Network modeling of infectious disease : mathematical framework and policy evaluation

A majority of infectious disease models are compartmental models, due to their simplicity and interpretability. However the assumption of homogeneous mixing in compartmental models does not hold in general, and for certain diseases or subpopulations the heterogeneity in mixing behavior is particularly prominent. With more and more data becoming available to shed light on the heterogeneous mixing of population, network model has emerged as an alternative solution. This dissertation focuses on developing and applying network models to study infectious diseases with readily available data. In Chapter 2, we propose a mathematical framework to study the spread of blood-borne diseases among people who inject drugs (PWID). The prevalence rate of blood-borne diseases is higher among PWID as compared to the general population, due to unprotected sex and needle-sharing. To accurately account for transmissions via both types of risky contact, we build a bi-layer network model. We present methodology for inferring model parameters from readily available data sources. The model can be used to evaluate interventions that exploit the structure of the contact network to contain infectious diseases in PWID. To illustrate, we instantiate a network model with data collected by a needle and syringe program in Chicago, then we use the model to evaluate the potential effects of a peer education (PE) program on averting new HIV and HCV infections over the next ten years. We show that a targeted PE program would avert significantly more HIV and HCV infections than an untargeted program, highlighting the importance of reaching individuals who are centrally located in contact networks. In Chapter 3, we adapt the network model of Chapter 2 to evaluate the cost-effectiveness of a pre-exposure prophylaxis (PrEP) program for PWID. Inspired by the illustrative example of targeted PE program in Chapter 2, we sought to improve the cost-effectiveness of PrEP program by enrolling high risk PWID. Specifically we investigate four strategies: 1) random PWID are enrolled (Unselected Enrollment); 2) individuals are randomly selected and enrolled together with their partners (Enroll Partners); 3) individuals with the highest number of sexual and needle-sharing partnerships are enrolled (Most Partners); 4) individuals with the greatest number of infected partners are enrolled (Most Positive Partners). We measure costs and QALYs associated with each strategy, and find that all strategies except the Unselected Enrollment strategy are cost-effective, indicating that selection of high risk PWID for PrEP can indeed improve the cost-effectiveness of PrEP for PWID. In Chapter 4, we explore a household model to study the circulation of poliovirus in a community.The Polio Eradication and Endgame Strategic Plan 2013-2018 calls for the gradual withdrawal of OPV from routine immunization. Little is understood regarding the transmission potential of Sabin virus when it is reintroduced in the post-cessation era. In view of this, we develop an individual-based stochastic epidemic model and use the model to investigate two scenarios: 1) an outbreak response using OPV takes place to interrupt wild poliovirus transmission; 2) a case actively shedding Sabin virus is released into the community and sparks an undetected circulation. We find that inactivated poliovirus vaccine (IPV) alone is insufficient to prevent long term circulation of Sabin virus under current level of sanitation and contact patterns in Mexican communities if OPV is reintroduced. Our result highlights the need for aggressive OPV use in outbreak response, vigilant poliovirus surveillance, improvement in hygiene and sanitation, as well as development of new vaccines that protect against fecal shedding and reinfection. We conclude with a summary of our findings and a discussion of future research directions in Chapter 5.

Keywords: Management Science and Engineering.
Advisor: Andrews Jason Randolph 
Brandeau Margaret L. 
Owens Douglas K.