Our Research

Funder: Centers for Disease Control and Prevention/Center for Forecasting and Outbreak Analytics

Narrative: The purpose of the DMA-PRIME initiative is to save lives by increasing the ability of public health organizations and communities to prepare for, and respond to, infectious disease outbreaks through a multi-pronged approach: 1) procurement of informative data sources and their integration into proven infectious disease forecasting and outbreak analytic tools, 2) integration of these analytic tools into decision-support toolkits to inform public health response, and 3) enhancement of methods for visualizing data and communicating analytic results to decision makers and communities. This project is in close collaboration with our four implementing partners, comprising South Carolina’s (SC’s) two largest health care systems, Prisma Health and Medical University of South Carolina; Clemson Rural Health; and SC’s Center for Rural and Primary Healthcare, and in collaboration with SC’s Department of Health and Environmental Control (SCDHEC).

The decision-making toolkits will be pilot tested in real-world settings for informing a) field-level interventions for testing, treatment, and vaccination, b) healthcare system and statewide disaster planning and response, and c) community awareness on individual risk and availability of healthcare services. Ultimately, the DMA-PRIME initiative aims to integrate innovative analytic approaches to inform and improve preparedness, response, intervention, mitigation, and elimination of infectious disease outbreaks. Utilizing the strong relationships and trust cultivated between our partners throughout SC, our long-term objective is to broaden public health response to current and future infectious disease threats.

Impact: The DMA-PRIME initiative will drastically improve real-time infectious disease outbreak response by supporting always-on data collection, outbreak detection, and forecasting mechanisms for swift integration into public health response. This will be made possible by a statewide collaboration of health systems, health departments, and academic institutions with a strong working history and an enhanced ability to rapidly collect and provide data in real-time. Because healthcare systems are a major frontline defense for outbreaks, successful integration of our decision-support toolkits has potential to save thousands of lives through improving public health response, including timely delivery of essential resources to populations of greatest risk and need. Simultaneously, the public version of our toolkit will improve health outcomes through increasing understanding of individual risk and informing availability of community health care resources.

Reference: Centers for Disease Control and Prevention NU38FT000011
PI: Lior Rennert 
Title: Disease Modeling and Analytics to inform Outbreak Preparedness, Response, Intervention, Mitigation, and Elimination in South Carolina (DMA-PRIME)
Funding: $17,370,990
Dates: 09-30-2023 to 09-29-2028.

Contact: For information, please contact our DMA-PRIME project Manager, Dr. Tolu Fashina, at tfashin@clemson.edu.

Funder: National Institutes of Health/National Institute on Drug Abuse

Narrative: This project aims to develop, deliver, and evaluate an innovative 1) Peer Support Specialist (PSS) intervention to increase Medications for Opioid Use Disorder (MOUD) initiation and retention rates in rural populations and underserved communities and 2) dynamic modeling framework to prioritize at-risk communities for delivery of Mobile Health Clinics. In collaboration with key stakeholders, the interventions will be developed in the R61 phase and implemented in the R33 phase to systematically deliver Mobile Health Clinics with PSS services to the highest priority communities in South Carolina (identified via modeling). With opioid overdose deaths continuing to rise in South Carolina (SC) and nationally, our sustainable framework has potential to prevent hundreds to thousands of opioid overdoses in SC and can be scaled up in other regions to save many more lives.

Impact: Development of a Peer Support Specialist intervention delivered by Mobile Health Clinics via our proposed framework has potential to prevent hundreds to thousands of opioid overdoses in SC alone and has potential to be scaled up and prevent many more deaths if adopted by public health decision makers in other regions or for other substances.

Reference: National Institute on Drug Abuse of the National Institutes of Health R61DA059892
PI: Lior Rennert
Title: Data-Driven Approaches for Opioid Use Disorder Treatment, Recovery, and Overdose Prevention in Rural Communities via Mobile Health Clinics and Peer Support Services
Funding: $5,546,082
Dates: 09-30-2023 to 09-29-2029.

