Beyond network structure: How heterogeneous susceptibility modulates the spread of epidemics
Abstract
The compartmental models used to study epidemic spreading often assume the same susceptibility for all individuals and are therefore, agnostic about the effects that differences in susceptibility can have on epidemic spreading. Here we show that–for the SIS model–differential susceptibility can make networks more vulnerable to the spread of diseases when the correlation between a node's degree and susceptibility are positive and less vulnerable when this correlation is negative. Moreover, we show that networks become more likely to contain a pocket of infection when individuals are more likely to connect with others that have similar susceptibility (the network is segregated). These results show that the failure to include differential susceptibility to epidemic models can lead to a systematic over/under estimation of fundamental epidemic parameters when the structure of the networks is not independent from the susceptibility of the nodes or when there are correlations between the susceptibility of connected individuals
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BibTeX
@article{smilkov2026beyond,
title = {{Beyond network structure: How heterogeneous susceptibility modulates the spread of epidemics}},
author = {Daniel Smilkov and Cesar A Hidalgo and Ljupco Kocarev},
journal = {Scientific Reports},
year = {2026},
month = nov,
doi = {10.1038/srep04795},
url = {https://hal.science/hal-05852232v1},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Beyond network structure: How heterogeneous susceptibility modulates the spread of epidemics
AU - Daniel Smilkov
AU - Cesar A Hidalgo
AU - Ljupco Kocarev
PY - 2026
DA - 2026/11/04
JO - Scientific Reports
DO - 10.1038/srep04795
UR - https://hal.science/hal-05852232v1
AB - The compartmental models used to study epidemic spreading often assume the same susceptibility for all individuals and are therefore, agnostic about the effects that differences in susceptibility can have on epidemic spreading. Here we show that–for the SIS model–differential susceptibility can make networks more vulnerable to the spread of diseases when the correlation between a node's degree and susceptibility are positive and less vulnerable when this correlation is negative. Moreover, we show that networks become more likely to contain a pocket of infection when individuals are more likely to connect with others that have similar susceptibility (the network is segregated). These results show that the failure to include differential susceptibility to epidemic models can lead to a systematic over/under estimation of fundamental epidemic parameters when the structure of the networks is not independent from the susceptibility of the nodes or when there are correlations between the susceptibility of connected individuals
SN - 2045-2322
ER -