Fighting Avian Influenza: An International Modeling Challenge to Strengthen Preparedness and Response to Current and Future Outbreaks

Mathematical modeling challenges in health are friendly competitions organized between scientific teams from around the world. They are a major component of epidemic preparedness, allowing modeling teams to improve their ability to respond quickly to the needs of public decision-makers in combating infectious diseases. A group of researchers from the INRAE Animal Health Department, specializing in infectious disease modeling, is launching the next challenge, dedicated for the first time to avian influenza (highly pathogenic avian influenza – HPAI).

Published on 03 September 2025

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A powerful tool for public decision-making

Mathematical models make it possible to simulate epidemics under different infectious disease management scenarios (containment, vaccination, etc.). They can thus become a valuable asset for decision-making. However, these models are time-consuming to develop, especially during a health crisis. Modeling challenges are essential to develop and test these models, thereby improving forecasts that can guide health decisions. Such challenges, organized before and during epidemics, have already contributed to improving epidemic response capacities for diseases such as Ebola, dengue, seasonal flu, and African swine fever (1).

The researchers organizing this new challenge are epidemiologists specializing in infectious diseases, working at INRAE and the National Veterinary School of Toulouse. Their expertise covers statistical and mathematical modeling, epidemic preparedness, and management measures against livestock diseases.

How does the challenge work?

A modeling challenge in health aims to be both rigorous and engaging, encouraging participants to collaborate, innovate, and share their expertise. It combines diverse modeling approaches with shared simulation data, provided in real time and identical for all participants.

During the preparation phase of the HPAI modeling challenge, the organizers generated in silico epidemiological data, reproducing a typical HPAI outbreak on the fictional island of Jolly Island, where poultry (chickens, ducks) are raised under conventional and organic production systems.

In the challenge phase itself, which will last about four months, these data will be progressively shared with the participating modeling teams. Through data analysis, these teams will provide monthly summary statistics of predicted epidemic outcomes for the following period and attempt to use their models to provide evidence to answer questions on the effectiveness of different management strategies.

Once the challenge is over, a final phase will take place during which the models will be compared, the forecasts integrated, and the lessons learned from this simulation evaluated.

Key stages of the challenge (2)

  1. From September 2025: opening of applications to modeling teams wishing to take part in a real-time public policy support simulation (free participation).
  2. January 2026 – April 2026: real-time analysis by modeling teams of data reflecting the spread of HPAI in a territory.
  3. From May 2026: synthesis of results by the organizers, organization of feedback workshops, and publication of the results.

At each stage of the challenge, risk managers (e.g. ministries of agriculture, food safety agencies) will be involved in its conduct (defining rules, formulating questions to be addressed by the teams, analyzing and synthesizing results). This collaborative work will foster better mutual understanding between modeling teams and risk managers and contribute to building a true global preparedness network against this epidemic.

HPAI continues to pose a major threat to poultry populations and wild birds worldwide. This international challenge (3)  aims to strengthen the capacity of modeling teams to rapidly provide evidence-based information to inform policy decisions and improve preparedness for these emerging threats.

 

An example of an INRAE initiative supporting public policies

The first international animal health modeling challenge was organized by researchers from INRAE’s Animal Health Department between 2020 and 2022. It focused on African swine fever (ASF), with the aim of improving forecasting of the disease’s spread.

The “Biology, Epidemiology and Risk Analysis in Animal Health” (BIOEPAR) and “Host-Pathogen Interactions” (IHAP) units of INRAE’s Animal Health Department developed an original model of the spatiotemporal spread of the ASF virus. This virtual epidemic spread at the interface between domestic pigs and wild boar in a typical European context. The number of detected cases in each host population was provided to international teams so they could reproduce the epidemic, predict its expansion, and prioritize control measures. This challenge improved the preparedness of modeling teams to face future ASF epidemics and also demonstrated that taking into account the livestock–wildlife interface is essential to increase our effectiveness in managing certain emerging animal diseases. The results were published in a special issue of the journal Epidemics.

With the second challenge dedicated to avian influenza now underway, the INRAE Animal Health Department confirms its contribution to supporting public policies by providing essential tools for managing current and future epidemics.

 

(1) For an overview of the first animal health modeling challenge, see the special issue dedicated to this challenge on African swine fever, published in the journal Epidemics. For more information on modeling challenges focused on human health, see related publications on seasonal influenza, dengue fever, and Ebola virus.

(2) To learn more about the challenge: https://www.wiliman-id.eu/modelling-challenge/

(3) This challenge is organized as part of the five-year Horizon Europe WiLiMan-ID research project coordinated by INRAE (https://www.wiliman-id.eu/),

Communication Santé animale

Scientific contact

Timothée VERGNE

Joint Research Unit “Host-Pathogen Interactions” IHAP (INRAE-ENVT)

Centre

Division

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