Speaker
Description
Epidemic models commonly assume Markovian disease progression, in which transitions between infection states occur at constant, memoryless rates. However, real infectious diseases are inherently history-dependent: the probability of becoming infectious depends on time since exposure, and latent and infectious periods rarely follow exponential distributions. This mismatch can bias epidemic inference, leading to overestimation of key transmission parameters and mischaracterization of hidden initial conditions.
Here, we present two data-driven, history-dependent frameworks for more accurate epidemic inference. The first incorporates gamma-distributed latent and infectious periods to improve estimation of epidemiological parameters, including the reproduction number, directly from reported case data. The second reconstructs hidden exposure histories to more accurately infer the initial exposed population, even under noisy or rapidly changing outbreak conditions. Together, these approaches move epidemic inference beyond conventional Markovian assumptions, reduce systematic bias in both parameter and initial-condition estimation, and provide a more reliable foundation for epidemic analysis and prediction.
Bibliography
Eom et al., Overcoming bias in estimating epidemiological parameters with realistic history-dependent disease spread dynamics, Nature Communications, 2024.
Lim et al., A history-dependent approach for accurate initial condition estimation in epidemic models, PLOS Computational Biology, 2025
