Guillaume Fournié

This paper applies Neural Posterior Estimation to infer parameters in epidemiological and phylodynamic models using Ebola outbreak data, showing accurate and reliable posterior estimates.

This paper develops a farm distribution model to predict poultry farm locations and sizes in data-scarce regions, showing that spatial clustering increases disease transmission risk.

The paper uses citizen science data to identify wild bird species associated with HPAIV outbreaks in poultry, showing the value of such data for rapid disease surveillance.

The study shows that avian influenza virus prevalence in chickens increases after arrival at live bird markets, partly due to infections during transport and trade.

EPINEST simulates epidemic dynamics in poultry production networks to study pathogen transmission and health hazards.