Scholarship & Research
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Item Leveraging social networks for identification of people living with HIV who are virally unsuppressed(Wolters Kluwer Health, Inc., 2023-10) Cummins, Breschine; Johnson, Kara; Schneider, John A.; Del Vicchio, Natasha; Moshiri, Niema; Wertheim, Joel O.; Goyal, Ravi; Skaathun, BrittObjectives: This study investigates primary peer-referral engagement (PRE) strategies to assess which strategy results in engaging higher numbers of people living with HIV (PLWH) who are virally unsuppressed. Design: We develop a modeling study that simulates an HIV epidemic (transmission, disease progression, and viral evolution) over 6 years using an agent-based model followed by simulating PRE strategies. We investigate two PRE strategies where referrals are based on social network strategies (SNS) or sexual partner contact tracing (SPCT). Methods: We parameterize, calibrate, and validate our study using data from Chicago on Black sexual minority men to assess these strategies for a population with high incidence and prevalence of HIV. For each strategy we calculate the number of PLWH recruited who are undiagnosed or out-of-care and the number of direct or indirect transmissions. Results: SNS and SPCT identified 256.5 (95% C.I.: [234,279]) and 15 (95% C.I.: [7,27]) PLWH, respectively. Of these, SNS identified 159 (95% C.I.: [142,177]) PLWH out-of-care and 32 (95% C.I.: [21, 43]]) PLWH undiagnosed compared to 9 (95% C.I.: [3,18]) and 2 (95% C.I.: [0,5]) for SPCT. SNS identified 15.5 (95% C.I.: [6,25]) and 7.5 (95% C.I.: [2, 11]]) indirect and direct transmission pairs, while SPCT identified 6 (95% C.I.: [0,8]) and 5 (95% C.I.: [0,8]), respectively. Conclusions: With no testing constraints, SNS is the more effective strategy to identify undiagnosed and out-of-care PLWH. Neither strategy is successful at identifying sufficient indirect or direct transmission pairs to investigate transmission networks.Item Estimating contact network properties by integrating multiple data sources associated with infectious diseases(Wiley, 2023-07) Goyal, Ravi; Carnegie, Nicole; Slipher, Sally; Turk, Philip; Little, Susan J.; De Gruttola, VictorTo effectively mitigate the spread of communicable diseases, it is necessary to understand the interactions that enable disease transmission among individuals in a population; we refer to the set of these interactions as a contact network. The structure of the contact network can have profound effects on both the spread of infectious diseases and the effectiveness of control programs. Therefore, understanding the contact network permits more efficient use of resources. Measuring the structure of the network, however, is a challenging problem. We present a Bayesian approach to integrate multiple data sources associated with the transmission of infectious diseases to more precisely and accurately estimate important properties of the contact network. An important aspect of the approach is the use of the congruence class models for networks. We conduct simulation studies modeling pathogens resembling SARS-CoV-2 and HIV to assess the method; subsequently, we apply our approach to HIV data from the University of California San Diego Primary Infection Resource Consortium. Based on simulation studies, we demonstrate that the integration of epidemiological and viral genetic data with risk behavior survey data can lead to large decreases in mean squared error (MSE) in contact network estimates compared to estimates based strictly on risk behavior information. This decrease in MSE is present even in settings where the risk behavior surveys contain measurement error. Through these simulations, we also highlight certain settings where the approach does not improve MSE.