DYNAMIC NETWORK DISEASE MODELING OF THE SYNERGISTIC TRANSMISSION OF HUMAN IMMUNODEFICIENCY VIRUS AND HERPES SIMPLEX VIRUS 2

Purpose:

The purpose of the study is to test the hypothesis that contact dynamics and network structures are important to closely predict the spread of sexually transmitted diseases (STDs).

Method:

The Network, Norms and HIV/STI Risk Among Youth (NNAHRAY) project included both a relationship survey and laboratory testing for STDs among 465 interviewees, residing in Bushwick, New York between 2002 and 2004. Testing included human immunodeficiency virus (HIV) and herpes simplex virus-2 (HSV2). We created a dynamic network model to replicate the risky behavior contact network structures reported in NNAHRAY. Combining stochastic disease models of HIV and HSV2 with our dynamic network model, we simulated the spread of both diseases from 1990 to 2002 in a synthesized population based on the census information collected in NNAHRAY data. We proposed a network model fitting framework to adjust for the biased network sampling issues inherent in the NNAHRAY data, which included chain referral methods. Using the same set of parameters estimated from the NNAHRAY data and the published literature, we compared our dynamic model with a range of static network models in fitting the contact network structures in NNAHRAY and the 12 year HIV prevalence in New York City (NYC) in the literature.

Result:

After fitting our dynamic network model to the proposed contact network structure metrics measured, allowing 10% deviation for 11 metrics and 50% deviation for 2 metrics of high dimensions, we also fit our disease spread model to produce HIV (9%) and HSV2 (45%) prevalence, which were close to those reported in NNAHRAY (9% and 48%). Without the proposed network model fitting framework, neither the static network models nor our dynamic network model could reproduce all the proposed network structure metrics in the allowed range at the same time. Only our dynamic network model succeeded in doing so after the network sampling process was incorporated in the model fitting. Our combined stochastic disease model also provided the most accurate predictions of HIV prevalence relative to predictions from disease models based on static networks.

Conclusion:

Our work supported the hypothesis that considering the underlying contact dynamics as well as network structures was important for making optimal disease prevalence predictions, demonstrating the need to model the data sampling process when validating against real-world data.