Samantha Bents

All Publications

• Trends in County-Level Childhood Vaccination Exemptions in the US. JAMA Fattah, M., Stoffel, L. A., Bubar, K. M., Bents, S. J., Maldonado, Y., Hotez, P. J., Kiang, M. V., Lo, N. C. 2026

  View details for DOI 10.1001/jama.2025.24407

  View details for PubMedID 41533386

  View details for PubMedCentralID PMC12805488

• Multiplex serology reveals age-specific immunodynamics of respiratory pathogens in the wake of the COVID-19 pandemic. Nature communications Bents, S. J., Martin, E. T., Stevens-Ayers, T., Andrews, C., Adler, A., Perofsky, A. C., Krantz, E. M., Blazevic, R., Kimball, L., Prentice, R., Hansen, C., Starita, L., Han, P., Englund, J. A., Wolter, N., von Gottberg, A., Maake, L., Moyes, J., Cohen, C., Boeckh, M., Hay, J. A., Waghmare, A., Viboud, C. 2025; 16 (1): 11015

  Abstract

  The rebound of endemic respiratory viruses following the COVID-19 pandemic was marked by atypical transmission dynamics, with children experiencing increased disease burden and out-of-season epidemics as restrictions relaxed. Here we used serology from a newly developed quantitative multiplex assay to assess the post-pandemic immunity debt. We assessed age-specific antibody dynamics across a broad range of respiratory viruses, including influenza, respiratory syncytial virus, seasonal coronaviruses, and SARS-CoV-2 using serology collected in King County, Washington, US, from 2020-2022 (n = 1508). We found that respiratory virus immunodynamics differed between individuals <5 years of age and older individuals, with young children experiencing larger boosts and quicker waning of antibodies across pathogens. We confirmed that these patterns are upheld in a non-pandemic setting by analyzing influenza serology collected in South Africa between 2016-2018 (n = 1028). We incorporated our serological insights into an influenza transmission model calibrated to epidemiological data from King County and show that consideration of age-specific immunodynamics may be important to anticipate the effects of pandemic perturbations.

  View details for DOI 10.1038/s41467-025-65957-9

  View details for PubMedID 41372127

  View details for PubMedCentralID PMC12695953

• Factors predicting incidence of nontuberculous mycobacteria in an era of climate change and altered ecosystems in the United States. The Science of the total environment Bents, S. J., Powell, C., French, J. P., Prevots, D. R., Mercaldo, R. A. 2025; 999: 180338

  Abstract

  Nontuberculous mycobacteria (NTM) are ubiquitous environmental bacteria that cause chronic pulmonary disease. Incidence patterns have risen globally over the last several decades. Prior studies suggest that climate change may have a role in increasing incidence patterns.We analyzed NTM incidence from two US-based populations: Medicare beneficiaries and persons with cystic fibrosis (pwCF). We identified predictors of NTM incidence with time-lagged meteorological and severe weather event covariates across US climate zones.The average annual incidence of NTM was 30.4 per 100,000 for the Medicare population and 2071.4 per 100,000 pwCF, with both populations showing rising incidence over the study period. We found that several factors predicted NTM incidence risk for the Medicare population and pwCF. In the Southeastern US particularly, floods were predictors of NTM incidence risk and across the mid-latitude US, dust storms were predictors of incidence. Air pressure, cloud cover, precipitation, and the number of days above various temperature thresholds were consistent predictors of NTM incidence across climate zones. The lag time between predictive meteorological variation or weather events and NTM incidence varied by zone and population studied.Geographic heterogeneity exists in the meteorological and severe event factors predictive of NTM incidence, evidenced by data from two high-risk study populations in the US. The role of continued climate change in the spatial and temporal distribution of NTM incidence merits further research.

