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Pandemic cryptic phase modeling hub

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# Pandemic cryptic phase modeling hub

There are limited modeling capabilities to address the initial “cryptic” phase
of a pandemic, which is characterized by noisy data, rapid changes in
reporting, high uncertainty, and the importance of connectivity and external
seeding. Yet this is a period where models can help answer key public health
questions, including the extent of local transmission, how the epidemic might
unfold, and the anticipated benefits of different interventions. The Scenario
Modeling Hub (SMH) plans to fill this gap in modeling capabilities by running
one or more modeling rounds addressing the early arrival of a hypothetical new
respiratory virus in the US, France and the UK.

Inspired by earlier work on
[Ebola](https://pubmed.ncbi.nlm.nih.gov/28958414/), 
the SMH cryptic phase effort will rely on synthetic epidemiological
data, i.e., outbreak data generated by a model. The rationale for relying on
synthetic data is several fold. First, if we used real world data on historic
pandemics and conducted a retrospective round of modeling, SMH participants
would be aware of the realized future and projections would not be blind to
future outbreak information. Second, use of real world data does not allow any
control over multiple factors that play an important role in the cryptic phase
of a pandemic, including epidemiological conditions (eg amount of testing,
pathogen transmissibility) or data quality (eg, reporting rate and delays).
Further, by conducting a prospective modeling effort where participants are
blind to the future, we can run a form of tabletop exercise that improves
disease modeling and scenario design in a pandemic context. Note that the
proposed modeling effort will not necessarily proceed at the accelerated
timescale that a real pandemic would require. The primary goal of this work is
to develop and improve modeling capabilities, while respecting competing time
constraints on participating modelers.

This repository follows the guidelines and standards outlined by the 
[hubverse](https://hubverse.io/), which provides a set of data formats 
and open source tools for modeling hubs.

## How to participate

The Pandemic cryptic phase round is ongoing and open to any modeler willing to provide 
projections at the right temporal and spatial scales. Scenarios are currently in development 
and will be shared in mid-2026, with a projection deadline set for the second half of 2026.

Those interested to participate and/or to join the associated Slack channel and periodic meetings, 
please email us at
[scenariohub\@midasnetwork.us](mailto:scenariohub@midasnetwork.us).

## Synopsis of the cryptic phase round

Synthetic epidemiological data will be generated by two separate but 
linked models. The Global Epidemic and Mobility model, 
[GLEAM](https://www.gleamproject.org/), will be used to simulate the 
global spread of a hypothetical new respiratory virus (virus X) out 
of Brisbane, Australia. GLEAM will generate data on the course of the 
epidemic at the source location and importations into the US, UK and France. 
Once the hypothetical virus has been seeded in the US, UK and France, an 
[agent-based model, Epihiper](https://academic.oup.com/pnasnexus/article/4/1/pgae557/7921484), 
will take over and simulate local transmission. A set of natural history 
parameters for virus X will be chosen in line with prior respiratory 
virus outbreaks, but will not be identical to any past outbreak. These 
parameters will not be disclosed to participating modeling teams to 
ensure the exercise is as realistic as possible. Participating teams 
will be asked to project the future of the outbreak in two US states (CA and VA), 
UK and France. We recommend that teams try to project all locations, although 
submissions of projections for a subset of locations will be accepted.

Measurement error will be applied to simulated epidemiological observations 
to account for limited and changing rates of testing, and reporting noise 
and delays. Participating teams will be provided with one instance of 
observational outbreak data, accounting for measurement error, including 
weekly cases and deaths at the source, line list of US importations, and 
weekly cases and deaths in VA and CA, France and the UK. To provide context 
for this hypothetical case study, fictional situation reports will be 
generated and provide a broad timeline of the outbreak at the source in 
Australia, information on virus X clinical manifestations, severity, and any 
interventions implemented in Australia. Limited information on natural history 
parameters will be provided as well, as would be available in the early 
days of any real outbreak based on modeling of limited data at the source.

