The Key to Eliminating Hepatitis C: Reducing New Infections in Special Populations through Mathematical/Computational Modeling
"Dahari Lab is committed to the WHO goal of global HCV elimination"
Now that a more effective treatment via direct-acting antiviral agents (DAAs) is widely available for HCV, we have entered an era of refining the research in an effort to achieve cost-reduction and ease of treatment. The goal to eliminate HCV as defined by the World Health Organization (WHO) is not only possible, but well within reach by their target of 2030. However, it will only be achieved if treatments can be scaled and paired with strategies that address the characteristics of special groups and the need for re-treatment.
Targeting larger populations with chronic infection is one of two tactics needed to achieve the elimination goal. The first tactic is reducing the prevalence (or number of cases) in the general population. It is the most basic strategy. The second is critical to finally eliminating HCV. This involves the ability to map, model and target special populations that represent a disproportionate number of new infections, reinfections or the transfer of infection. Dahari Lab has been at the forefront of modeling one of these special populations: people who inject drugs (PWID). Without an effective treatment strategy (cost, ease to administer, duration, identifying targets) this population, which represents a small percentage of the overall infections, will continue to reintroduce the virus and make elimination difficult to impossible.
In our research we used mathematical modeling to predict that DAA treatment scale-up could dramatically reduce the prevalence of chronic HCV infection among PWID in Chicago. This population was identified and targeted due to its on-going high HCV transmission and reinfection rates (PLoS One). The WHO defines elimination as a reduction of HCV incidence by 90% by 2030. We used this parameter in our research to gauge the feasibility and select the factors to consider. In 2019, we published modeling outcomes for cost and reduction of HCV incidence in the population of PWID. The results showed that a 90% reduction is indeed possible through DAA-based treatments. The model produced data that was valuable for public health policy makers to understand the costs associated with various treatment rates, timelines, and their effectiveness (Vaccine).
While DAA-based treatment is a very effective therapy in general for PWID, many factors within this community can cause serious setbacks. These factors include cost, restricted access to DAAs, and the risk of reinfection. Intervention strategies must address this dynamic and complex interplay for PWID (e.g. behavior, structure, access to clean needles, etc.) (Lancet Infct Dis). To better understand and map this population with greater depth and detail, we developed an agent-based model (ABM) for PWID in the Chicago metro area. ABM is a more sophisticated modeling that allowed us to create and map unique profiles to model behavior and outcomes. This data gave us deeper insights to make more detailed and relevant predictions at the individual level (such as geography, PWID network, ethnicity, and age) (PLoS One).
We formalized this work as the HepCEP model (Hepatitis C Elimination in PWID) and the research was expanded with funding by the NIH. It allowed us to work in close partnership with Marian Major (FDA), Basmattee Boodram (UIC), and Jonathan Ozik (UofC & Argonne National Lab). The HepCEP model showed that, in the Chicago community, exploiting PWID network structures by targeting individuals who may have transmitted the infection helped reduce incidence and cost (through targeted DAA treatment). The benefit to running models is the range of scenarios and options that can be explored quickly to inform policy (2019 Winter Simulation Conference).
Another significant finding is the impact of retreatment. The HepCEP model predicted that ignoring retreatment as a factor in the elimination strategy, cost, or duration of treatment will jeopardize achieving the WHO goal. These simulations showed that based on the parameters of the model, just under half the population would need retreatment with a small percentage of individuals needing as many as 7 retreatments. While it may lead the general public to question if retreatments are a waste of time and public money, the data confirms it is all interconnected. The model highlights the importance of a strategy that includes re-treatment of re-infected individuals in order to achieve significant reductions in incidence (ARXIV). Mathematical modeling has proved to be a critical tool for helping define the problem and all of its complexities. Paired with the innovations in DAA treatment and informed public policy, the commitment by the WHO to eliminate HCV is no longer a dream.