Last updated: 23-10-2025 for Round 3 Scenarios.
Even the best models of infectious disease transmission struggle to give accurate forecasts at time scales greater than 3-4 weeks due to unpredictable drivers like changing policy environments, behavior change, development of new control measures, and stochastic events. However, policy decisions around the course of infectious diseases, particularly emerging and seasonal infections, often require projections in the time frame of months. The goal of long-term projections is to compare outbreak trajectories under different scenarios, as opposed to offering a specific, unconditional estimate of what “will” happen. As such, long-term projections can guide longer-term decision-making while short-term forecasts are more useful for situational awareness and guiding immediate response.
We have specified a set of scenarios and target outcomes to allow alignment of model projections for collective insights. Scenarios have been designed in consultation with academic modeling teams and government agencies (e.g., CDC).
This repository follows the guidelines and standards outlined by the hubverse, which provides a set of data formats and open source tools for modeling hubs.
The RSV Scenario Modeling Hub is open to any team willing to provide projections at the right temporal and spatial scales, with minimal gatekeeping. We only require that participating teams share point estimates and uncertainty bounds, along with a short model description and answers to a list of key questions about design. A major output of the projection hub is ensemble estimates of epidemic outcomes (e.g., infection, hospitalizations, and deaths), for different time points, intervention scenarios, and US jurisdictions.
Those interested to participate, please read the README file and email us at [email protected] .
Model projections should be submitted via pull request to the model-output/ folder and associated metadata should be submitted at the same time to the model-metadata/ folder of this GitHub repository. Technical instructions for submission and required file formats can be found here, here, for the metadata file and in the Wiki.
The goal of this RSV round is to generate ensemble projections of RSV hospitalizations for the 2025-26 season under different intervention scenarios. Two questions of particular interest are (i) the potential impact of vaccine waning among seniors now in their second or third year after vaccination and (ii) the potential benefits of infant interventions at different coverage levels. We will consider 5 scenarios in total, following a 2*2 table describing the impact of infant products (first dimension; moderate vs high coverage of long-acting infant monoclonals and maternal vaccines) and senior vaccination (second dimension; optimistic vs pessimistic waning of senior vaccine immunity). A 5th counterfactual scenario will consider no RSV mitigation strategies implemented during the 2023-24, 2024-25, and 2025-26 seasons (during which these new products came to market). Projections will be generated for a 45-week period, running Sunday July 27, 2025 to Saturday June 6, 2026.
The scenario structure is as follows:
- Scenarios set (no changes after): Friday, Oct 31, 2025
- Projections due: Tuesday, Nov 11, 2025
- Report finalized: End of November 2025
- New age breakdown:
- 2025-26 Round: <1 yr, 1-4, 5-49, 50-64, 65+
- 2024-25 Round: <1 yr, 1-4, 5-64, 65+
- Senior vaccination expanded age:
- 2025-26 Round: 50+ yrs (50-74 high risk, all 75+)
- 2024-25 Round: 60+ yrs (60-74 high risk, all 75+)
- 2023-24 Round: 60+ yrs (all 60+ yrs based on clinical recommendations)
- Assumptions for VE against hospitalizations for maternal immunization:
- 2025-26 Round: 75%
- 2024-25 Round: 60%
Weekly cumulative age-specific coverage for senior vaccines, maternal vaccine, and monoclonals will be provided. Because of the shifting population denominator of infants coming in and aging out of eligibility, and the issue with seniors not being recommended for revaccination, we will provide coverage as no. of doses and no. of eligible population per week, in addition to percent immunized. This year, we will consider state-specific differences in infant immunization. State-specific coverage curves will use NIS data reported for 2024-25 to establish a timeline and saturation point for vaccination nationally, while geographic differences will be indexed on last year’s state specific coverage reported to IIS.
For infants, we will consider two coverage assumptions depending on whether immunization uptake is optimistic (sc A and B) or similar to last year in 2024-25 (sc C and D). In contrast, coverage assumptions for seniors do not vary between scenarios.
Below, we describe important details of the planned implementation of RSV interventions as well as our rationale for intervention coverage and effectiveness assumptions.
