6. Network Dynamics & Simulation Science Laboratory Our Contributions (cont.) We find that there is an optimal AV allocation strategy between the market and clinics that minimizes the attack rate and recovers the cost of AV through markets. We find that markets are inherently unfair in their allocation scheme but price regulations and other incentives can control this. The market based system combined with behavioral adaptation reduces the epidemic peak as well as delays it.

7. Network Dynamics & Simulation Science Laboratory Disease Spread in a Social Network Within-host disease model: SEIR –State transitions are probabilistic and timed. Between-host disease model: transmission occurs along edges of a social contact network –People + Locations => Contacts. –Transmissions are probabilistic.

8. Network Dynamics & Simulation Science Laboratory Interventions Pharmaceutical interventions: vaccination or antiviral changes an individual’s role in the transmission chain –Lower susceptibility or infectiousness Non-pharmaceutical interventions: social distancing measures change people activities and hence the social network –Generic social distancing, school closure, isolation, etc.

9. Network Dynamics & Simulation Science Laboratory Interventions: Multiple Models Infection reporting/diagnosing model: infected people show symptom with a prob.; symptomatic people are diagnosed with a prob. Antiviral distribution models: –Public sector distribution: targeted to diagnosed people, free. –Market distribution: mostly prophylaxis, not free. Behavioral models for self-interventions: –Household isolation: when one member is diagnosed. –Household AV demand: function of local variables (budget) as well as global variables (price, disease prevalence).

10. Network Dynamics & Simulation Science Laboratory Case Study: Simulation Setup New River Valley population: 150K Total 15K AV supply –allocated between clinics and market –from all-to-clinic to all-to-market. 20 index cases are randomly picked. 45% attack rate without any intervention. The price of the AV kit can vary between $50-$150. Total household budget for the AV is 1% of the income.

11. Network Dynamics & Simulation Science Laboratory Experiment Results Attack rate reaches minimum at 40% clinic allocation. Application of clinic allocation is upper bounded by attack rate. No need to allocate more to clinics. Extra AV can go to markets. Revenue covers cost.

12. Network Dynamics & Simulation Science Laboratory Effect of Prevalence, Social Distancing and Individual Behavior Dependence of demand on disease prevalence helps to delay the peak of the epidemic by about 30 days. Isolation is able to reduce the peak infection by 30%. A combination of market, public distribution and contextual behavioral adaptation is indeed likely to be more “effective” in reaching the masses and controlling the epidemic.

13. Network Dynamics & Simulation Science Laboratory Fairness: Who Buys and Who Doesn’t? Only top 30 percentile income households are able to buy AV through the market when demand is sensitive to prevalence. Poorer households do not buy because early in the epidemic the prevalence is low; later in the epidemic when the prevalence increases, the price increases making it unaffordable for the poorer people.

14. Network Dynamics & Simulation Science Laboratory Fairness: Impact of Demand Sensitivity Suppose demand is independent of prevalence: each household has positive demand with a fixed probability every day, only subject to budget constraint. AV price rises gradually. More people are able to purchase it.

15. Network Dynamics & Simulation Science Laboratory Conclusion We have developed a system that can study the co- evolution of individual behavior, disease dynamics and social networks. It can study economic and social effect of an epidemic. There exists an optimal strategy for allocating antiviral between the market and clinics to minimize the attack rate and to recover cost of antiviral through the market. The market based system combined with behavioral adaptation lowers and delays the epidemic peak.