  View details for DOI 10.1016/j.scitotenv.2025.180338

  View details for PubMedID 40897094

  View details for PubMedCentralID PMC12482993

• Scenario Projections of COVID-19 Burden in the US, 2024-2025. JAMA network open Loo, S. L., Jung, S. M., Contamin, L., Howerton, E., Bents, S. J., Hochheiser, H., Runge, M. C., Smith, C. P., Carcelén, E. C., Yan, K., Lemaitre, J. C., Przykucki, E., McKee, C. D., Sato, K., Hill, A. L., Chinazzi, M., Davis, J. T., Bay, C., Vespignani, A., Chen, S., Paul, R., Janies, D., Thill, J. C., Moore, S. M., Perkins, T. A., Srivastava, A., Al Aawar, M., Bi, K., Bandekar, S. R., Bouchnita, A., Fox, S. J., Meyers, L. A., Porebski, P., Venkatramanan, S., Lewis, B., Chen, J., Marathe, M., Ben-Nun, M., Turtle, J., Riley, P., Shea, K., Viboud, C., Lessler, J., Truelove, S. 2025; 8 (9): e2532469

  Abstract

  COVID-19 remains a disease with high burden in the US, prompting continued debate about optimal targets for annual vaccination.To project COVID-19 burden in the US for April 2024 to April 2025 under 6 scenarios of immune escape (20% and 50% per year) and levels of vaccine recommendation (no recommendation, vaccination for individuals at high risk only, vaccination for all eligible groups) and to assess the potential benefit of vaccine recommendations in reducing disease burden.For this decision analytical model, the US Scenario Modeling Hub, a collaborative modeling effort, convened 9 teams to provide scenario projections of US COVID-19 hospitalizations and deaths for April 2024 to April 2025, under 6 scenarios combining levels of immune escape and possible vaccine recommendations.Annually reformulated vaccines were assumed to be 75% effective against hospitalization for variants circulating on June 15, 2024, and available on September 1, 2024. Age- and state-specific coverage was assumed to be as reported in September 2023 to April 2024.Ensemble estimates were made for weekly COVID-19 hospitalizations and deaths. Projections are presented for relative and absolute prevented hospitalizations and deaths averted due to vaccination over the April 2024 to April 2025 period.For the US population (332 million, with an estimated 58 million aged ≥65 years), COVID-19 was expected to cause 814 000 (95% projection interval [PI], 400 000-1.2 million) hospitalizations and 54 000 (95% PI, 17 000-98 000) deaths for April 2024 to April 2025, comparable in magnitude to the prior year. Vaccination of high-risk groups only was projected to reduce hospitalizations (compared to no vaccination recommendation) by 76 000 (95% CI, 34 000-118 000) and deaths by 7000 (95% CI, 3000-11 000) across both immune escape scenarios. Compared with vaccinating high-risk groups only, a universal vaccine recommendation was projected to provide direct and indirect benefits, further preventing 11 000 hospitalizations and 1000 deaths in those aged 65 years and older.In this decision analytical modeling study of COVID-19 burden in the US in 2024 to 2025, ensemble projections suggested that although vaccinating high-risk groups had substantial benefits in reducing disease burden, maintaining the vaccine recommendation for all individuals had the potential to save thousands more lives. Despite divergence of projections from observed disease trends in 2024 to 2025-possibly driven by variant emergence patterns and immune escape-averted COVID-19 burden due to vaccination was robust across immune escape scenarios, emphasizing the substantial benefit of broader vaccine availability for all individuals.

  View details for DOI 10.1001/jamanetworkopen.2025.32469

  View details for PubMedID 40965885

  View details for PubMedCentralID PMC12447233

• Scenario Projections of Respiratory Syncytial Virus Hospitalizations Averted Due to New Immunizations. JAMA network open Hansen, C. L., Lee, L., Bents, S. J., Perofsky, A. C., Sun, K., Starita, L. M., Adler, A., Englund, J. A., Chow, E. J., Chu, H. Y., Viboud, C. 2025; 8 (6): e2514622