Immunization recommendations for infants are available on CDC website
a. Long-acting monoclonal antibodies (nirsevimab) were recommended for prophylactic use in infants on August 3, 2023. Current recommendations are that all infants aged ≤ 7 months who are born during or entering their first RSV season should be prioritized to receive the new monoclonals. Older children up to 19 months who are at increased risk for severe RSV disease may also be recommended. We require that teams implement monoclonal interventions in infants ≤ 7 months during the RSV season, while explicit consideration of interventions in older high-risk babies (a small fraction of all US babies, see later) is at teams’ discretion. The timing of administration of long-acting monoclonals depends on the scenario modeled (Aug 15-Mar 30 or Oct 1-Mar 30). The process is as follows:
- Newborns: During the RSV campaign, a fraction of all newborns will receive long-acting monoclonals at birth. Specifically, a fraction of children born Oct 1 - Mar 30 receive a birth dose of monoclonals based on specified weekly coverage.
- Babies born before RSV season and <7 months on October 1: During the RSV campaign, a fraction of infants who were born prior to the start of the RSV campaign and are aged 0-7 months at the start of the RSV campaign will receive a dose of monoclonals. This administration takes place at an accelerated pace during the first month of the campaign. Specifically, babies born Apr 1 -Sep 30 receive a dose during Oct 1-31.
- Note: Two monoclonals will be available for the 2025-26 season, including nirsevimab and clorsevimab. The timing of administration and effectiveness of the two products is assumed to be the same. Teams have discretion to model products separately or in combination. The provided immunization coverage curves represent combined coverage for both products.
b. Maternal RSV vaccine. Maternal vaccines were recommended in fall 2023 for women who are 32 through 36 weeks pregnant during the RSV vaccine campaign. We assume that a fraction of eligible women will get one dose of maternal RSV vaccine throughout the RSV campaign. Babies will be protected at birth (approximately one month after mom’s vaccination) and throughout the RSV season. Specifically, a fraction of 32-36 wk pregnant women will receive vaccination during Sep 1 -Jan 31 based on weekly maternal vaccine coverage.
c. No double immunization: We assume that long-acting monoclonals are administered to babies whose mothers have not received the RSV vaccine. CDC recommendations are that parents choose one of the existing intervention strategies. In other words, the coverage of long-acting monoclonals and maternal vaccines should be considered additive (i.e. these modes of immunization target different babies).
Older individual RSV vaccine recommendations were updated for the 2025-26 season. Current recommendations stipulate that 1) Individuals vaccinated during prior seasons should not be revaccinated 2) All unvaccinated seniors 75+ yrs are recommended for vaccination, and 3) All unvaccinated high-risk individuals 50-74 are recommended for vaccination.
We recommend that teams consider vaccination in eligible individuals 50+ yrs. Target data are available from RSV-NET for fine age groups, including 50-64, 65-74 and 75+. The exact implementation of targeting eligible 50+ yrs is left at teams’ discretion. This year, to align with new recommendations, we will request one more age breakdown, so that we can make projections for the 50-64 and 65+ separately from the non-intervened age groups 5-49. Vaccine curves will be provided separately for high-risk individuals 50-74 and those 75+, for both 2023-24 and 2024-25 (reported vaccination) and 2025-26 (vaccination for the projection period).
We assume that CDC revaccination guidelines are followed up closely, so that seniors vaccinated last year will not get revaccinated this year. Hence the coverage from the past two seasons and this year should be considered as additive (i.e., targeting different individuals).
Further, we also assume that high risk guidelines are followed closely. Is it estimated that 50% of 60-74 yo have high-risk conditions that are eligible for RSV vaccines. Further, high-risk individuals aged 60-74 yo have a 6.2-fold (95% CI, 4.0-9.5) risk of RSV hospitalization compared to their healthy age peers (CDC personal communication and slide 104).
This year, we have updated VE estimates for maternal vaccines in light of recent real-world observational data. VE estimates for nirsevimab and senior vaccines remain as in last year’s scenarios. All VE estimates are against hospitalization.
We assume that VE is 80% for infant monoclonals and 75% for maternal vaccines based on recent CDC data. Waning immunity for these products is at teams discretion; we refer to a recent modeling paper by Hodgson et al describing waning curves from RCT data (see Fig SM3.6 SM3.9 in Supp).
