We study the epidemic spreading on spatial networks where the probability that two nodes are connected decays with their distance as a power law. As the exponent of the distance dependence grows, the resulting networks smoothly transition from the random network limit to the regular lattice limit. We show that despite keeping the average number of contacts constant, the increasing exponent hampers the epidemic spreading as it makes long-distance connections less frequent. The growth of the number of infections is also influenced by the exponent value and changes from exponential growth to power-law growth. The resulting growth is compatible with recent analyses of the COVID-19 spreading in most countries.
We study the epidemic spreading on spatial networks where the probability that two nodes are connected decays with their distance as a power law. As the exponent of the distance dependence grows, the model networks smoothly transition from the random network limit to the regular lattice limit. We show that despite keeping the average number of contacts constant, the increasing exponent hampers the epidemic spreading by making long-distance connections less frequent. The spreading dynamics is also influenced by the exponent value and changes from exponential growth to power-law growth. The observed power-law growth is compatible with recent analyses of the COVID-19 spreading in numerous countries.
R
resulting
model
R
as it makes
by making
R
growth of the number of infections
spreading dynamics
R
resulting
observed power-law
R
most
numerous