  Abstract

  In 2023, new immunization strategies became available for preventing respiratory syncytial virus (RSV) hospitalizations in infants and older adults. Modeling studies to understand the population-level impact of their use are important for public health planning.To estimate the number of hospitalizations averted in 2023 to 2024 due to new RSV immunization strategies and provide scenario projections for future seasons.This decision analytical model examined RSV hospitalizations in King County, Washington, from October 7, 2023, through April 26, 2025. The population of King County was disaggregated into infants younger than 6 months, infants aged 6 to 11 months, children aged 1 to 4 years, children/adults aged 5 to 59 years, adults aged 60 to 74 years, and adults aged 75 years or older.Respiratory syncytial virus vaccination for adults aged 60 years or older, maternal RSV vaccination, and long-acting monoclonal antibodies (nirsevimab) for infants younger than 8 months.The proportion of RSV hospitalizations averted in adults aged 60 years or older and infants younger than 1 year were estimated using an RSV transmission model calibrated to RSV hospitalizations.The RSV transmission model simulated the population of King County, which includes approximately 2.3 million individuals, with 23 700 infants younger than 1 year and 446 500 adults aged 60 years or older. During the 2023 to 2024 RSV season, 21.2% of adults aged 60 to 74 years, 32.7% of adults aged 75 years or older, and 33.0% of infants were protected through active or passive immunization. A total of 125 (95% projection interval [PI], 77-192) RSV hospitalizations were averted, with most of the benefit observed in infants younger than 6 months (28.6% [95% PI, 26.9%-30.5%] reduction from baseline) and adults aged 75 years or older (14.8% [95% PI, 14.3%-15.5%] reduction from baseline). For the 2024 to 2025 season, optimistic scenarios of high immunization coverage (50% in older adults and 80% in infants) projected reductions of 29.8% (95% PI, 29.1%-30.8%) in adults aged 75 years or older and 68.8% (95% PI, 66.0%-71.7%) in infants younger than 6 months compared with a counterfactual scenario with no immunizations. Targeting infants eligible for catch-up doses of nirsevimab early in the season increased the proportion of RSV hospitalizations averted in infants aged 6 to 11 months from 31.7% (95% PI, 29.4%-33.9%) to 40.4% (95% PI, 39.0%-42.1%). If vaccine protection in adults aged 75 years or older waned by 50% in the second year after immunization, the proportion of RSV hospitalizations averted was projected to decrease to 22.2% (95% PI, 21.7%-23.0%).In this decision analytical model of RSV immunizations, the results suggest a modest reduction in RSV-diagnosed hospitalizations during the 2023 to 2024 season due to limited availability of immunization products, particularly for infants. A higher uptake earlier in the season may lead to substantial reductions in RSV hospitalizations in the 2024 to 2025 season.

  View details for DOI 10.1001/jamanetworkopen.2025.14622

  View details for PubMedID 40498487

  View details for PubMedCentralID PMC12159778

• Nontuberculous mycobacterial pulmonary disease (NTM PD) incidence trends in the United States, 2010-2019. BMC infectious diseases Bents, S. J., Mercaldo, R. A., Powell, C., Henkle, E., Marras, T. K., Prevots, D. R. 2024; 24 (1): 1094

  Abstract

  Nontuberculous mycobacteria (NTM) are ubiquitous environmental bacteria that cause chronic lung disease. Rates of NTM pulmonary disease (NTM PD) have increased over the last several decades, yet national estimates in the United States (US) have not been assessed since 2015.We used a nationally representative population of Medicare beneficiaries aged ≥ 65 years to assess rates of NTM PD in a high-risk population from 2010 to 2019. Poisson generalized linear models were used to assess the annual percent change in incidence in the overall population and among key demographic groups such as sex, geography, and race/ethnicity. We evaluated the relative prevalence of various comorbid conditions previously found to be associated with NTM PD.We identified 59,724 cases of incident NTM PD from 2010 to 2019 from an annual mean population of 29,687,097 beneficiaries, with an average annual incidence of 20.1 per 100,000 population. NTM PD incidence was overall highest in the South and among women, Asian individuals, and persons aged ≥ 80 years relative to other studied demographic groups. The annual percent change in NTM PD incidence was highest in the Northeast, at 6.5%, and Midwest, at 5.9%, and among women, at 6.5%. Several comorbid conditions were highly associated with concurrent NTM diagnosis, including allergic bronchopulmonary aspergillosis, bronchiectasis, and cystic fibrosis.Here we provide current estimates of NTM PD incidence and prevalence and describe increasing trends in the US from 2010 to 2019. Our study suggests a need for improved healthcare planning to handle an increased future caseload, as well as improved diagnostics and therapeutics to better detect and treat NTM PD in populations aged ≥ 65 years.