For senior vaccines, the VE in the first year of vaccination is set at 75%, which is an intermediate value between estimates from a recent meta-analysis and real-world data from the 2023-24 and 2024-25 seasons in the US. The VE in the second and third years is reduced due to waning, with the level of reduction depending on scenario. VE is reduced by 10% each year in optimistic scenarios A, C, with VE assumed to be 75% the first year, 75*.9=68% in the second year and 75*.9*.9=61% in the third year. In pessimistic scenarios B and D, VE is halved each year, so that VE = 75% the first year, 75*.5=38% in the second year and 75*.5*.5=19% in the third year. The pessimistic assumption of 50% reduction by the second year is based on the lower bound of VE estimates from a RCT assessing VE in the second season after vaccine receipt, see Ison at al. and an observational case-control study assessing VE as a function of time since immunization, Surie at al. (VE declined from 69% in 1st year to 48% in 2nd year, a 30% relative decline in VE). The waning distribution is left at team’s discretion provided that the average VE level remains at the prescribed level each year after vaccination. For models that consider an effect of the vaccine against infection, the waning of VE against infection is at teams discretion, provided that the resulting VE against hospitalization follows the prescribed decline.
A few auxiliary references are provided below.
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Senior Vaccine:
- RCTS
- GSK, Papi et al
- Pfizer, Walsh et al
- Observational 2023-24 data
- RCTS
-
Infants - Nirsevimab:
- RCTS
- Full term infants, Hammitt et al
- Pre-term infants, Griffin et al
- Observational 2023-24 data
- RCTS
-
Maternal Vaccine:
- RCTS:
It is at teams’ discretion to proportionate VE values into protection against infection, protection against severe disease given infection, and any effect on transmission. However note that the current thinking and available data, Wilkins et al suggests a limited protection against infection, if any. There is no data on transmission and the possible impact on these interventions on RSV shedding, so a small to moderate transmission effect cannot be ruled out.
Teams can refer to existing literature cited in the above section. As a general guideline, monoclonals and maternal vaccines are only expected to provide sizable protection within the first 6 months after receipt, while senior protection is expected to persist for multiple years. Within this timeframe, there can be waning of VE. We will explore waning uncertainty via our scenarios for seniors. Teams have discretion to explore waning for infant products.
Consideration of non-specific RSV interventions such as a low level of residual masking is allowed.
In this scenario, we consider no change to the historic policy of RSV mitigation, namely no senior vaccination (none in 2023-24, 2024-25, or 2025-26) and a limited coverage of palivizumab monoclonals to high-risk premature infants (~2% of the US birth cohort receives a partial or full dose, Ambrose et al). The calibration data available from 2017-2023 takes into account the impact of palivizumab. Teams have discretion to consider this policy explicitly or ignore it given the small fraction of infants covered. We note that high risk premature infants <6mo who previously would have received palivizumab (the older treatment) will now receive the new long-acting monoclonals, with comparable effectiveness.
Age- and state-specific data on laboratory-confirmed RSV hospitalization rates are available for 12 states and the US from RSV-NET spanning 2017-18 to present (RSV-NET CDC Webpage). RSV-NET is an RSV hospitalization surveillance network that collects data on laboratory-confirmed RSV-associated hospitalizations through a network of acute care hospitals in a subset of states (13 states as of August 2024; WA joined for the 2024-25 season). Age-specific weekly rates per 100,000 population are reported in this system.
The data has been standardized and posted on the SMH RSV github
and is updated weekly. The target in this data is the weekly number of
hospitalizations in each given state (inc_hosp variable), for all ages and by
age group. To obtain counts, we have converted RSV-NET weekly rates based on
state population sizes. This method assumes that RSV-NET hospitals are
representative of the whole state. To obtain national US counts, we have used
the rates provided for the “overall RSV-NET network”. The data covers
2017-present. Reported age groups include: [0-6 months], [6-12 months],
[1-2 yr], [2-4 yr], [5-17 yr], [18-49 yr], [50-64 yr], and [65-74] and
75+ years. The standardized dataset provided by SMH includes week- state- and
age-specific RSV counts (the target), rates, and population sizes.
The source of age distribution used for calibration (RSV-NET vs other estimates) should be provided in the abstract meta-data that is submitted with the projections.
A few auxiliary datasets, updated weekly (except POPHIVE), are available in the auxiliary-data/rsv folder including:
- National and region-specific CDC surveillance from NREVSS
- State-specific and national ED data with demographic information (national only)
- Data on trends in RSV testing is available from POPHIVE.
Given that the risk of RSV hospitalization changes substantially throughout the first year of life, and that timing of interventions differs for catch-up babies (who are older and less at risk) vs newborns (who are at highest risk), we recommend that teams consider the risk profile of infants by month (or 2-month) of age. Detailed hospitalization risk estimates are available here, Curns et al..
