About

Jason R. Gantenberg is an Assistant Professor of the Practice of Epidemiology at Brown University and a Senior Research Scientist in the Center for Evidence Synthesis in Health. His primary research interests include infectious disease transmission, computational modeling, causal inference, and applied machine learning. His work encompasses social and sexual networks, complex systems, and the use of agent-based models, with a focus on diseases such as gonorrhea, respiratory syncytial virus, HIV, and influenza. Gantenberg has conducted epidemiologic research on respiratory syncytial virus and HIV, and has investigated the use of ensemble machine learning techniques to predict seasonal influenza hospitalizations. His current research involves improving localization accuracy using Bluetooth and Ultrawide Band technologies, modeling biological features of Alzheimer's disease, and analyzing heart failure incidence through descriptive and causal inference methods. He holds a PhD from Brown University and has postdoctoral training at the Brown University School of Public Health. Gantenberg is actively involved in collaborations and professional societies related to epidemiology and causal inference.

Research topics

• Medicine
• Computer Science
• Psychology
• Pathology
• Internal medicine
• Immunology
• Medical education
• Virology
• Data science
• Pediatrics
• Intensive care medicine

Selected publications

• Examining Associations between COVID-19 Distress, Multilevel Resilience, and HIV Viral Suppression among African American/Black Adults in the Southeastern United States

  Journal of Health Care for the Poor and Underserved · 2026-02-01

  article

  Abstract: The COVID-19 pandemic may have exacerbated HIV viral suppression disparities between African American/Black and White people living with HIV in the United States. Although COVID-19 disrupted HIV care and caused distress, multilevel resilience resources may have mitigated the impact of COVID-19 distress on HIV viral suppression. We used prospective observational data on 124 African American/Black adults from two clinical cohorts in the Southeastern United States and modified Poisson regression to examine whether an intervention that reduced COVID-19 distress and enhanced multilevel resilience resources would have more effectively improved HIV viral suppression had such a dual intervention been implemented in the real-world during the COVID-19 pandemic. This examination did not yield strong evidence that a real-world dual intervention would have more effectively improved HIV viral suppression on the additive scale during the pandemic among study participants. Larger future studies that minimize systematic sources of bias are needed.

  PublisherDOI

• Measuring Mobility and Social Mixing to Inform Pandemic Prediction and Response

  Sustainable development goals series · 2026-01-01

  book-chapter
  PublisherDOI

• Do Amyloid Trajectories Reach a Physiologic Ceiling? Evidence from Iterative Approximation and Simulation

  medRxiv · 2026-04-21

  articleOpen access1st authorCorresponding

  Abstract Qualitative models of Alzheimer’s pathology often posit that amyloid accumulation follows a sigmoid curve, indicating that the rate of deposition wanes over time. Longitudinal PET data now allow us to investigate amyloid accumulation trajectories with greater detail and over longer follow-up periods. We combine inferences from simulated amyloid trajectories, empirical PET data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), and the sampled iterative local approximation algorithm (SILA) to assess whether amyloid accumulation reaches a physiologic ceiling. We find that SILA reliably detects a ceiling, when present, across a range of simulated scenarios that impose a sigmoid shape. When fit to empirical data from ADNI, however, SILA does not appear to indicate the presence of a ceiling. Thus, we conclude that amyloid trajectories may not reach a physiologic ceiling during the stages of Alzheimer’s disease typically observed while patients remain under follow-up in cohort studies. Fits using SILA indicate that illustrative models of biomarker cascades, while useful tools for conceptualizing and interrogating pathologic processes, may not represent the shapes of amyloid trajectories accurately. Summary for General Public Amyloid, a protein implicated in Alzheimer’s disease, is thought to reach a plateau in the brain, but methods that estimate how amyloid changes over time suggest it grows unabated. Gantenberg et al. use one such method and simulations to argue that amyloid does not reach a plateau during the typical course of Alzheimer’s.

