Epidemic spreading under mutually independent intra- and inter-host pathogen evolution

Xiyun Zhang, Zhongyuan Ruan, Muhua Zheng, Jie Zhou, Stefano Boccaletti, Baruch Barzel

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


The dynamics of epidemic spreading is often reduced to the single control parameter R0 (reproduction-rate), whose value, above or below unity, determines the state of the contagion. If, however, the pathogen evolves as it spreads, R0 may change over time, potentially leading to a mutation-driven spread, in which an initially sub-pandemic pathogen undergoes a breakthrough mutation. To predict the boundaries of this pandemic phase, we introduce here a modeling framework to couple the inter-host network spreading patterns with the intra-host evolutionary dynamics. We find that even in the extreme case when these two process are driven by mutually independent selection forces, mutations can still fundamentally alter the pandemic phase-diagram. The pandemic transitions, we show, are now shaped, not just by R0, but also by the balance between the epidemic and the evolutionary timescales. If mutations are too slow, the pathogen prevalence decays prior to the appearance of a critical mutation. On the other hand, if mutations are too rapid, the pathogen evolution becomes volatile and, once again, it fails to spread. Between these two extremes, however, we identify a broad range of conditions in which an initially sub-pandemic pathogen can breakthrough to gain widespread prevalence.

Original languageEnglish
Article number6218
JournalNature Communications
Issue number1
StatePublished - Dec 2022

Bibliographical note

Funding Information:
X.Z. thanks Dr. Xiaobo Chen, Dr. Tingting Shi, Dr. Xing Lu and Prof. Weirong Zhong for their fruitful discussions and support of the numerical simulations. This work was supported by the NNSF of China (grant No. 12105117; X.Z., and 12005079; M.Z.), the Fundamental Research Funds for Central Universities (grant No. 21621007; X.Z.), the Guangdong Basic and Applied Basic Research Foundation (grant No. 2022A1515010523; X.Z.), the Science and Technology Planning Project of Guangzhou (grant No. 202201010360; X.Z.), the funding for Scientific Research Startup of Jiangsu University (grant No. 4111710001; M.Z.), Jiangsu Specially-Appointed Professor Program (M.Z.), the Israel Science Foundation (grant No. 499/19; B.B.), the bi-national Israel-China ISF-NSFC joint research program (grant No. 3552/21; B.B.) and the Bar-Ilan University Data Science Institute grant for COVID-19 related research (B.B.).

Publisher Copyright:
© 2022, The Author(s).


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