In this round, we will require submission of 300-600 individual trajectories for each target while submission of quantiles is optional. Targets will be based on the RSV-NET dataset. The required targets for trajectories will be weekly RSV incident hospital admissions. We request hospitalization counts for the 12 RSV-NET states and nationally, for all ages, and for a set of minimal age groups. A more resolved set of age groups is strongly encouraged (see below). Estimates of cumulative counts can be obtained from weekly trajectories and hence we do not require trajectories for cumulative counts. Similarly, peak targets (peak hospital admission magnitude and peak timing) can be reconstructed from weekly trajectories. Teams who wish to submit quantiles along with trajectories should provide quantiles for weekly and cumulative counts, as well as for hospital admission peak size and peak timing.
- Weekly reported all-age and age-specific state-level incident hospital admissions, based on RSV-NET. This dataset is updated daily and covers 2017-2025. There should be no adjustment for reporting (=raw data from RSV-NET dataset to be projected). A current and standardized version of the weekly data has been posted here
- Optional, Weekly all-age and age-specific state-level incident infection
- No case target
- No death target
- All targets should be numbers of individuals, rather than rates
- Hospital admissions should be provided for the following age groups: all ages, <1 yr, 1-4, 5-49, 50-64, 65+. (Most of the RSV burden on hospitalizations comes from the 0-1 and 65+ age groups.)
- Weekly state-specific and national RSV hospitalizations among individuals <1 yr, 1-4, 5-17, 18-49, 50-64, 65-74, 75+ and all ages (Most of the RSV burden on hospitalizations comes from the 0-1 and 65-74 and 75+ age groups.)
- Cumulative hospital admissions. Cumulative outcomes start at 0 at the start of projections, on July 27, 2025
- Cumulative and Incident infection. Cumulative outcomes start at 0 at the start of projections, on July 27, 2025
- State-level peak hospital admissions
- State-level timing of peak hospital admission
- Scenarios set (no changes after): Friday, Oct 31, 2025
- Projections due: Tuesday, Nov 11, 2025
- Report finalized: End of November 2025
Several reference studies are worth considering to set (or guide) RSV model
parameters that cannot be estimated from the available hospitalization data.
These include work by Ginny Pitzer and colleagues in the US (see
Pitzer et al for state-specific
models driven by environmental drivers in the pre-intervention era, including
Table 2 for parameters; and
Zheng et al for an
updated model with interventions).
Risk of severity given infection was parametrized in these models based on
children cohort studies in the US and Kenya: see
Nokes et al,
Glezen et al, and
Breese Hall at al.
Since these studies have been published, there has been an increased recognition of the burden of RSV among seniors (see Jackson et al, Rha et al, and McLaughlin et al
- Prior immunity is at each team’s discretion. Immunity against infection is waning rapidly for RSV; based on prior modeling work, estimates of duration of immunity against infection range between 200-365 days. However, immunity against severe disease can be more long-lasting and generally increases with age and number of prior infections (eg see discussion in Pitzer et al). Overall, most individuals will get reinfected multiple times throughout life, but severe RSV infections that lead to hospitalizations tend to only occur among young children and seniors. Maternal immunity is expected to be brief
- Teams are allowed to vary prior immunity by age or other demographic characteristic, and state
- Unlike influenza virus, antigenic evolution is not a key feature of RSV
- No major interactions with future COVID-19 and flu surges (e.g., immunological, social, behavioral) should be considered in this round
- We note that many studies have reported that RSV circulation was perturbed during the COVID19 pandemic, as can be seen in the RSVnet data. Whether and how to fit the COVID19 pandemic period is left at teams discretion
- Sun July 27, 2025 to Sat June 6, 2026 (45 weeks)
- NO CALIBRATION TO DATA after July 27,2025
- Variability in severity and reporting to RSV-NET between states is possible
Teams should include their best estimates of RSV seasonality in their model but we do not prescribe a specific level of seasonal forcing.
No reactive NPIs to COVID-19 or influenza should be modeled in this round; low level masking is allowed at groups’ discretion.
We leave seeding intensity, timing and geographic distribution at the discretion of the teams. In addition to the RSV-NET hospital admission dataset, CDC’s NREVSS viral surveillance dataset is a good resource for state-specific information on epidemic intensity (e.g., weekly % positive, or weekly ILI*%positive), and can be used to adjust seeding.
