Using mechanistic model-based inference to understand and project epidemic dynamics with time-varying contact and vaccination rates.

ABSTRACT: Epidemiological models range in complexity from relatively simple statistical models that make minimal assumptions about the variables driving epidemic dynamics to more mechanistic models that include effects such as vaccine-derived and infection-derived immunity, population structure and heterogeneity. The former are often fitted to data in real-time and used for short-term forecasting, while the latter are more suitable for comparing longer-term scenarios under differing assumptions about control measures or other factors. Here, we present a mechanistic model of intermediate complexity that can be fitted to data in real-time but is also suitable for investigating longer-term dynamics. Our approach provides a bridge between primarily empirical approaches to forecasting and assumption-driv

SUBMITTER: Plank MJ 

PROVIDER: S-EPMC9702885 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature