Collaborative Research: Modeling the opiod and HIV epidemics

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In the last 15 years, US deaths due to the opioid epidemic have quadrupled. The increase in opioid use, and in particular, in injection drug use, has both directly and indirectly contributed to the spread of infectious diseases such as HIV. Drug use also lowers inhibitions, which in turn facilitates more HIV transmission. This has resulted in a leveling off of the decrease of HIV cases in the US. The goal of this project is to develop novel, data-driven, multi-scale models of the intertwined opioid and HIV epidemics. Such models will lead to a better understanding of the interplay of the epidemics and can be used to guide public health policy, for instance to reverse the recent trends in HIV cases. The models will use data on HIV and opioid use in the US as well as viremia data for individuals who both have HIV and use drugs. Considering the dynamic interplay of the two epidemics will provide insights that are missing when one focuses on either alone, including how processes within individuals bear on dynamics at the population scale. Our preliminary results have already identified promising methods of control that targeting the drug abuse epidemic could reduce HIV risk among drug users. The PIs will test the robustness of these control strategies in multiple modeling scenarios. This research will help identify improved strategies to control these two intertwined epidemics.To understand the two epidemics, a multi-scale immuno-epidemiological model of the opioid and HIV epidemics will be developed, analyzed and used for simulations. This model explicitly includes the dynamics of HIV within hosts and the effect of such dynamics on transmission of HIV between hosts, as well as the effect of opioid use on each level of infectious disease dynamics. The model will be linked to multi-scale data, and identifiability analyses will be performed. A novel network version of the multi-scale model will be developed that allows modeling of individuals with varying number of contacts. The impact of the combined effects of contact network structure and within-host dynamics on the two linked epidemics will be investigated both analytically and numerically, using novel analytical techniques that will be developed. Finally, optimal control approaches will be applied to the multi-scale immuno-epidemiological network model in order to characterize effect of control strategies. To study the robustness of the two control methods identified in the preliminary results (targeting drug abuse and reducing HIV risk among drug users), a comparison of these methods with results of the optimal control models will be made. Rigorous incorporation of multi-scale data and simulations will uncover any dependence of the best control strategy on which modeling framework is employed. All these results will be synthesized in a book on immuno-epidemiological modeling that incorporates elements of this research, in the context of a broad perspective on this rapidly developing field. The book will be useful to graduate students, academics, and practitioners.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.