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
3. Enhancement of methods for visualizing data and communicating analytic results to decision makers and communities

This project is in close collaboration with our implementing partners, comprising South Carolina’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. This project is also in collaboration with South Carolina Department of Public Health (SC DPH).

The decision-making toolkits will be pilot tested in real-world settings for informing:

1. Field-level interventions
2. Healthcare system and statewide disaster planning and response
3. Community awareness on 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 South Carolina, 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. Simultaneously, the public version of our toolkit will improve health outcomes through informing availability of community health care resources.

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

Contact: For information, please contact our DMA-PRIME Project Managers, Kerry Howard, Ph.D., at khowar7@clemson.edu, and Emily Serman, Ph.D., at eserman@clemson.edu.

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

Narrative: This project aims to deliver mobile health clinics (MHCs) for opioid use disorder (OUD) screening, treatment and overdose prevention in communities at high propensity for OUD with a lack of medications for OUD (MOUD) providers, and conduct a randomized controlled trial to evaluate effectiveness of peer support specialists (PSS) on MOUD initiation and retention. The project also aims to develop a framework to

1. Evaluate the impact and cost-effectiveness of PSS on preventing fatal overdose
2. Improve MHC protocols to increase effectiveness of MHC-delivered interventions for OUD
3. Assess impact and cost-effectiveness of the PSS compared to standard MHC protocol.

Mobile health clinics will be delivered to regions prioritized by the modeling framework and provide OUD screening, MOUD enrollment, fentanyl test strips and take-home naloxone.

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 South Carolina 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): “Data-Driven Approaches for Opioid Use Disorder Treatment, Recovery, and Overdose Prevention in Rural Communities via Mobile Health Clinics and Peer Support Services”, $5,546,082. 09-30-2023 to 09-29-2029. 

Contact: For information, please contact our Research Manager, Kerry Howard, Ph.D., 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 improve health outcomes during infectious disease outbreaks and other health emergencies. 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. It will also support the decision-making process for resource allocation to populations with a high disease burden. In collaboration with public health decision-makers, we will utilize our toolkit to inform real-time scheduling of South Carolina’s fleet of mobile health clinics across the state. This has potential to save countless lives during infectious disease outbreaks and will lay the foundation for effective resource allocation to high-burden communities in other health emergencies. 

Impact: Our project will improve emergency planning by developing the modeling infrastructure for community-level disease surveillance and epidemiology, 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 infectious disease-related deaths. Our modeling framework is translatable to all infectious diseases and geographic regions and has potential to save many more lives during infectious disease outbreaks and future health emergencies.  

Reference: National Library of Medicine of the National Institutes of Health R01LM014193 (PI: Lior Rennert): “Developing a dynamic modeling framework for surveillance, prediction, and real-time resource allocation to improve health outcomes during infectious disease outbreaks,” $3,092,665. 01-05-2023 to 11-30-2028.

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

Funder: South Carolina Center for Rural and Primary Healthcare

Narrative: This project addresses persistent healthcare access challenges in rural South Carolina, where long travel distances, limited transportation options and shortages of medical providers contribute to elevated rates of chronic disease and poor maternal and child health outcomes. Mobile health clinics (MHCs) offer a practical approach to reaching communities with limited access by bringing preventive services and ongoing care directly into local settings. Building on prior work on data-driven approaches to identifying high-risk rural communities for delivery of MHCs, this project seeks to enhance the reach and impact of MHCs through data-driven planning and optimization. With funding from the South Carolina Center for Rural and Primary Healthcare, our team has developed and implemented a modeling framework to identify communities for opioid use disorder, hepatitis C virus and human deficiency virus. This project will expand these efforts to address health issues that are both preventable and widespread in these areas, including maternal-child health concerns, diabetes, hypertension and cardiovascular disease.

Specifically, the project aims to:

1. Allocate MHC services to areas of greatest need
2. Reduce geographic barriers to accessing MHC services
3. Assist MHC decision-makers in identifying and prioritizing site locations for services

Impact: By equipping program leaders and partner organizations with tools to guide service scheduling and location selection, the project seeks to improve outcomes by ensuring that MHC visits occur in locations where they are most likely to support early detection, timely treatment and ongoing management for residents in rural communities. The project will support the distribution of MHC services to regions of greatest need, lead to improved MHC coverage for rural and resource-constrained populations and strengthen long-term decision-making capacity among MHC planners, promoting more efficient use of limited resources, greater patient reach and measurable improvement in health outcomes. 

