ModAH Hub
Federating the scientific community working in epidemiological modelling in animal health
The aim of this international initiative is to federate the scientific community working on epidemiological modeling in animal health. By fostering collaboration and friendly competition rather than rivalry, the initiative seeks to promote the sharing of best modeling practices and improve access to critical data in the field. Additionally, it aims to develop new methods, address current modeling challenges, facilitate model comparison and ensemble modeling, and strengthen interactions with end-users and stakeholders.
ModAH Conference 2026
The 4th international scientific conference dedicated to modelling in animal health to be held in Nantes, France.
This event will bring together scientists from all over the world around one theme, in order to open new avenues of research and international collaborations. For more informations, visit the dedicated conference page.
ModAH webinar series
Join the ModAH-Hub webinar series, held on the last Friday of every month (usually at 2:00 PM UTC+2). Each session highlights ongoing work, emerging ideas, and recent developments, while creating space for discussion and exchange. The series aims to foster dialogue, encourage collaboration, and keep participants informed about the latest activities and initiatives within the community.
Ongoing and planned working groups
Effective modeling practices are essential to ensure that models accurately represent epidemiological systems. Conceptually, this involves selecting appropriate model structures, formulating realistic assumptions, and designing models that capture the essential dynamics of disease transmission and control. Programming aspects encompass the implementation of models using robust and efficient code, leveraging modern software engineering principles such as version control, modularity, and reproducibility. Adhering to best practices in both conceptual design and programming enhances model reliability, facilitates collaboration among researchers, and ensures that simulations and analyses are both accurate and scalable.
Within-host dynamics focus on interactions between pathogens and host biological systems. Modeling these processes helps clarify mechanisms of infection, immune response, and pathogen evolution. Understanding within-host dynamics is crucial for developing effective treatments, vaccines, and predicting disease outcomes. Challenges include capturing complex biological interactions and integrating multi-scale data.
Phylodynamics combines evolutionary biology and epidemiology to study pathogen genetic changes over time. By analyzing genetic data, models trace transmission pathways, estimate mutation rates, and understand evolutionary pressures. These insights inform surveillance and control strategies.
The modelling-policy nexus explores how epidemiological models inform public health decision-making. Models provide evidence supporting control measures, resource allocation, and risk assessment. Effective collaboration between modelers and policymakers ensures models address relevant questions and are applied appropriately to guide policy.
High-quality data is crucial for building and validating epidemiological models, serving as the foundation for accurate and meaningful insights. Ensuring data quality involves addressing accuracy, completeness, consistency, and timeliness, which are essential for reliable model outcomes. Adhering to FAIR principles enhances data management and sharing, enabling integration of surveillance data, genetic information, environmental factors, and host demographics. Challenges include data availability, accuracy, integration from multiple sources, and maintaining FAIR compliance.
Parameter inference involves estimating the values of model parameters that best fit observed data. Techniques such as Bayesian inference, maximum likelihood estimation, and machine learning are commonly used. Accurate parameter estimation is vital for model credibility and for understanding key factors influencing disease spread. Challenges include dealing with uncertainty, limited data, and computational complexity.
Ensemble modelling combines multiple models to improve predictive performance and account for uncertainty. By integrating diverse modelling approaches, ensemble methods provide more robust forecasts and insights. Intervention ranking leverages ensemble outputs to prioritize control strategies based on effectiveness and feasibility, supporting evidence-based decision-making.
Forecasting aims to predict the future course of disease outbreaks using epidemiological models. Accurate forecasts are essential for proactive public health responses. Challenges include handling uncertainty, adapting to changing conditions, and integrating real-time data. Advanced forecasting techniques allow anticipating trends and informing timely interventions.
The inter-species interface examines interactions between species that facilitate disease transmission, such as zoonotic spillover. Modeling these interfaces helps identify critical transmission points, assess risks, and develop strategies to prevent cross-species outbreaks. Challenges include understanding complex ecological relationships and integrating data across species.
