AI for Drug Discovery
Marianna Rapsomaniki, Jannis Born, et al.
AMLD EPFL 2024
Without vaccines and treatments, societies must rely on non-pharmaceutical intervention strategies to control the spread of emerging diseases such as COVID-19. Though complete lockdown is epidemiologically effective, because it eliminates infectious contacts, it comes with significant costs. Several recent studies have suggested that a plausible compromise strategy for minimizing epidemic risk is periodic closure, in which populations oscillate between wide-spread social restrictions and relaxation. However, no underlying theory has been proposed to predict and explain optimal closure periods as a function of epidemiological and social parameters. In this work we develop such an analytical theory for SEIR-like model diseases, showing how characteristic closure periods emerge that minimize the total outbreak, and increase predictably with the reproductive number and incubation periods of a disease– as long as both are within predictable limits. Using our approach we demonstrate a sweet-spot effect in which optimal periodic closure is maximally effective for diseases with similar incubation and recovery periods. Our results compare well to numerical simulations, including in COVID-19 models where infectivity and recovery show significant variation.
Marianna Rapsomaniki, Jannis Born, et al.
AMLD EPFL 2024
Julien Cors, Aditya Kashyap, et al.
PLoS ONE
Yinghong Xiao, Peter V. Lidsky, et al.
Cell
Roger D. Traub, Miles A. Whittington, et al.
Reviews in the Neurosciences