Prior immunity and amount of infections at the start of the projection period is at the discretion of the teams based on their interpretation of the scenarios. Variation in initial prevalence between states is left at teams’ discretion.
All of the teams’ specific assumptions should be documented in meta-data and abstract.
| Scenario | Scenario name | Scenario ID for submission file (scenario_id) |
|---|---|---|
| Scenario A. Optimistic senior waning and high coverage of infant interventions | optSenWan_highInfCov | A-2025-10-31 |
| Scenario B. Pessimistic senior waning and high coverage of infant interventions | pessSenWan_highInfCov | B-2025-10-31 |
| Scenario C. Optimistic senior waning and moderate coverage of infant interventions | optSenWan_modInfCov | C-2025-10-31 |
| Scenario D. Pessimistic senior waning and moderate coverage of infant interventions | pessSenWan_modInfCov | D-2025-10-31 |
| Scenario E. Counterfactual | counterfactual | E-2025-10-31 |
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Projection Due date: Tuesday, Nov 11, 2025
-
End date for fitting data: Saturday July 26, 2025
-
Start date for scenarios: Sunday July 27, 2025 (first date of simulated transmission/outcomes)
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Simulation end date: Saturday June 6, 2026 (45-week horizon)
-
Desire to release results by end November 2025
-
Simulation trajectories: We ask that teams submit a sample of 300-600 trajectories most likely to capture the uncertainty of the simulated process. For some models, this may mean a random sample of simulations, for others with larger numbers of simulations, it may require weighted sampling. Trajectories will need to be paired across age, horizon, and scenarios (e.g., for a given model, location, scenario and week, all age data for simulation 1 corresponds to the sum of age-specific estimates for simulation 1).
-
Geographic scope: state-level and national projections
- 12 states or a subset of 12 states, US overall recommended. We note that WA joined RSV-NET in 2024-25 as a 13th participating state. Projections for WA are optional.
-
Results:
- Summary: Results must consist of a subset of weekly targets listed below; all are not required.
- Weeks follow epi-weeks (Sun-Sat) dated by the last day of the week.
- Weekly Targets:
- Weekly incident hospitalizations by location, all ages and age-specific
- Weekly incident infection by location, all ages and age-specific (optional)
-
Metadata: We will require a brief meta-data form, from all teams.
-
Uncertainty:
- For trajectories (required submission): we require 300 to 600 trajectories, paired.
- For quantiles (optional submission) We ask for 0.01, 0.025, 0.05, every 5% to 0.95, 0.975, and 0.99.
The target-data/ folder contains the RSV hospitalization data (also called "truth data") standardized from the Weekly Rates of Laboratory-Confirmed RSV Hospitalizations from the RSV-NET Surveillance System. in the hubverse format.
The weekly hospitalization number per location is used as truth data in the hub.
More information on the source, workflow and archive files are available in the auxiliary-data/target-data/ folder.
The code to generate the data are available in the src folder.
The repository stores and updates additional data relevant to the RSV modeling efforts in the auxiliary-data/ folder:
-
Vaccine Coverage: data on vaccination coverage that can be used for a specific round
-
Population and census data:
- National and State level name and fips code as used in the Hub and associated population size.
- State level population size per year and per age from the US Census Bureau.
-
Birth Rate:
- Birth Number and Rate per state and per year from 1995 to 2022 included.
- Data from the US Census Bureau and from the Centers for Disease Control and Prevention, National Center for Health Statistics. National Vital Statistics System, Natality on CDC WONDER Online Database.
-
RSV data:
- The National Respiratory and Enteric Virus Surveillance System (NREVSS) data at national and state level [ARCHIVED].
- The Weekly Rates of Laboratory-Confirmed RSV Hospitalizations from the RSV-NET Surveillance System
- The National Emergency Department Visits for COVID-19, Influenza, and Respiratory Syncytial Virus
-
Target Data Archive: archive of the target-data time-series data. The data are automatically updated on Monday, and a version with additional information (rate, population size). The data are automatically moved to this folder, with the date append to the file name.
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Reports: Reports from RSV Scenario Modeling Hub rounds results. Each report contains an executive summary with key messages and results, and analyses of ensemble and individual projections.
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Rounds: Information on ongoing round and previous round available in the repository
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Model Examples: Model output and metadata example files
For more information, please consult the associated README file.
All source code that is specific to the overall project is available under an open-source MIT license. We note that this license does NOT cover model code from the various teams, model scenario data (available under specified licenses as described above) and auxiliary data.
