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RSV Scenario Modeling Hub

Last updated: 02-10-2024 for Round 2 Scenarios.

Rationale

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.

How to participate

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.

Round 2: Projections for the 2024-25 season

The goal of this RSV round is to expand on RSV modeling capabilities developed last year by the Scenario Modeling Hub to generate ensemble projections of RSV hospitalizations for the 2024-25 season under different intervention scenarios. Two questions of particular interest are (i) the potential impact of waning of vaccine-induced immunity among seniors in the second year after vaccination and (ii) the optimal timing of infant interventions. We will consider 5 scenarios in total, following a 2 * 2 table describing the impact of infant products (first dimension, early and classic timing of administration of nirsevimab and maternal vaccine) and senior vaccination (second dimension, optimistic and pessimistic waning of vaccine effectiveness). A 5th counterfactual scenario will consider no RSV mitigation strategy implemented in either 2023-24 or 2024-25. Projections will be generated for a 45-week period, running Sun July 28, 2024 to Sat June 7, 2025.

The scenario structure is as follows:

Assumptions regarding RSV interventions

Weekly cumulative age-specific coverage for senior vaccines, maternal vaccine, and monoclonals are available in the auxiliary data folder, in the vaccine_coverage subfolder.

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.

Below, we describe important details of the planned implementation of RSV interventions as well as our rationale for intervention coverage and effectiveness assumptions.

Implementation of RSV Interventions

Infants

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 , 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 (fraction based on weekly monoclonal coverage). Specifically, for scenarios A-B (early timing of interventions), a fraction of children born Aug 15 - Mar 30 receive a birth dose of monoclonals based on weekly coverage. For scenarios C-D (classic timing of interventions), a fraction of children born Oct 1 - Mar 30 receive a birth dose of monoclonals based on weekly coverage.
  • Babies (0-7m): 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, for scenarios A-B (early timing of interventions), babies born Apr 1 - Aug 14 receive a nirsevimab dose during the first month of the campaign, namely Aug 15 -Sep 15. For scenarios C-D (classic timing of interventions), babies born Apr 1 -Sep 30 receive a dose during Oct 1-31.

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, for scenarios A-B (early timing of interventions), a fraction of 32-36 wk pregnant women receive vaccine during July 15-Jan 31 based on weekly coverage. For scenarios C-D (classic timing of interventions), a fraction of 32-36 wk pregnant women receive vaccine during Sep 1 -Jan 31 based on weekly coverage. For all scenarios A-D, we assume that 25% of eligible women are immunized.

c. No double immunization: We assume that nirsevimab is administered to babies whose mother has not received the RSV vaccine. CDC recommendations are that parents choose one of the two existing intervention strategies.
In other words, the coverage of nirsevimab and maternal vaccines should be considered additive (ie these modes of immunization target different babies).

Seniors

Senior RSV vaccine recommendations were updated for the 2024-25 season. Current recommendations stipulate that 1) Seniors vaccinated during the 2023-24 season should not be revaccinated 2) All unvaccinated seniors 75+ yrs are recommended for vaccination, and 3) All high-risk seniors 60-74 not yet vaccinated are recommended for vaccination.

We recommend that teams consider vaccination in all individuals 60+ yrs, even though the hospitalization target data is only available for individuals 65+ yrs. The exact implementation of targeting 60+ yrs is left at teams’ discretion. Vaccine curves will be provided separately for high-risk individuals 60-74 and those 75+, for both 2023-24 (reported vaccination) and 2024-25 (hypothesized vaccination).

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 last year 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).

Coverage assumptions

Vaccination will proceed seasonally, similarly to the flu vaccine, between Aug 2024 and June 2025. We will index the coverage of RSV interventions on a combination of this past year’s coverage and the flu vaccine coverage in a given state and relevant age group. Indexing on flu is meant to address the potential increase of RSV coverage in 2024-25, compared to 2023-24, and remedy the lack of state-specific coverage information for infants, while reflecting the propensity of different states to adopt health interventions. Senior and infant immunization coverage curves will be provided for all projection weeks and target locations.

Intervention effectiveness

This year, we have updated VE estimates for RSV products in light of real-world observational data published for the 2023-24 season (except for maternal vaccines, for which there is no real world effectiveness data). All VE estimates are against hospitalization.

We assume that VE is 80% for infant monoclonals and 60% for maternal vaccines. 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%. The VE in the second year is reduced due to waning, with the level of reduction depending on scenario. VE is reduced by 10% in the second year in optimistic scenarios A, C, with a second year VE assumed to be 75 * .9 = 68%. VE is reduced by 50% in the second year in scenarios B and D, so that VE = 75*.5=38%. 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, Ison at al.. The waning distribution is left at team’s discretion provided that the average VE level remains at 75% within one year of vaccination, and at 38% or 68% on average within the second year of vaccine.