  View details for DOI 10.1186/s12879-024-09965-y

  View details for PubMedID 39358723

  View details for PubMedCentralID PMC11445848

• Differential impact of COVID-19 non-pharmaceutical interventions on the epidemiological dynamics of respiratory syncytial virus subtypes A and B. Scientific reports Holmdahl, I., Bents, S. J., Baker, R. E., Casalegno, J. S., Trovão, N. S., Park, S. W., Metcalf, J. E., Viboud, C., Grenfell, B. 2024; 14 (1): 14527

  Abstract

  Nonpharmaceutical interventions (NPIs) implemented during the COVID-19 pandemic have disrupted the dynamics of respiratory syncytial virus (RSV) on a global scale; however, the cycling of RSV subtypes in the pre- and post-pandemic period remains poorly understood. Here, we used a two subtype RSV model supplemented with epidemiological data to study the impact of NPIs on the two circulating subtypes, RSV-A and RSV-B. The model is calibrated to historic RSV subtype data from the United Kingdom and Finland and predicts a tendency for RSV-A dominance over RSV-B immediately following the implementation of NPIs. Using a global genetic dataset, we confirm that RSV-A has prevailed over RSV-B in the post-pandemic period, consistent with a higher R0 for RSV-A. With new RSV infant monoclonals and maternal and elderly vaccines becoming widely available, these results may have important implications for understanding intervention effectiveness in the context of disrupted subtype dynamics.

  View details for DOI 10.1038/s41598-024-64624-1

  View details for PubMedID 38914626

  View details for PubMedCentralID PMC11196647

• Potential impact of annual vaccination with reformulated COVID-19 vaccines: Lessons from the US COVID-19 scenario modeling hub. PLoS medicine Jung, S. M., Loo, S. L., Howerton, E., Contamin, L., Smith, C. P., Carcelén, E. C., Yan, K., Bents, S. J., Levander, J., Espino, J., Lemaitre, J. C., Sato, K., McKee, C. D., Hill, A. L., Chinazzi, M., Davis, J. T., Mu, K., Vespignani, A., Rosenstrom, E. T., Rodriguez-Cartes, S. A., Ivy, J. S., Mayorga, M. E., Swann, J. L., España, G., Cavany, S., Moore, S. M., Perkins, T. A., Chen, S., Paul, R., Janies, D., Thill, J. C., Srivastava, A., Aawar, M. A., Bi, K., Bandekar, S. R., Bouchnita, A., Fox, S. J., Meyers, L. A., Porebski, P., Venkatramanan, S., Adiga, A., Hurt, B., Klahn, B., Outten, J., Chen, J., Mortveit, H., Wilson, A., Hoops, S., Bhattacharya, P., Machi, D., Vullikanti, A., Lewis, B., Marathe, M., Hochheiser, H., Runge, M. C., Shea, K., Truelove, S., Viboud, C., Lessler, J. 2024; 21 (4): e1004387

