期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2021
卷号：118
期号：41
DOI：10.1073/pnas.2112607118
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：Significance
Initial seeding of an epidemic from the best-connected nodes of a network would intuitively lead to the largest outbreak. We challenge this picture and explore a switchover phenomenon: Epidemics started from the central part of a geometric metapopulation network can reach more individuals only if the basic reproduction number is small, but if the epidemic is more infectious, it reaches a larger population when seeded from uniformly selected nodes. We identify spatial geometry to amplify this effect in both data-driven and synthetic epidemic models, and we give rigorous proofs that this phenomenon appears in various random networks. Our results help us understand why real epidemics started from seemingly similar conditions may have significantly different outcomes.
It is a fundamental question in disease modeling how the initial seeding of an epidemic, spreading over a network, determines its final outcome. One important goal has been to find the seed configuration, which infects the most individuals. Although the identified optimal configurations give insight into how the initial state affects the outcome of an epidemic, they are unlikely to occur in real life. In this paper we identify two important seeding scenarios, both motivated by historical data, that reveal a complex phenomenon. In one scenario, the seeds are concentrated on the central nodes of a network, while in the second one, they are spread uniformly in the population. Comparing the final size of the epidemic started from these two initial conditions through data-driven and synthetic simulations on real and modeled geometric metapopulation networks, we find evidence for a switchover phenomenon: When the basic reproduction number
R
0
is close to its critical value, more individuals become infected in the first seeding scenario, but for larger values of
R
0
, the second scenario is more dangerous. We find that the switchover phenomenon is amplified by the geometric nature of the underlying network and confirm our results via mathematically rigorous proofs, by mapping the network epidemic processes to bond percolation. Our results expand on the previous finding that, in the case of a single seed, the first scenario is always more dangerous and further our understanding of why the sizes of consecutive waves of a pandemic can differ even if their epidemic characters are similar.
