Illustrative: a hexagonal population grid where a wave rises, peaks and recovers from seeded outbreaks

Risk Academy, led by the Nexus Consortium · Course catalogue · Health and Society

Global Health Security

Epidemic modelling and forecasting

Epidemic modelling and forecasting, taught twice over: as the working methods of the field and as its frontier since 2024, with labs on the Academy's tools, open data and the Nexus Standards.

Core course Frontier course

The discipline

01Core course · L2 Intermediate

Epidemic modelling and forecasting: foundations and practice

This area covers the mathematical and statistical models of infectious disease spread and their use in preparedness and response: compartmental and network models, real-time estimation of transmission, forecasting and scenario modelling, and the surveillance data they depend on.

Level
L2 Intermediate
Study time
about 24 hours (planning estimate)
Status
Planned
Prerequisites
An L1 course in the area, or an entry review

Why now

Collaborative forecast hubs became routine public health infrastructure: CDC FluSight and the ECDC respiratory forecasting hub publish weekly ensembles scored by the weighted interval score, and CDC's Center for Forecasting and Outbreak Analytics (2022) institutionalised outbreak analytics. PLOS Computational Biology, Evaluating epidemic forecasts in an interval format (Bracher et al.), 2021

Learning outcomes

  1. Define effective reproduction number and the other key terms of the discipline and use them correctly in a written case
  2. Explain compartmental models (SIR, SEIR) with interventions and real-time estimation of the reproduction number with a worked example and its source
  3. Apply the discipline's method to a documented case and explain each result with its source
  4. Analyse a named case against nowcasting and reporting delays, stating evidence, date and limits

Modules

  1. Compartmental models (SIR, SEIR) with interventions
    • Compartmental models (SIR, SEIR) with interventions
    • Real-time estimation of the reproduction number
    • Nowcasting and reporting delays
  2. Ensemble forecasting and forecast hubs
    • Ensemble forecasting and forecast hubs
    • Scenario modelling for policy
    • Network and agent-based transmission models
  3. Wastewater and genomic surveillance as model inputs
    • Wastewater and genomic surveillance as model inputs
    • Vaccination and capacity planning
    • Communicating model uncertainty
  4. Lab: the discipline's method
    • The reproduction number estimated from reported cases is only as good as the correction for two gaps between infection and the data: the share of infections ever recorded, and the days between infection and the report; left uncorrected, a growing epidemic always looks smaller and slower than it is.
    • Work the method through a documented case and explain each result
    • Read the method's sources and stated values

Labs with Academy tools

Open data

Assessment

  • 40%Lab recordWork the discipline's method through a documented case and explain each result with its source and date
  • 40%Case memoApply the discipline's methods to a named place or organisation, stating evidence and limits
  • 20%Key terms checkShort answers on the key terms, marked against the definitions

Delivery

  • Self-paced online, with the labs in the browser
  • Cohort study over six to eight weeks, with a weekly lab review
  • Co-delivered with a partner institution, inside its own courses

Partner on this course

  • Co-teach a module and bring a current case from your work
  • Host a cohort for your learners, with your own schedule and review
  • Contribute a lab: a dataset your institution publishes openly, or a model
  • Review the course content and its references before it opens
  • Translate the course and adapt its cases to your region

How institutions and experts take part

In the Academy LMS

Course on the learning platform
Intermediate, 4 topics
Cohort group
A community group for each cohort of learners
Achievement
Recorded on completion of the assessed work

References

  1. Cori, Ferguson, Fraser and Cauchemez, A new framework and software to estimate time-varying reproduction numbers during epidemics, 2013
  2. Gostic and others, Practical considerations for measuring the effective reproductive number, 2020
  3. Wallinga and Teunis, Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures, 2004
02Live map

Environmental exposure in India

Haze, nitrogen dioxide, dust, surface heat by day and night, and where people live, over India.

The interactive map needs JavaScript; the layer list and sources are below.

Layers 2

Environmental exposure

Latest available

NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth

Loading the latest NASA imagery.

Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.

Environmental exposure, framed on India.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.

Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONETSources and licences

  • We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • MODIS and VIIRS atmosphere products, NASA LANCE and LAADS DAAC; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • OMI nitrogen dioxide (Aura) and OMPS aerosol index (Suomi NPP), NASA; distributed by the NASA GES DISC; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • MERRA-2 reanalysis, NASA Global Modeling and Assimilation Office; distributed by the NASA GES DISC; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
  • NASA Fire Information for Resource Management System (FIRMS), VIIRS 375 m active fire product, NASA LANCE; vector tiles via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
  • Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
  • © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). ODbL
  • Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
  • Globe: MapLibre GL JS 6.9.1, BSD-3-Clause, © MapLibre contributors.
  • Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
  • NASA GIBS: We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
  • Air quality forecast, 5 days, PM2.5 (CAMS via Open-Meteo): Contains modified Copernicus Atmosphere Monitoring Service information 2026.
  • Past year, daily temperature (ERA5 via Open-Meteo): Contains modified Copernicus Climate Change Service information 2026.
  • Weather data by Open-Meteo.com (https://open-meteo.com/), licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
  • Optional layers (12 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.