  Abstract

  Coronavirus Disease 2019 (COVID-19) continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. Here, we present projections of COVID-19 hospitalizations and deaths in the United States for the next 2 years under 2 plausible assumptions about immune escape (20% per year and 50% per year) and 3 possible CDC recommendations for the use of annually reformulated vaccines (no recommendation, vaccination for those aged 65 years and over, vaccination for all eligible age groups based on FDA approval).The COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023 and April 15, 2025 under 6 scenarios representing the intersection of considered levels of immune escape and vaccination. Annually reformulated vaccines are assumed to be 65% effective against symptomatic infection with strains circulating on June 15 of each year and to become available on September 1. Age- and state-specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster. State and national projections from 8 modeling teams were ensembled to produce projections for each scenario and expected reductions in disease outcomes due to vaccination over the projection period. From April 15, 2023 to April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November to January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% projection interval (PI) [1,438,000, 4,270,000]) hospitalizations and 209,000 (90% PI [139,000, 461,000]) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% confidence interval (CI) [104,000, 355,000]) fewer hospitalizations and 33,000 (95% CI [12,000, 54,000]) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000-598,000) fewer hospitalizations and 49,000 (95% CI [29,000, 69,000]) fewer deaths.COVID-19 is projected to be a significant public health threat over the coming 2 years. Broad vaccination has the potential to substantially reduce the burden of this disease, saving tens of thousands of lives each year.

  View details for DOI 10.1371/journal.pmed.1004387

  View details for PubMedID 38630802

  View details for PubMedCentralID PMC11062554

• The US COVID-19 and Influenza Scenario Modeling Hubs: Delivering long-term projections to guide policy. Epidemics Loo, S. L., Howerton, E., Contamin, L., Smith, C. P., Borchering, R. K., Mullany, L. C., Bents, S., Carcelen, E., Jung, S. M., Bogich, T., van Panhuis, W. G., Kerr, J., Espino, J., Yan, K., Hochheiser, H., Runge, M. C., Shea, K., Lessler, J., Viboud, C., Truelove, S. 2024; 46: 100738

  Abstract

  Between December 2020 and April 2023, the COVID-19 Scenario Modeling Hub (SMH) generated operational multi-month projections of COVID-19 burden in the US to guide pandemic planning and decision-making in the context of high uncertainty. This effort was born out of an attempt to coordinate, synthesize and effectively use the unprecedented amount of predictive modeling that emerged throughout the COVID-19 pandemic. Here we describe the history of this massive collective research effort, the process of convening and maintaining an open modeling hub active over multiple years, and attempt to provide a blueprint for future efforts. We detail the process of generating 17 rounds of scenarios and projections at different stages of the COVID-19 pandemic, and disseminating results to the public health community and lay public. We also highlight how SMH was expanded to generate influenza projections during the 2022-23 season. We identify key impacts of SMH results on public health and draw lessons to improve future collaborative modeling efforts, research on scenario projections, and the interface between models and policy.

  View details for DOI 10.1016/j.epidem.2023.100738

  View details for PubMedID 38184954

  View details for PubMedCentralID PMC12444780

• Modeling the impact of COVID-19 nonpharmaceutical interventions on respiratory syncytial virus transmission in South Africa. Influenza and other respiratory viruses Bents, S. J., Viboud, C., Grenfell, B. T., Hogan, A. B., Tempia, S., von Gottberg, A., Moyes, J., Walaza, S., Hansen, C., Cohen, C., Baker, R. E. 2023; 17 (12): e13229

  Abstract

  The South African government employed various nonpharmaceutical interventions (NPIs) to reduce the spread of SARS-CoV-2. Surveillance data from South Africa indicates reduced circulation of respiratory syncytial virus (RSV) throughout the 2020-2021 seasons. Here, we use a mechanistic transmission model to project the rebound of RSV in the two subsequent seasons.We fit an age-structured epidemiological model to hospitalization data from national RSV surveillance in South Africa, allowing for time-varying reduction in RSV transmission during periods of COVID-19 circulation. We apply the model to project the rebound of RSV in the 2022 and 2023 seasons.We projected an early and intense outbreak of RSV in April 2022, with an age shift to older infants (6-23 months old) experiencing a larger portion of severe disease burden than typical. In March 2022, government alerts were issued to prepare the hospital system for this potentially intense outbreak. We then assess the 2022 predictions and project the 2023 season. Model predictions for 2023 indicate that RSV activity has not fully returned to normal, with a projected early and moderately intense wave. We estimate that NPIs reduced RSV transmission between 15% and 50% during periods of COVID-19 circulation.A wide range of NPIs impacted the dynamics of the RSV outbreaks throughout 2020-2023 in regard to timing, magnitude, and age structure, with important implications in a low- and middle-income countries (LMICs) setting where RSV interventions remain limited. More efforts should focus on adapting RSV models to LMIC data to project the impact of upcoming medical interventions for this disease.