Multi-scale modelling integrates processes occurring at multiple biological and ecological levels, from molecular interactions to population dynamics. This approach links within-host processes with population-level outcomes, enhancing understanding of complex epidemiological systems and improving predictive power.
Interconnecting epidemiological models with economic, environmental, or social models enables a holistic understanding of disease dynamics and their broader impacts. This integration supports evaluating multifaceted interventions and exploring complex interactions between health and other societal factors. Challenges include ensuring compatibility between modelling frameworks and managing computational complexity.
Modelling Challenges
Modelling challenges are structured exercises in which multiple teams use their models to analyse the same outbreak scenario or dataset. By comparing predictions, assumptions, and proposed control strategies under shared conditions, they help assess model performance, identify uncertainty, and improve the use of modelling for decision-making and preparedness. The ModAH Hub actively encourages and supports the design of such exercises to promote collaboration, stimulate methodological innovation, and strengthen preparedness for animal health threats.
The HPAI Modelling Challenge is a large-scale international initiative designed to enhance modelling preparedness for highly pathogenic avian influenza at the interface between poultry and wild bird populations. Bringing together a broad and diverse community of teams, it relies on incrementally released synthetic outbreak data to reconstruct epidemic trends, forecast future spread, and assess alternative intervention strategies.
The ASF Modelling Challenge brought together five international teams to compare their ability to predict the spread of African swine fever in a synthetic epidemic involving both domestic pigs and wild boar. Beyond forecasting the spatial and temporal dynamics of the disease, the challenge also assessed how models could help prioritise control measures, highlighting the value of model comparison, ensemble approaches, and collaboration across complementary disciplines.
Coordinators
Gaël Beaunée
INRAE, France
Gaël is a researcher in epidemiology, specialized in epidemiological modelling and likelihood-free inference, at the French National Research Institute for Agriculture, Food and Environment - INRAE. His work aims to unravel the complexity of the mechanisms underlying the spread of infectious diseases at different scales, using mathematical models and computer simulations.
Hélène Cecilia
INRAE, France
Hélène is a researcher in epidemiology and mathematical modeling at the French National Research Insitute for Agriculture, Food and Environment - INRAE. She’s primarily interested in the contribution of hosts and vectors to the spread of vector-borne, zoonotic diseases. She develops mechanistic and phenomenological models to answer questions at different scales : from countrywide risk mapping to within-host/vector viral dynamics and immune response.
Pauline Ezanno
INRAE, France
Pauline is a senior researcher at INRAE, in BIOEPAR unit (Nantes, France), working in epidemiological modelling and population dynamics. She aims at better understanding, anticipating and controlling the multi-scale spread of pathogens and the spatio-temporal dynamics of vector-host interactions in animal populations.
Steering committee
Gaël Beaunée
INRAE, France
Gaël is a researcher in epidemiology, specialized in epidemiological modelling and likelihood-free inference, at the French National Research Institute for Agriculture, Food and Environment - INRAE. His work aims to unravel the complexity of the mechanisms underlying the spread of infectious diseases at different scales, using mathematical models and computer simulations.
Quirine ten Bosch
Wageningen University & Research, the Netherlands
Quirine is a Professor in Infectious Disease Epidemiology working in the Quantitative Veterinary Epidemiology group at Wageningen University. She uses data-driven statistical and mathematical modeling approaches to better understand how pathogens spread in populations. She has a specific interest in the spread and control of vector-borne diseases.
Hélène Cecilia
INRAE, France
Hélène is a researcher in epidemiology and mathematical modeling at the French National Research Insitute for Agriculture, Food and Environment - INRAE. She’s primarily interested in the contribution of hosts and vectors to the spread of vector-borne, zoonotic diseases. She develops mechanistic and phenomenological models to answer questions at different scales : from countrywide risk mapping to within-host/vector viral dynamics and immune response.