Contact: For information, please contact our Research Manager, Dr. Kerry Howard, at khowar7@clemson.edu.

Funder: National Institutes of Health/National Library of Medicine

Narrative: This project develops a dynamic simulation modeling framework for surveillance, prediction, and real-time allocation of essential resources to underserved communities in order to reduce health disparities during Covid-19 and future pandemics. Our flexible modeling framework will integrate real-time data on infectious disease outcomes with individual and community level contextual factors to inform infectious disease surveillance and improve understanding of disease epidemiology in underserved communities, and to support the decision-making process for resource allocation to high-risk populations. In collaboration with public health decision-makers, we will utilize our toolkit to inform real-time scheduling of South Carolina’s fleet of Covid-19 mobile vaccination clinics to underserved communities across the state; this has potential to save countless lives during the Covid-19 pandemic and will lay the foundation for effective resource allocation to underserved communities in other health emergencies.

Impact: Our project will improve pandemic planning by developing the modeling infrastructure for infectious disease surveillance and understanding of disease epidemiology in underserved communities, ultimately improving timely delivery of essential resources to those of greatest need. Utilization of this toolkit by public health decision makers can prevent thousands of future Covid-19 deaths. Through adaptation of input data sources, our modeling framework is easily translatable to other infectious diseases and geographic regions and has potential to save many more lives in future health emergencies.

Reference: National Library of Medicine of the National Institutes of Health R01LM014193
PI: Lior Rennert
Title: Developing a dynamic modeling framework for surveillance, prediction, and real-time resource allocation to reduce health disparities during Covid-19 and future pandemics
Funding: $3,092,665
Dates: 01-05-2023 to 11-30-2028.

Contact: For information, please contact our Research Manager, Dr. Kerry Howard, at khowar7@clemson.edu.

Data-driven approach to identify high-risk rural communities for delivery of mobile health clinics
With funding from South Carolina’s Center for Rural and Primary Healthcare (CRPH), our team developed and implemented a modeling framework to identify communities at greatest risk of OUD, hepatitis C virus (HCV), and human deficiency virus (HIV) for delivery of mobile health clinics. In collaboration with CRPH, Clemson Rural Health, and Prisma Health, we are working to identify high-risk rural communities for targeted delivery of Mobile Health Clinics.

Evaluation of policies limiting opioid exposure
In a joint collaboration between Prisma Health’s Opioid Stewardship Program and Clemson University, our teams are continuously evaluating the immediate impact and downstream effects of policies limiting opioid exposure. Importantly, we have found that policies implemented by Prisma Health, and the State of South Carolina, have successfully limited opioid exposure without compromising patient pain and discomfort.

Statewide infectious disease monitoring, prediction, and resource allocation
Our team is working on developing a dynamic simulation modeling framework for monitoring, prediction, and real-time allocation of essential resources to underserved communities in order to reduce health disparities during Covid-19 and future pandemics. Our flexible modeling framework will integrate real-time data on infectious disease outcomes with individual and community level contextual factors to inform infectious disease monitoring and improve understanding of disease epidemiology in underserved communities, and to support the decision-making process for resource allocation to high-risk populations. The proposed modeling toolkit will help inform and assist the distribution of Covid-19 mobile health clinics to medically underserved and at-risk communities.

Infectious disease epidemiology
Understanding infectious disease epidemiology is critical for understanding individual and community risk, conducting accurate disease monitoring, and implementing effective mitigation measures. This knowledge is especially useful to estimate input parameters of modeling frameworks used to allocate resources to at-risk communities. Using statistical models, we have estimated a wide range of Covid-19 epidemiological metrics, including risk of reinfection, vaccine effectiveness and waning immunity, and predictive value of clinical symptoms.

Infectious disease modeling for Institutes of Higher Education
Our team is developing an integrative modeling toolkit for Covid-19 monitoring, prediction, resource allocation, and intervention evaluation in institutes of Higher Education. Our goal is to generalize this toolkit in other institutional settings in order to inform public health decision making. This toolkit has been utilized at Clemson University to inform decision making on a wide variety of mitigation measures (e.g., testing strategies) and procurement of essential resources.    

