  In this study, we considered the problem of estimating epidemiological
parameters based on physics-informed neural networks (PINNs). In practice, not
all trajectory data corresponding to the population estimated by epidemic
models can be obtained, and some observed trajectories are noisy. Learning
PINNs to estimate unknown epidemiological parameters using such partial
observations is challenging. Accordingly, we introduce the concept of algebraic
observability into PINNs. The validity of the proposed PINN, named as an
algebraically observable PINNs, in terms of estimation parameters and
prediction of unobserved variables, is demonstrated through numerical
experiments.