Reference: South Carolina Center for Rural and Primary Healthcare (PI: Lior Rennert): “Increasing mobile health clinic utilization in rural communities,” $99,998. 07-01-2025 to 06/30/2026.

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

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 communities with limited access in order to reduce differences in health outcomes 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 communities with limited access, and to support the decision-making process for resource allocation. The proposed modeling toolkit will help inform and assist the distribution of COVID-19 mobile health clinics to communities with limited access and with greatest disease burden.

Infectious disease epidemiology

Understanding infectious disease epidemiology is critical for understanding individual and community likelihood of infection, 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 communities with greatest disease burden. Using statistical models, we have estimated a wide range of COVID-19 epidemiological metrics, including likelihood of reinfection, 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 with a greater likelihood of 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.

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.

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 local churches in order to distribute 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

Ahammed T, Hossain MS, McMahan C, Rennert L. Machine learning approaches for real-time ZIP code and county-level estimation of state-wide infectious disease hospitalizations using local health system data. Epidemics. 2025;51:100823. https://doi.org/10.1016/j.epidem.2025.100823

Gezer F, Howard KA, Bennett KJ, Litwin AH, Sease KK, Rennert L. Predicting mobile health clinic utilization of COVID-19 vaccination in South Carolina: A statistical framework for strategic resource allocation. PLOS Global Public Health. 2025;5(6):e0003837. https://doi.org/10.1371/journal.pgph.0003837

Howard KA, Gezer F, Moore CA, Witrick B, Babatunde A, Roth P, Coleman A, Boswell K, Gimbel RW, Litwin AH, Rennert L. Factors associated with utilization of mobile health clinic hepatitis C virus services among medically underserved communities in South Carolina. BMC Global Public Health. 2024;2(1):84. https://doi.org/10.1186/s44263-024-00114-w

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  † Denotes co-first author

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

Ahammed T, Hossain MS, McMahan C, Rennert L. Machine learning approaches for real-time ZIP code and county-level estimation of state-wide infectious disease hospitalizations using local health system data. Epidemics. 2025;51:100823. https://doi.org/10.1016/j.epidem.2025.100823

Hossain MS, Goyal R., Martin NK, DeGruttola V, Chowdhury MM, McMahan C, Rennert L. A flexible framework for local-level estimation of the effective reproductive number in geographic regions with sparse data. BMC Med Res Methodol. 2025;25(73). https://doi.org/10.1186/s12874-025-02525-1

Gezer F, Howard KA, Bennett KJ, Litwin AH, Sease KK, Rennert L. Predicting mobile health clinic utilization of COVID-19 vaccination in South Carolina: A statistical framework for strategic resource allocation. PLOS Global Public Health. 2025;5(6):e0003837. https://doi.org/10.1371/journal.pgph.0003837

Dotherow JE, Byrne KA, Howard KA, Rennert L, Litwin AH. Development, validity, and acceptability of the Peer Recovery Coach (PRC) Service Checklist for Medication for Opioid Use Disorder (MOUD) support. Substance Use & Misuse. 2025;30:1. https://doi.org/10.1080/10826084.2025.2508741

Howard KA, Massimo LM, Witrick B, Zhang L, Griffin S, Ross LA, Rennert L. Investigation of risk of Alzheimer’s Disease diagnosis and survival based on variation to life experiences used to operationalize cognitive research. Journal of Alzheimer's Disease. 2025;105(1):147-158. https://doi.org/10.1177/13872877251326818

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

Howard KA, Gezer F, Moore CA, Witrick B, Babatunde A, Roth P, Coleman A, Boswell K, Gimbel RW, Litwin AH, Rennert L. Factors associated with utilization of mobile health clinic hepatitis C virus services among medically underserved communities in South Carolina. BMC Global Public Health. 2024;2(1):84. https://doi.org/10.1186/s44263-024-00114-w

Rennert L,^† Howard KA,^† Kickham CM, Gezer F, Coleman A, Roth P, Boswell K, Gimbel RW, Litwin AH. 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, Gezer F, Jayawardena I, Howard KA, Bennett KJ, Litwin AH, Sease KK. Mobile health clinics for distribution of vaccinations to underserved communities during health emergencies: A COVID-19 case study. Public Health in Practice. 2024;8:100550. https://doi.org/10.1016/j.puhip.2024.100550

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

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

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 C, 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