  View details for DOI 10.1111/irv.13229

  View details for PubMedID 38090227

  View details for PubMedCentralID PMC10710953

• Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty. Nature communications Howerton, E., Contamin, L., Mullany, L. C., Qin, M., Reich, N. G., Bents, S., Borchering, R. K., Jung, S. M., Loo, S. L., Smith, C. P., Levander, J., Kerr, J., Espino, J., van Panhuis, W. G., Hochheiser, H., Galanti, M., Yamana, T., Pei, S., Shaman, J., Rainwater-Lovett, K., Kinsey, M., Tallaksen, K., Wilson, S., Shin, L., Lemaitre, J. C., Kaminsky, J., Hulse, J. D., Lee, E. C., McKee, C. D., Hill, A., Karlen, D., Chinazzi, M., Davis, J. T., Mu, K., Xiong, X., Pastore Y Piontti, A., Vespignani, A., Rosenstrom, E. T., Ivy, J. S., Mayorga, M. E., Swann, J. L., España, G., Cavany, S., Moore, S., Perkins, A., Hladish, T., Pillai, A., Ben Toh, K., Longini, I., Chen, S., Paul, R., Janies, D., Thill, J. C., Bouchnita, A., Bi, K., Lachmann, M., Fox, S. J., Meyers, L. A., Srivastava, A., Porebski, P., Venkatramanan, S., Adiga, A., Lewis, B., Klahn, B., Outten, J., Hurt, B., Chen, J., Mortveit, H., Wilson, A., Marathe, M., Hoops, S., Bhattacharya, P., Machi, D., Cadwell, B. L., Healy, J. M., Slayton, R. B., Johansson, M. A., Biggerstaff, M., Truelove, S., Runge, M. C., Shea, K., Viboud, C., Lessler, J. 2023; 14 (1): 7260

  Abstract

  Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections.

  View details for DOI 10.1038/s41467-023-42680-x

  View details for PubMedID 37985664

  View details for PubMedCentralID PMC10661184

• Application of a forecasting model to mitigate the consequences of unexpected RSV surge: Experience from the post-COVID-19 2021/22 winter season in a major metropolitan centre, Lyon, France. Journal of global health Casalegno, J. S., Bents, S., Paget, J., Gillet, Y., Ploin, D., Javouhey, E., Lina, B., Morfin, F., Grenfell, B. T., Baker, R. E. 2023; 13: 04007

  Abstract

  The emergence of COVID-19 triggered the massive implementation of non-pharmaceutical interventions (NPI) which impacted the circulation of respiratory syncytial virus (RSV) during the 2020/2021 season.A time-series susceptible-infected-recovered (TSIR) model was used early September 2021 to forecast the implications of this disruption on the future 2021/2022 RSV epidemic in Lyon urban population.When compared to observed hospital-confirmed cases, the model successfully captured the early start, peak timing, and end of the 2021/2022 RSV epidemic. These simulations, added to other streams of surveillance data, shared and discussed among the local field experts were of great value to mitigate the consequences of this atypical RSV outbreak on our hospital paediatric department.TSIR model, fitted to local hospital data covering large urban areas, can produce plausible post-COVID-19 RSV simulations. Collaborations between modellers and hospital management (who are both model users and data providers) should be encouraged in order to validate the use of dynamical models to timely allocate hospital resources to the future RSV epidemics.

  View details for DOI 10.7189/jogh.13.04007

  View details for PubMedID 36757127

  View details for PubMedCentralID PMC9893715