Utilizing wastewater monitoring for early disease detection and response
Utilizing samples collected through wastewater, our team has developed dynamic models to predict active Covid-19 cases in local communities. Currently, we are working to expand wastewater detection to detect communities at high-risk for opioid overdose. The ultimate goal of this project is to supplement resource allocation models with this information to increase the timeliness and effectiveness of mobile health clinic response, community response, and educational efforts.

Building a campus-community partnership to raise public health awareness
With funding from the Interfaith Youth Core and FIVA Carolinas, we have built a partnership with 60 African-American churches in order to raise Covid-19 vaccine awareness and distribute other Covid-related information.

Identifying predictors of cognitive decline in older adults
Our team is working on an extensive examination of the relationship between cognitive reserve built up through life experiences and cognitive decline in older adults. The ultimate goal is to help identify influential factors of cognitive decline and potential timing for early interventions to promote cognitive health in older adults.

Gezer F, Howard KA, Litwin AH, Martin NK, Rennert L. Identification of factors associated with opioid-related and hepatitis C virus-related hospitalisations at the ZIP code area level in the USA: an ecological and modelling study. Lancet Public Health. 2024;9(6):E354-E364. https://doi.org/10.1016/S2468-2667(24)00076-8

Rennert L,^† Howard KA,^† Kickham CM, Gezer F, Coleman A, Roth P, Boswell K, Gimbel RW, Litwin AL. Implementation of a mobile health clinic framework for Hepatitis C virus screening and treatment: a descriptive study. Lancet Reg Health - Americas. 2024;29:100648. https://doi.org/10.1016/j.lana.2023.100648

Rennert L, McMahan CS, Kalbaugh CA, Yang Y, Lumsden B, Dean D, Pekarek L, Colenda CC. Surveillance-based informative testing for detection and containment of SARS-CoV-2 outbreaks on a public university campus: An observational and modelling study. Lancet Child Adolesc Health. 2021;5(6):428–36. https://doi.org/10.1016/S2352-4642(21)00060-2

McMahan CS, Self S, Rennert L, Kalbaugh C, Kriebel D, Graves D, Colby C, Deaver JA, Popal SC, Karanfil T, Freedman DL. COVID-19 wastewater epidemiology: A model to estimate infected populations. Lancet Planet Health. 2021;5(12):e874–81. https://doi.org/10.1016/S2542-5196(21)00230-8

Ma Z, Rennert L. An Epidemiological Modeling Framework to Inform Institutional-Level Response to Infectious Disease Outbreaks: A Covid-19 Case Study. Nature Scientific Reports. 2024;14:7221. https://doi.org/10.1038/s41598-024-57488-y

Rennert L, Ma Z, McMahan CS, Dean D. Effectiveness and protection duration of Covid-19 vaccines and previous infection against any SARS-CoV-2 infection in young adults. Nat Commun. 2022;13(1):3946. https://doi.org/10.1038/s41467-022-31469-z

Rennert L, McMahan CS. Risk of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) reinfection in a university student population. Clin Infect Dis. 2022;74(4):719–22. https://doi.org/10.1093/cid/ciab454

Rennert L,^† Howard KA,^† Kickham CM, Gezer F, Coleman A, Roth P, Boswell K, Gimbel RW, Litwin AL. Implementation of a mobile health clinic framework for Hepatitis C virus screening and treatment: a descriptive study. Lancet Reg Health - Americas. 2024;29:100648. https://doi.org/10.1016/j.lana.2023.100648. 