Those teams interested in accessing additional computational power should contact Katriona Shea at [email protected]. Additional resources might be available from the MIDAS Coordination Center - please contact [email protected] for information.
Scenario modeling groups are supported through grants to the contributing investigators.
The Scenario Modeling Hub site is supported by the MIDAS Coordination Center, NIGMS Grant U24GM132013 (2019-2024) and R24GM153920 (2024-2029) to the University of Pittsburgh.
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CEPH Lab at Indiana University - MetaRSV
- Marco Ajelli (Indiana University Bloomington), Shreeya Mhade (Indiana University Bloomington), Paulo C. Ventura (Indiana University Bloomington), Maria Litvinova (Indiana University Bloomington), Snigdha Agrawal (Indiana University Bloomington), Allisandra G. Kummer (Indiana University Bloomington), Kedir Turi (Indiana University Bloomington)
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Columbia University — RSV_SVIRS
- Teresa Yamana (CU), Sen Pei (CU)
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Johns Hopkins University and University of North Carolina at Chapel Hill - flepiMoP
- Shaun Truelove (JHU), Alison Hill (JHU), Justin Lessler (UNC), Sara Loo (JHU), Joseph Lemaitre (UNC), Anjalika Nande (JHU), Madeleine Gastonguay (JHU), Sung-mok Jung (UNC), Timothy Willard (UNC), Carl Pearson (UNC), Vivek Murali (JHU)
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MOBS Lab at Northeastern University - GLEAM RSV Model
- Alessandro Vespignani (Network Science Institute, NEU), Matteo Chinazzi (The Roux Institute, NEU, Portland (ME); Network Science Institute, NEU), Jessica T. Davis (Network Science Institute, NEU), Clara Bay (Network Science Institute, NEU), Guillaume St-Onge (The Roux Institute, NEU, Portland (ME); Network Science Institute, NEU),
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National Institutes of Health - RSV_MSIRS
- Kaiyuan Sun (Fogarty International Center, NIH), Cécile Viboud (Fogarty International Center, NIH)
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National Institutes of Health - RSV_WIN
- Chelsea Hansen (Fogarty International Center, NIH), Samantha Bents (Fogarty International Center, NIH), Cécile Viboud (Fogarty International Center, NIH)
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Predictive Sciences - Package for Respiratory Disease Open-source Forecasting
- James Turtle (Predictive Science Inc), Michal Ben-Nun (Predictive Science Inc), Pete Riley (Predictive Science Inc)
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University of North Carolina Charlotte - Hierbin
- Chen S (UNCC), Janies D (UNCC), Paul R (UNCC)
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University of Notre Dame - FRED
- Sean Moore (UND), Alex Perkins (UND), Guido Espana (CDC Center for Forecasting and Analysis)
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University of Southern California - SIkJalpha
- Ajitesh Srivastava (USC), Majd Al Aawar (USC)
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University of Texas at Austin - UT-ImmunoSEIRS
- Kaiming Bi (UTA), Shraddha Ramdas Bandekar (UTA), Anass Bouchnita (The University of Texas at El Paso), Spencer J. Fox (The University of Georgia), Lauren Ancel Meyers (UTA)
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University of Virginia - EpiHiper Scenario Modeling for RSV
- Jiangzhuo Chen (UVA), Stefan Hoops (UVA), Bryan Lewis (UVA), Srini Venkatramanan (UVA), Parantapa Bhattacharya (UVA), Dustin Machi (UVA), Madhav Marathe (UVA)
- National Institutes of Health - RSV_Phenomenological
- Kaiyuan Sun (Fogarty International Center, NIH), Cécile Viboud (Fogarty International Center, NIH)
- Shaun Truelove, Johns Hopkins University
- Cécile Viboud, NIH Fogarty
- Justin Lessler, University of North Carolina
- Sara Loo, Johns Hopkins University
- Lucie Contamin, University of Pittsburgh
- Emily Howerton, Penn State University
- Claire Smith, Johns Hopkins University
- Harry Hochheiser, University of Pittsburgh
- Katriona Shea, Penn State University
- Michael Runge, USGS
- Erica Carcelen, John Hopkins University
- Sung-mok Jung, University of North Carolina
- Jessi Espino, University of Pittsburgh
- John Levander, University of Pittsburgh
- Samantha Bents, NIH Fogarty
- Katie Yan, Penn State University