  PublisherOA PDFDOI

• Differentially Private Modeling of Disease Transmission within Human Contact Networks

  arXiv (Cornell University) · 2026-04-08

  preprintOpen access

  Epidemiologic studies of infectious diseases often rely on models of contact networks to capture the complex interactions that govern disease spread, and ongoing projects aim to vastly increase the scale at which such data can be collected. However, contact networks may include sensitive information, such as sexual relationships or drug use behavior. Protecting individual privacy while maintaining the scientific usefulness of the data is crucial. We propose a privacy-preserving pipeline for disease spread simulation studies based on a sensitive network that integrates differential privacy (DP) with statistical network models such as stochastic block models (SBMs) and exponential random graph models (ERGMs). Our pipeline comprises three steps: (1) compute network summary statistics using \emph{node-level} DP (which corresponds to protecting individuals' contributions); (2) fit a statistical model, like an ERGM, using these summaries, which allows generating synthetic networks reflecting the structure of the original network; and (3) simulate disease spread on the synthetic networks using an agent-based model. We evaluate the effectiveness of our approach using a simple Susceptible-Infected-Susceptible (SIS) disease model under multiple configurations. We compare both numerical results, such as simulated disease incidence and prevalence, as well as qualitative conclusions such as intervention effect size, on networks generated with and without differential privacy constraints. Our experiments are based on egocentric sexual network data from the ARTNet study (a survey about HIV-related behaviors). Our results show that the noise added for privacy is small relative to other sources of error (sampling and model misspecification). This suggests that, in principle, curators of such sensitive data can provide valuable epidemiologic insights while protecting privacy.

  PublisherDOI

• Differentially Private Modeling of Disease Transmission within Human Contact Networks

  arXiv (Cornell University) · 2026-04-08

  articleOpen access

  Epidemiologic studies of infectious diseases often rely on models of contact networks to capture the complex interactions that govern disease spread, and ongoing projects aim to vastly increase the scale at which such data can be collected. However, contact networks may include sensitive information, such as sexual relationships or drug use behavior. Protecting individual privacy while maintaining the scientific usefulness of the data is crucial. We propose a privacy-preserving pipeline for disease spread simulation studies based on a sensitive network that integrates differential privacy (DP) with statistical network models such as stochastic block models (SBMs) and exponential random graph models (ERGMs). Our pipeline comprises three steps: (1) compute network summary statistics using \emph{node-level} DP (which corresponds to protecting individuals' contributions); (2) fit a statistical model, like an ERGM, using these summaries, which allows generating synthetic networks reflecting the structure of the original network; and (3) simulate disease spread on the synthetic networks using an agent-based model. We evaluate the effectiveness of our approach using a simple Susceptible-Infected-Susceptible (SIS) disease model under multiple configurations. We compare both numerical results, such as simulated disease incidence and prevalence, as well as qualitative conclusions such as intervention effect size, on networks generated with and without differential privacy constraints. Our experiments are based on egocentric sexual network data from the ARTNet study (a survey about HIV-related behaviors). Our results show that the noise added for privacy is small relative to other sources of error (sampling and model misspecification). This suggests that, in principle, curators of such sensitive data can provide valuable epidemiologic insights while protecting privacy.

  PublisherOA PDF

• Code for "Do Amyloid Trajectories Reach a Physiologic Ceiling?"

  Open MIND · 2026-01-01

  articleOpen access1st authorCorresponding

  Code to reproduce: Gantenberg JR, La Joie R, Heston MB, and Ackley SF. Do Amyloid Trajectories Reach a Physiologic Ceiling? Evidence from Iterative Approximation and Simulation.

  PublisherOA PDFDOI

• Evaluating the Presence of a Physiologic Ceiling in Amyloid Trajectories: Insights from the Sampled Iterative Local Approximation (SILA) Algorithm and Simulations