Layers and dates

  • Aerosol optical depth, daily: VIIRS NOAA-20 Deep Blue AOD, NASA LANCE · 13 Oct 2026
  • Land surface temperature, day, 8-day composite: VIIRS NOAA-20 Level 3, 8-day, NASA LANCE · 14 Sep 2026
  • Nitrogen dioxide, lower atmosphere column: OMI Aura, NASA GES DISC · 10 Oct 2026
  • Surface dust PM2.5, monthly mean: MERRA-2, NASA GMAO / GES DISC · July 2026
  • Land surface temperature, night, 8-day composite: VIIRS NOAA-20 Level 3, 8-day, NASA LANCE · 14 Sep 2026
  • Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
  • Active fires and thermal anomalies: VIIRS NOAA-20 375 m (FIRMS), NASA LANCE FIRMS · 13 Oct 2026
  • Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026

03How learning runs

How learning runs, as a process you can run

Select a scenario and press Play: course, pathway, practice, faculty, cells and record.

04What is changing

What is changing, with standards and evidence

Epidemic modelling and forecasting: frontier methods

The methods, data, tools and studies that changed epidemic modelling and forecasting between 2024 and 2026, taught by reproducing them on open data.

Level
L3 Advanced
Study time
about 28 hours (planning estimate)
Status
Planned
Prerequisites
Epidemic modelling and forecasting: foundations and practice; Working knowledge of a scripting language (Python or R) for the labs

Why now

Collaborative forecast hubs became routine public health infrastructure: CDC FluSight and the ECDC respiratory forecasting hub publish weekly ensembles scored by the weighted interval score, and CDC's Center for Forecasting and Outbreak Analytics (2022) institutionalised outbreak analytics. PLOS Computational Biology, Evaluating epidemic forecasts in an interval format (Bracher et al.), 2021

Learning outcomes

  1. Explain what changed in epidemic modelling and forecasting since 2024 and cite the studies, data or rules behind each change
  2. Reproduce transmission models on open data and state the uncertainty of the result
  3. Evaluate a 2024 to 2026 study: its question, data, method, limits and what it changes for practice
  4. Design an analysis that combines a frontier method with the faculty's instruments, and justify each choice

Modules

  1. Transmission models
    • SIR, SEIR and renewal equations
    • Time-varying reproduction number with EpiNow2
    • Delays, reporting and nowcasting
  2. Forecasting and scenarios
    • Quantile forecasts and ensembles
    • Scenario hubs versus forecast hubs
    • The hubverse data standard
  3. Evaluation
    • Weighted interval score and calibration
    • Baselines and skill
    • Communicating forecast uncertainty
  4. Reproduction lab
    • Extend the discipline's method with one frontier method and compare its results
    • State the uncertainty and the limits of the result
    • Peer review against a shared checklist
  5. Technology that helps and the risks it carries
    • AI-assisted diagnostics, genomic surveillance and digital health.
    • Ransomware in hospitals, AI medical device risk, biosecurity and neurotechnology.

Labs with Academy tools

Open data

Assessment

  • 50%Method reproductionReproduce one frontier method on open data and state the uncertainty of the result
  • 30%Study reviewA critical review of a 2024 to 2026 study: question, data, method, limits, what it changes
  • 20%Peer reviewReview two other learners' reproductions against a shared checklist

Delivery

  • Self-paced online, with the labs in the browser
  • Cohort study over six to eight weeks, with a weekly lab review
  • Co-delivered with a partner institution, inside its own courses

Partner on this course

  • Co-teach a module and bring a current case from your work
  • Host a cohort for your learners, with your own schedule and review
  • Contribute a lab: a dataset your institution publishes openly, or a model
  • Review the course content and its references before it opens
  • Translate the course and adapt its cases to your region

How institutions and experts take part

In the Academy LMS

Course on the learning platform
Expert, 5 topics
Cohort group
A community group for each cohort of learners
Achievement
Recorded on completion of the assessed work

References

  1. PLOS Computational Biology, Evaluating epidemic forecasts in an interval format (Bracher et al.), 2021
  2. CDC, Center for Forecasting and Outbreak Analytics, 2025
  3. hubverse, Infrastructure for collaborative modelling hubs, 2025
  4. epiforecasts, EpiNow2, 2025
  5. Cori, Ferguson, Fraser and Cauchemez, A new framework and software to estimate time-varying reproduction numbers during epidemics, 2013
  6. Gostic and others, Practical considerations for measuring the effective reproductive number, 2020
  7. Wallinga and Teunis, Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures, 2004
05Take part

Take part in the work

Start from who you are.

06Questions

Questions, answers and key terms

Is the course free?
Yes. Every course and learning material of Risk Academy, led by the Nexus Consortium, is free to study, self-paced or with a cohort. Ordinary participation has no fee; paid work, internships and fellowships are separate arrangements.
What does completing a course give me?
A completion record: the course, its version, the date and what was assessed, private by default and shared by you. It stacks into discipline, department and programme achievements. A record states what was studied and assessed and does not certify competence; it is not a licence, a professional title or an employment status.
Where do the data and the places come from?
Every faculty teaches on a twin of a real place built from open data; each layer carries its provider, licence and date, and every result on the Academy states its source. Maps and figures are indicators for teaching, never an assessment of a place.
How do I take part beyond the course?
Form a competence cell of three to seven people, study a shared pathway, take a supervised assignment in the National Working Groups, or bring a cohort from your institution. For organisations.

Key terms

Term

Effective reproduction number

The average number of secondary infections caused by one infected person at a given time, written Rt.

Term

SEIR model

A compartmental model that divides a population into susceptible, exposed, infectious and recovered.

Term

Nowcasting

Estimating the current state of an epidemic while accounting for delays in reporting.

Term

Forecast hub

A platform that collects, scores and combines forecasts from many modelling teams.

ReportsContribute

Contribute evidence from this page.

Reports is the Consortium's quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.

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