Howard KA, Massimo L, Griffin SF, Gagnon RJ, Zhang L, Rennert L. Systematic examination of methodological inconsistency in operationalizing cognitive reserve and its impact on identifying predictors of late-life cognition. BMC Geriatr. 2023;23(1):547. https://doi.org/10.1186/s12877-023-04263-9

Rennert L, Ma Z. An Epidemiological Modeling Framework to Inform Institutional-Level Response to Infectious Disease Outbreaks: A Covid-19 Case Study. Revision Invited (Nature Scientific Reports); 2023. https://doi.org/10.21203/rs.3.rs-3116880/v1

Gezer F, Howard KA, Litwin AH, Martin NK, Rennert L. Identification of Factors Associated with, and Prediction of, Opioid- and HCV-Related Hospitalizations at the Zip Code Level: A Statistical Modeling Approach. Revision Invited (Lancet Public Health). SSRN; 2023. https://doi.org/10.2139/ssrn.4506562

Hossfeld C, Rennert L, Baxter SLK, Griffin SF, Parisi M. The Association between Food Security Status and the Home Food Environment among a Sample of Rural South Carolina Residents. Nutrients. 2023;15(18):3918. https://doi.org/10.3390/nu15183918

Babatunde A, Rennert L, Walker KB, Furmanek DL, Blackhurst DW, Cancellaro VA, Litwin AH, Howard KA. Association between Initial Opioid Prescription and Patient Pain with Continued Opioid Use among Opioid-Naïve Patients Undergoing Elective Surgery in a Large American Health System. Int J Environ Res Public Health. 2023;20(10):5766. https://doi.org/10.3390/ijerph20105766

Rennert L, Ma Z, McMahan CS, Dean D. Covid-19 vaccine effectiveness against general SARS-CoV-2 infection from the omicron variant: A retrospective cohort study. Katoto PD, editor. PLOS Glob Public Health. 2023;3(1):e0001111. https://doi.org/10.1371/journal.pgph.0001111

Rennert L, Howard KA, Walker KB, Furmanek DL, Blackhurst DW, Cancellaro VA, Litwin AH. Evaluation of policies limiting opioid exposure on opioid prescribing and patient pain in opioid-naive patients undergoing elective surgery in a large American health system. J Patient Saf. 2022. https://doi.org/10.1097/PTS.0000000000001088

Rennert L, Ma Z, McMahan CS, Dean D. Effectiveness and protection duration of Covid-19 vaccines and previous infection against any SARS-CoV-2 infection in young adults. Nat Commun. 2022;13(1):3946. https://doi.org/10.1038/s41467-022-31469-z

McMahan CS, Lewis D, Deaver JA, Dean D, Rennert L, Kalbaugh CA, Shi L, Kriebel D, Graves, D, Popat SC, Karanfil T, Freedman DL. Predicting COVID-19 infected individuals in a defined population from wastewater RNA data. ACS EST Water. 2022;2(11):2225–32. https://doi.org/10.1021/acsestwater.2c00105

Kunkel D, Stuenkel M, Sivaraj LB, Colenda CC, Pekarek L, Rennert L. Predictive value of clinical symptoms for COVID-19 diagnosis in young adults. J Am Coll Health. 2022;1–4. https://doi.org/10.1080/07448481.2022.2068963

King KL, Wilson S, Napolitano JM, Sell KJ, Rennert L, Parkinson CL, Dean D. SARS-CoV-2 variants of concern Alpha and Delta show increased viral load in saliva. Abd El-Aty AM, editor. PLOS ONE. 2022;17(5):e0267750. https://doi.org/10.1371/journal.pone.0267750

Pericot-Valverde I, Heo M, Niu J, Rennert L, Norton BL, Akiyama MJ, Arsten J, Litwin AH. Relationship between depressive symptoms and adherence to direct-acting antivirals: Implications for Hepatitis C treatment among people who inject drugs on medications for opioid use disorder. Drug Alcohol Depend. 2022;234:109403. https://doi.org/10.1016/j.drugalcdep.2022.109403

Rennert L, McMahan CS. Risk of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) reinfection in a university student population. Clin Infect Dis. 2022;74(4):719–22. https://doi.org/10.1093/cid/ciab454

Plumb EV, Ham RE, Napolitano JM, King KL, Swann TJ, Kalbaugh CA, Rennert L, Dean D. Implementation of a rural community diagnostic testing strategy for SARS-CoV-2 in Upstate South Carolina. Front Public Health. 2022;10:858421. https://doi.org/10.3389/fpubh.2022.858421