We will consider at least two sequential rounds of predictions for the 
US, UK and France:

1. Cryptic phase round 1 (earliest phase of the outbreak in the US, UK 
   and France): scenario projections for early emergence of a hypothetical 
   pandemic virus originating from Australia and seeded into the US, UK 
   and France
2. Cryptic phase round 2 (slightly later phase of the outbreak in the US, 
   UK and France): inference of disease parameters for a hypothetical 
   pandemic virus. as more detailed local epidemiological data becomes 
   available in the US, UK and France

The team in charge of generating the data will be separate from 
participating modeling teams and from the regular SMH coordination group

In round 1, SMH teams will be asked to provide projections of outbreak 
trajectories in the US, UK and France under different scenarios. Scenarios 
will be left to the discretion of the SMH coordination team (independent of 
the data-generation team), and will be established after discussion with the 
participating teams and public health stakeholders, in keeping with the 
regular SMH process. While we cannot fully anticipate what these scenarios will 
be, scenario axes will presumably follow the usual 2 * 2 design. For instance, 
one axis may cover disease uncertainty (eg, reporting rate, R0, or a combination) 
and a second axis may cover interventions (eg, testing intensity in round 1); 
alternatively both axes may cover disease uncertainty. Predictions targets will 
include simulations of reported cases and deaths in CA and VA, UK and France. 
Teams may choose to focus on one or more locations. We may also request 
projections of infections, to facilitate comparison between models (as cases 
and deaths are subject to reporting assumptions). We will request that teams 
submit simulation trajectories rather than quantiles.

In round 2, we will address inference of disease parameters and will request 
point estimates and distributions (format TBD) of the serial interval, doubling 
time, Rt, case fatality rate, and infection fatality rate. For pathogen-specific 
parameters such as the case fatality rate and infection fatality rate, we will 
evaluate the accuracy of model estimates against the ground truth synthetic data. 
For parameters that are model-dependent such as Rt, we will compare estimates 
across different models and methods as well as against the ground truth 
data-generating model.

## Deliverables:

Predictions of component models and ensemble outputs will be made publicly 
available on Github, along with meta-data summarizing the different modeling 
approaches. We also encourage participating teams to post their modeling code 
and assumptions when feasible. Ultimately, we will describe the results and 
lessons of this work in a supplementary issue (journal TBD), in which each 
participating team will be encouraged to submit one or more publications, as 
well as co-authoring the overview publication.

## Broad significance of this work:

Through the proposed SMH cryptic phase project, we will advance both the 
operational and research aspects of long-term disease projections, including:

1) From an operational angle, we will develop the modeling capabilities of 
SMH teams to handle the early stages of a hypothetical pandemic (particularly, 
models will need to account for changing levels of noise and reporting). We 
will also practice scenario design in the context of a pandemic.

2) From a research angle, we will explore how synthetic data generation 
can help advance the science of scenario projections. This is a unique 
case study of a hypothetical outbreak, where the full ground truth is known, 
allowing for a full evaluation of model accuracy and proper accounting for 
deviations between scenario assumptions and reality, which has been a limitation 
with prior [SMH evaluation](https://www.nature.com/articles/s41467-023-42680-x). 
Reliance on real-world disease data is restricted to a “single realization”, 
which severely limits model improvement.

Thus, this work will pave the way for a large library of (synthetic) outbreak data 
to improve calibration and evaluation of future disease models.

## Cryptic Scenario Modeling Hub - Resources

An associated GitHub repository: [cryptic-scenario-modeling-hub_resources](https://github.com/midas-network/cryptic-scenario-modeling-hub_resources/) 
contains all the data, documents, and contextual
materials for the **Scenario Modeling Hub Cryptic Challenge (C-CHA)**.

The repository contains epidemiological data, news reports, government advisories, WHO situation reports,
and scientific publications that progressively reveal the natural history and spread of the disease.
The goal is for teams to use these materials to inform their scenario modeling efforts.

> **Important:** All documents, news articles, scientific papers, and advisories in the repository are **fictional** and were created solely for the purposes of this challenge.

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