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- Title
Stochastic transmission in epidemiological models.
- Authors
Albani, Vinicius V. L.; Zubelli, Jorge P.
- Abstract
Recent empirical evidence suggests that the transmission coefficient in susceptible-exposed-infected-removed-like (SEIR-like) models evolves with time, presenting random patterns, and some stylized facts, such as mean-reversion and jumps. To address such observations we propose the use of jump-diffusion stochastic processes to parameterize the transmission coefficient in an SEIR-like model that accounts for death and time-dependent parameters. We provide a detailed theoretical analysis of the proposed model proving the existence and uniqueness of solutions as well as studying its asymptotic behavior. We also compare the proposed model with some variations possibly including jumps. The forecast performance of the considered models, using reported COVID-19 infections from New York City, is then tested in different scenarios. Despite the simplicity of the epidemiological model, by considering stochastic transmission, the forecasted scenarios were fairly accurate.
- Subjects
NEW York (N.Y.); EPIDEMIOLOGICAL models; STOCHASTIC processes; COVID-19
- Publication
Journal of Mathematical Biology, 2024, Vol 88, Issue 3, p1
- ISSN
0303-6812
- Publication type
Article
- DOI
10.1007/s00285-023-02042-z