Gormley MA, Akiyama MJ, Rennert L, Howard KA, Norton BL, Pericot-Valverde I, Muench S, Heo M, Litwin AH. Changes in health-related quality of life for Hepatitis C Virus–infected people who inject drugs while on opioid agonist treatment following sustained virologic response. Clin Infect Dis. 2022;74(9):1586–93. https://doi.org/10.1093/cid/ciab669

McMahan CS, Self S, Rennert L, Kalbaugh C, Kriebel D, Graves D, Colby C, Deaver JA, Popal SC, Karanfil T, Freedman DL. COVID-19 wastewater epidemiology: A model to estimate infected populations. Lancet Planet Health. 2021;5(12):e874–81. https://doi.org/10.1016/S2542-5196(21)00230-8

Rennert L, Kalbaugh CA, McMahan CS, Shi L, Colenda CC. The impact of phased university reopenings on mitigating the spread of COVID-19: A modeling study. BMC Public Health. 2021;21(1):1520. https://doi.org/10.1186/s12889-021-11525-x

Massimo L, Rennert L, Xie SX, Olm C, Bove J, Van Deerlin V, Irwin DJ, Grossman M, McMillan CT. Common genetic variation is associated with longitudinal decline and network features in behavioral variant frontotemporal degeneration. Neurobiol Aging. 2021;108:16–23. https://doi.org/10.1016/j.neurobiolaging.2021.07.018

Howard KA, Rennert L, Pericot-Valverde I, Heo M, Norton BL, Akiyama MJ, Agyemang L, Litwin AH. Utilizing patient perception of group treatment in exploring medication adherence, social support, and quality of life outcomes in people who inject drugs with Hepatitis C. J Subst Abuse Treat. 2021;126:108459. https://doi.org/10.1016/j.jsat.2021.108459

Rennert L, McMahan CS, Kalbaugh CA, Yang Y, Lumsden B, Dean D, Pekarek L, Colenda CC. Surveillance-based informative testing for detection and containment of SARS-CoV-2 outbreaks on a public university campus: An observational and modelling study. Lancet Child Adolesc Health. 2021;5(6):428–36. https://doi.org/10.1016/S2352-4642(21)00060-2

Heo M, Pericot-Valverde I, Rennert L, Akiyama MJ, Norton BL, Gormley M, Agyemand L, Arnsten JH, Litwin AH. Hepatitis C Virus direct-acting antiviral treatment adherence patterns and sustained viral response among people who inject drugs treated in opioid agonist therapy programs. Clin Infect Dis. 2021;73(11):2093–100. https://doi.org/10.1093/cid/ciab334

Rennert L, Heo M, Litwin AH, Gruttola VD. Accounting for confounding by time, early intervention adoption, and time-varying effect modification in the design and analysis of stepped-wedge designs: application to a proposed study design to reduce opioid-related mortality. BMC Med Res Methodol. 2021;21(1):53. https://doi.org/10.1186/s12874-021-01229-6

Charron E, Rennert L, Mayo RM, Eichelberger KY, Dickes L, Truong KD. Contraceptive initiation after delivery among women with and without opioid use disorders: A retrospective cohort study in a statewide Medicaid population, 2005–2016. Drug Alcohol Depend. 2021;220:108533. https://doi.org/10.1016/j.drugalcdep.2021.108533

Pericot‐Valverde I, Rennert L, Heo M, Akiyama MJ, Norton BL, Agyemang L, Lumsden B, Litwin AH. Rates of perfect self‐reported adherence to direct‐acting antiviral therapy and its correlates among people who inject drugs on medications for opioid use disorder: The PREVAIL study. J Viral Hepat. 2021;28(3):548–57. https://doi.org/10.1111/jvh.13445

Rennert L, Kalbaugh CA, Shi L, McMahan CS. Modelling the impact of presemester testing on COVID-19 outbreaks in university campuses. BMJ Open. 2020;10(12):e042578. https://doi.org/10.1136/bmjopen-2020-042578

^† Denotes co-first author