A few auxiliary references are provided below.

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.

Assumptions about duration of protection

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 2 years. Within this timeframe, there can be waning of VE. We will explore waning uncertainty via our scenarios for seniors. Further, teams have discretion to explore waning for infant products, which would potentially interact with the scenario assumptions regarding early and late administration.

Other RSV-specific interventions

Consideration of non-specific RSV interventions such as a low level of residual masking is allowed.

Counterfactual scenario (scenario E)

In this scenario, we consider no change to the historic policy of RSV mitigation, which consists in the absence of senior vaccination (neither in 2023-24 nor 2024-25) 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-present 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 monoclonal nirsevimab, with comparable effectiveness.

RSV Model Calibration

RSV-NET Dataset

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 will be 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: [0-5 months], [6-11 months], [1 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.

Other RSV datasets available for calibration

A few auxiliary datasets have been posted in the GitHub repositority auxiliary-data/ folder including:

  • state-specific CDC surveillance from NVERSS (data available until end of April 2024)
  • state-specific ED data from NSSP

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..

Targets

In this round, we will require submission of 100-300 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 targets

  • Weekly reported all-age and age-specific state-level incident hospital admissions, based on RSV-NET. This dataset is updated daily and covers 2017-2024. 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 hospital
  • No case target
  • No death target
  • All targets should be numbers of individuals, rather than rates

Age target

Required
  • Hospital admissions should be provided for the following age groups: all ages, <1 yr, 1-4, 5-64, 65+. (Most of the RSV burden on hospitalizations comes from the 0-1 and 65+ age groups.)
Additional age details (optional)
  • 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.)

Optional targets (if submitting quantiles in addition to trajectories)

  • Cumulative hospital admissions. Cumulative outcomes start at 0 at the start of projections, on Nov 12, 2023
  • Cumulative and incident infection. Cumulative outcomes start at 0 at the start of projections, on Nov 12, 2023
  • State-level peak hospital admissions
  • State-level timing of peak hospital admission

Timeline

  • Scenarios set (no changes after): Friday, Oct 4, 2024
  • Projections due: Tuesday, Oct 29, 2024
  • Report finalized: No later than November 13, 2024

Other specifications and assumptions

Overall RSV dynamics and severity

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

  • 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

COVID-19 Interactions

  • 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

Projection Period

  • Sun July 28, 2024 to Sat June 7, 2025 (45 weeks)
    • NO CALIBRATION TO DATA after July 28,2024

State-level variability

  • Variability in severity and reporting to RSV-NET between states is possible

Seasonality

Teams should include their best estimates of RSV seasonality in their model but we do not prescribe a specific level of seasonal forcing.

NPI

No reactive NPIs to COVID-19 or influenza should be modeled in this round; low level masking is allowed at groups’ discretion.

Seeding of RSV

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.

Initial Conditions

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.

Submission Information

Scenario Scenario name Scenario ID for submission file (scenario_id)
Scenario A. Optimistic senior waning and early infant interventions optSen_ealyrInf A-2024-09-20
Scenario B. Pessimistic senior waning and early infant interventions pessSen_ealyrInf B-2024-09-20
Scenario C. Optimistic senior waning and classic timing of infant interventions optSen_classicrInf C-2024-09-20
Scenario D. Pessimistic senior waning and classic timing of infant interventions pessSen_classicrInf D-2024-09-20
Scenario E. Counterfactual counter_fact E-2024-09-20

  • Projection Due date: Tuesday, Oct 29, 2024

  • End date for fitting data: July 27, 2024

  • Start date for scenarios: Sunday July 28, 2024 (first date of simulated transmission/outcomes)

  • Simulation end date: Saturday June 7, 2025 (45-week horizon)

  • Desire to release results by mid-November 2024

Other submission requirements

  • Simulation trajectories: We ask that teams submit a sample of 100-300 simulations replicates. Simulations should be sampled in such a way that they will be most likely to produce 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 and will not be considered here due to lack of historic data for calibration.

  • 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 indicent 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 100 to 300 trajectories.
    • For quantiles (optional submission) We ask for 0.01, 0.025, 0.05, every 5% to 0.95, 0.975, and 0.99. Teams are also encouraged to submit 0 (min value) and 1 (max) quantiles if possible.

Target data

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.

The weekly hospitalization number per location are going to be used as truth data in the hub.

Auxiliary Data

The repository stores and updates additional data relevant to the RSV modeling efforts in the auxiliary-data/ folder:

For more information, please consult the associated README file.

Data license and reuse

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.

Computational power

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.

Teams and models

The RSV Scenario Modeling Hub Coordination Team

  • 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

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