  Alzheimer s & Dementia · 2025-12-01

  articleOpen access

  BACKGROUND: With continued collection of amyloid positron emission tomography (PET) neuroimaging and new quantitative approaches to analyze longitudinal PET data, the Alzheimer's disease (AD) research field is now positioned to determine amyloid trajectories empirically. Previous studies proposing a physiologic ceiling generally do not evaluate the direct relationship between amyloid levels and time. However, newer studies using sampled iterative local approximation (SILA), a nonparametric algorithm that estimates trajectories with data reflecting differential scan ages/intervals, have not indicated the presence of an accumulation plateau at high amyloid burden. These findings contradict temporal models of AD development that argue for a physiologic ceiling. METHOD: We simulated amyloid trajectories informed by Alzheimer's Disease Neuroimaging Initiative (ADNI) study data and the Jack model of AD pathogenesis. Empirically informed stochastic parameters included age at first PET scan, number of scans per individual, and inter-scan intervals. Estimated age of amyloid positivity onset was drawn from a distribution based on prior published literature (Betthauser et al. 2022). Simulations assume interindividual variability in the physiologic ceiling and rates of amyloid accumulation. RESULT: Reimplementing SILA in ADNI shows an apparent lack of a physiologic ceiling for amyloid, consistent with prior literature (Figure 1). Simulations that assume a physiologic ceiling show qualitatively different trajectories from trajectories in ADNI data (Figure 2), and lack an increase in density at high Centiloids characteristic of individuals approaching a ceiling (Figure 3). We are developing an R package for performing realistic amyloid trajectory simulations under various assumptions about trajectory shape and variability. CONCLUSIONS: Amyloid trajectories in ADNI aligned based on SILA-estimated age of amyloid-positivity onset suggest an apparent lack of a physiologic ceiling for amyloid. This result is in contrast to prior studies which argue for a ceiling based on differences in amyloid by clinical stage and baseline amyloid level. Additionally, generating data based on influential models of temporal AD biomarker evolution does not produce trajectories consistent with those observed in ADNI. Growing longitudinal data availability and new quantitative tools should allow us to formally evaluate prevailing models of AD biomarker evolution.

  PublisherOA PDFDOI

• Evaluating the Presence of a Physiologic Ceiling in Amyloid Trajectories: Insights from the Sampled Iterative Local Approximation (SILA) Algorithm and Simulations

  Alzheimer s & Dementia · 2025-12-01

  articleOpen access

  BACKGROUND: With continued collection of amyloid positron emission tomography (PET) neuroimaging and new quantitative approaches to analyze longitudinal PET data, the Alzheimer's disease (AD) research field is now positioned to determine amyloid trajectories empirically. Previous studies proposing a physiologic ceiling generally do not evaluate the direct relationship between amyloid levels and time. However, newer studies using sampled iterative local approximation (SILA), a nonparametric algorithm that estimates trajectories with data reflecting differential scan ages/intervals, have not indicated the presence of an accumulation plateau at high amyloid burden. These findings contradict temporal models of AD development that argue for a physiologic ceiling. METHOD: We simulated amyloid trajectories informed by Alzheimer's Disease Neuroimaging Initiative (ADNI) study data and the Jack model of AD pathogenesis. Empirically informed stochastic parameters included age at first PET scan, number of scans per individual, and inter-scan intervals. Estimated age of amyloid positivity onset was drawn from a distribution based on prior published literature (Betthauser et al. 2022). Simulations assume interindividual variability in the physiologic ceiling and rates of amyloid accumulation. RESULT: Reimplementing SILA in ADNI shows an apparent lack of a physiologic ceiling for amyloid, consistent with prior literature (Figure 1). Simulations that assume a physiologic ceiling show qualitatively different trajectories from trajectories in ADNI data (Figure 2), and lack an increase in density at high Centiloids characteristic of individuals approaching a ceiling (Figure 3). We are developing an R package for performing realistic amyloid trajectory simulations under various assumptions about trajectory shape and variability. CONCLUSIONS: Amyloid trajectories in ADNI aligned based on SILA-estimated age of amyloid-positivity onset suggest an apparent lack of a physiologic ceiling for amyloid. This result is in contrast to prior studies which argue for a ceiling based on differences in amyloid by clinical stage and baseline amyloid level. Additionally, generating data based on influential models of temporal AD biomarker evolution does not produce trajectories consistent with those observed in ADNI. Growing longitudinal data availability and new quantitative tools should allow us to formally evaluate prevailing models of AD biomarker evolution.

  PublisherOA PDFDOI

• Healthcare utilization during acute medically attended episodes of respiratory syncytial virus-related lower respiratory tract infection among infants in the United States

