Characterizing the Initial Phase of Epidemic Growth on some Empirical Networks
Kristoffer Spricer, Pieter Trapman

TL;DR
This study examines the initial exponential growth phase of epidemics on empirical networks through simulations, confirming exponential growth generally holds except in low-dimensional spatially constrained networks.
Contribution
It provides empirical evidence on the validity of exponential growth assumptions in epidemic modeling across various network structures.
Findings
Exponential growth characterizes early epidemic stages on most empirical networks.
Low-dimensional spatial constraints, like 2D lattices, deviate from exponential growth.
High-dimensional lattices exhibit exponential early epidemic growth.
Abstract
A key parameter in models for the spread of infectious diseases is the basic reproduction number , which is the expected number of secondary cases a typical infected primary case infects during its infectious period in a large mostly susceptible population. In order for this quantity to be meaningful, the initial expected growth of the number of infectious individuals in the large-population limit should be exponential. We investigate to what extent this assumption is valid by performing repeated simulations of epidemics on selected empirical networks, viewing each epidemic as a random process in discrete time. The initial phase of each epidemic is analyzed by fitting the number of infected people at each time step to a generalised growth model, allowing for estimating the shape of the growth. For reference, similar investigations are done on some elementary graphs such as…
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