TY - JOUR
T1 - Infection Percolation
T2 - A Dynamic Network Model of Disease Spreading
AU - Browne, Christopher A.
AU - Amchin, Daniel B.
AU - Schneider, Joanna
AU - Datta, Sujit S.
N1 - Funding Information:
This material is based upon work partially supported by the National Science Foundation Graduate Research Fellowship Program (to CB) under Grant No. DGE1656466. This publication
Funding Information:
It is a pleasure to acknowledge Navid C.P.D. Bizmark for stimulating discussions. Funding. This material is based upon work partially supported by the National Science Foundation Graduate Research Fellowship Program (to CB) under Grant No. DGE1656466. This publication was supported by the Princeton University Library Open Access Fund.
Publisher Copyright:
© Copyright © 2021 Browne, Amchin, Schneider and Datta.
PY - 2021/4/16
Y1 - 2021/4/16
N2 - Models of disease spreading are critical for predicting infection growth in a population and evaluating public health policies. However, standard models typically represent the dynamics of disease transmission between individuals using macroscopic parameters that do not accurately represent person-to-person variability. To address this issue, we present a dynamic network model that provides a straightforward way to incorporate both disease transmission dynamics at the individual scale as well as the full spatiotemporal history of infection at the population scale. We find that disease spreads through a social network as a traveling wave of infection, followed by a traveling wave of recovery, with the onset and dynamics of spreading determined by the interplay between disease transmission and recovery. We use these insights to develop a scaling theory that predicts the dynamics of infection for diverse diseases and populations. Furthermore, we show how spatial heterogeneities in susceptibility to infection can either exacerbate or quell the spread of disease, depending on its infectivity. Ultimately, our dynamic network approach provides a simple way to model disease spreading that unifies previous findings and can be generalized to diverse diseases, containment strategies, seasonal conditions, and community structures.
AB - Models of disease spreading are critical for predicting infection growth in a population and evaluating public health policies. However, standard models typically represent the dynamics of disease transmission between individuals using macroscopic parameters that do not accurately represent person-to-person variability. To address this issue, we present a dynamic network model that provides a straightforward way to incorporate both disease transmission dynamics at the individual scale as well as the full spatiotemporal history of infection at the population scale. We find that disease spreads through a social network as a traveling wave of infection, followed by a traveling wave of recovery, with the onset and dynamics of spreading determined by the interplay between disease transmission and recovery. We use these insights to develop a scaling theory that predicts the dynamics of infection for diverse diseases and populations. Furthermore, we show how spatial heterogeneities in susceptibility to infection can either exacerbate or quell the spread of disease, depending on its infectivity. Ultimately, our dynamic network approach provides a simple way to model disease spreading that unifies previous findings and can be generalized to diverse diseases, containment strategies, seasonal conditions, and community structures.
KW - epidemic
KW - network model
KW - percolation
KW - scaling
KW - susceptible-infected-recovered model
UR - http://www.scopus.com/inward/record.url?scp=85104933833&partnerID=8YFLogxK
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U2 - 10.3389/fphy.2021.645954
DO - 10.3389/fphy.2021.645954
M3 - Article
AN - SCOPUS:85104933833
SN - 2296-424X
VL - 9
JO - Frontiers in Physics
JF - Frontiers in Physics
M1 - 645954
ER -