  PLoS ONE · 2025-02-10 · 1 citations

  articleOpen access1st authorCorresponding

  BACKGROUND: Respiratory syncytial virus (RSV) is the leading cause of infant hospitalization in the United States. Understanding healthcare utilization associated with medically attended (MA) RSV lower respiratory tract infection (LRTI) might inform research priorities aimed at reducing RSV-associated pediatric morbidity. We described healthcare utilization during acute MA RSV LRTI episodes within a geographically diverse cohort of infants in the United States. METHODS: We created retrospective cohorts of infants born in the United States from July 1, 2016 through February 29, 2020 in each of three de-identified insurance claims datasets: Merative MarketScan Commercial Claims and Encounters, Multi-State MarketScan Medicaid, and Optum's de-identified Clinformatics ® Data Mart. We identified infants' first MA RSV LRTI diagnosis during their first RSV season and followed them for 7 subsequent days to record outpatient, emergency department, and inpatient hospital utilization. We calculated the number of outpatient visits, emergency department visits, and inpatient hospital stays occurring during this acute episode and estimated the proportion of episodes involving ≥ 2 visits to a given healthcare setting. RESULTS: In the CCAE database, we identified 25,409 acute MA RSV LRTI episodes under the specific RSV definition and 69,068 under the sensitive definition. In the MDCD database, these totals were 67,357 and 170,744, while in the CDM database, they were 12,402 and 31,363, respectively. Across data sources, 34%-69% of infants' first acute MA RSV LRTI episodes involve 2 or more visits to a healthcare setting within 7 days. The percentage of episodes involving at least 2 visits ranged from 34-62% among healthy term infants, 38-65% for Palivizumab-eligible infants, and 38-69% for infants with other comorbidities. CONCLUSIONS: Within a week of their first MA RSV LRTI diagnosis, infants frequently experience at least 2 visits to one or more healthcare settings, regardless of their comorbidity profile. The percentage of MA RSV LRTI episodes involving at least 2 visits to a healthcare setting may vary by insurance claims database, even between commercial payers.

  PublisherDOI

• Inpatient service utilization amongst infants diagnosed with Respiratory Syncytial Virus infection (RSV) in the United States

  PLoS ONE · 2025-01-13 · 1 citations

  articleOpen access1st authorCorresponding

  INTRODUCTION: Respiratory syncytial virus (RSV) is the leading cause of hospitalization among US infants. Characterizing service utilization during infant RSV hospitalizations may provide important information for prioritizing resources and interventions. OBJECTIVE: The objective of this study was to describe the procedures and services received by infants hospitalized during their first RSV episode in their first RSV season, in addition to what proportion of infants died during this hospitalization. METHODS: In this retrospective observational study, we analyzed three different administrative claims datasets to examine healthcare service utilization during RSV hospitalizations among infants. The study population included infants born between July 2016 and February 2020 who experienced an RSV episode during their first RSV season and had an associated inpatient hospitalization. We stratified infants into three comorbidity groups: healthy term, palivizumab-eligible, and other comorbidities. Outcomes included extracorporeal membrane oxygenation, supplemental oxygen use (in-hospital and post-discharge), mechanical ventilation (invasive and non-invasive), chest imaging, infant mortality, length of inpatient stay, intensive care unit (ICU) admission, and number of days in the ICU. RESULTS: Chest imaging was the most frequently administered procedure during RSV-associated hospitalizations, with approximately 34-38% of infants receiving it. Around one-quarter of infants were admitted to the ICU during their first RSV hospitalization. Median lengths of stay in the hospital were 3-4 days, extending to 4-6 days in the presence of ICU admission. Palivizumab-eligible infants had higher utilization of healthcare services and spent more time in the hospital or ICU compared to healthy infants or those with other comorbidities. CONCLUSIONS: This study provides insights into the utilization of healthcare services during RSV hospitalizations among infants. Understanding service utilization patterns can aid in improved management and resource allocation for infants in the United States, ultimately contributing to better outcomes and reduced healthcare costs overall. However, likely under-ascertainment of ventilation and oxygen-related services in insurance claims remains an impediment to studying these outcomes.

  PublisherOA PDFDOI

Frequent coauthors

• Andrew R. Zullo

  Brown University

  30 shared

• Robertus van Aalst

  Brown University

  30 shared

• David A. Savitz

  Brown University

  16 shared

• Brendan Limone

  IBM (United States)

  8 shared

• Chanelle J. Howe

  Providence College

  8 shared

• Brandon D. L. Marshall

  Brown University

  8 shared

• Christopher Rizzo

  Sanofi (United States)

  6 shared

• Sandra S. Chaves

  Universidade Federal de Minas Gerais

  6 shared

Labs

• Gantenberg LabPI

Education

• Ph.D., Epidemiology

  Brown University