Katharine R. Dean
Norwegian Veterinary Institute, Norway
Katharine is a researcher in epidemiology at the Norwegian Veterinary Institute. She specializes in using mathematical models to understand the spread of pathogens in aquaculture and livestock systems. Her main interest lies in integrating predictive models with near-real-time data to target surveillance and control measures during disease outbreaks.
Pauline Ezanno
INRAE, France
Pauline is a senior researcher at INRAE, in BIOEPAR unit (Nantes, France), working in epidemiological modelling and population dynamics. She aims at better understanding, anticipating and controlling the multi-scale spread of pathogens and the spatio-temporal dynamics of vector-host interactions in animal populations.
Simon Firestone
University of Melbourne, Australia
Simon is an Associate Professor in Veterinary Epidemiology and Public Health at The University of Melbourne, Australia. His research focuses on modelling infectious disease outbreaks, Bayesian diagnostic test validation, zoonoses surveillance, outbreak investigation and control. Current projects include: Enhancing Models for Rapid Decision-Support in Emergency Animal Disease Outbreaks (HASTE), capacity development with the Asia Pacific Consortium of Veterinary Epidemiologists (APCOVE) and on lumpy skin disease outbreak modelling, diagnostic development and surveillance planning in Indonesia, Australia and Japan. Simon is also a founding member of the World Organisation for Animal Health Collaborating Centre for Diagnostic Test Validation Science in the Asia-Pacific Region.
Egil A.J. Fischer
Utrecht University, the Netherlands
Dr. Egil A.J. Fischer is an Associate Professor of Veterinary Infectious Disease Epidemiology at the Faculty of Veterinary Medicine, Utrecht University. His research and teaching focus on understanding the spread of infectious diseases and antibiotic-resistant microorganisms in animal populations, particularly livestock. He places emphasis on integrating experimental research and quantitative modelling to develop comprehensive insight.
Rowland R. Kao
University of Edinburgh, United Kingdom
Rowland is professor of Veterinary Epidemiology and Data Science at University of Edinburgh. He trained as a physicist and his research interests include disease ecology, epidemiology, social network analysis, and statistical inference and on process models, including the exploitation of very large datasets to understand and control infectious diseases.
Gustavo Machado
NC State University, United States
His research develops mathematical methods using contact networks and population dynamics to develop multiscale disease control strategies to reduce the burden of livestock and poultry endemic and emerging diseases. His work encompasses various aspects of disease modeling, including developing mathematical models fitted to real-world populations, animal/semen, and vehicle movement data. Dr. Machado has experience in the regulatory aspects of disease control, nationally and internationally. He works in close partnership with government, industry, and academic stakeholders nationally and internationally and provides training. Dr. Machado is also the founder and lead manager of the RABapp systems.
Andrei MihalcaUSAMV Cluj-Napoca, Romania
Jana Schulz
Friedrich-Loeffler-Institut, Germany
Jana focuses on mathematical models and investigates the spread, surveillance and control of animal diseases. She concentrates particularly on analyzing the impact of different transmission routes on the course of disease and on assessing the effectiveness of current and potential surveillance and control measures.
Timothée Vergne
ENVT, France
Timothée Vergne is an associate professor of veterinary public health at the National Veterinary School of Toulouse, and leads the epidemiology lab of the IHAP research unit. Tim’s primary research interests include the understanding of detection and transmission processes of infectious diseases of humans and animals using statistical and mathematical modelling tools, in order to optimize the way infectious diseases are managed.
Subscribe to our mailing-list
If you’d like to join the ModAH Hub initiative and/or receive information on the progress of this initiative, you can subscribe to the mailing list via the form above.
Contact
For any questions regarding this initiative or the website, the easiest way to reach us is to send an email to the team.
This initiative is supported by INRAE (French National Research Institute for Agriculture, Food and the Environment) and the Animal Health department.
