Kristoffer Spricer
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Kristoffer Spricer has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
Tom Britton
Department of Mathematics, Stockholm University, 106 91 Stockholm, Sweden
3 shared publicationsPieter Trapman
Stockholm University
2 shared publications
Latest Publications
- Open Access
- 26 December 2019
- Published by Cambridge University Press (CUP) in Network Science
- Vol. 7 (4) , 556-580
An SIR epidemic on a weighted network
We introduce a weighted configuration model graph, whereedge weightscorrespond to the probability of infection in an epidemic on the graph. On these graphs, we study the development of a Susceptible–Infectious–Recovered epidemic using both Reed–Frost and Markovian settings. For the special case of having two different edge types, we determine thebasic reproduction numberR0, theprobability of a major outbreak, and therelative final size of a major outbreak. Results are compared with those for a calibrated unweighted graph. The degree distributions are based on both theoretical constructs and empirical network data. In addition, bivariate standard normal copulas are used to model the dependence between the degrees of the two edge types, allowing for modeling the correlation between edge types over a wide range. Among the results are that the weighted graph produces much richer results than the unweighted graph. Also, whileR0always increases with increasing correlation between the two degrees, this is not necessarily true for the probability of a major outbreak nor for the relative final size of a major outbreak. When using copulas we see that these can produce results that are similar to those of the empirical degree distributions, indicating that in some cases a copula is a viable alternative to using the full empirical data.
- 26 December 2019
- Published by Cambridge University Press (CUP) in Network Science
- Vol. 7 (4) , 556-580
Open AccessAn SIR epidemic on a weighted network
We introduce a weighted configuration model graph, whereedge weightscorrespond to the probability of infection in an epidemic on the graph. On these graphs, we study the development of a Susceptible–Infectious–Recovered epidemic using both Reed–Frost and Markovian settings. For the special case of having two different edge types, we determine thebasic reproduction numberR0, theprobability of a major outbreak, and therelative final size of a major outbreak. Results are compared with those for a calibrated unweighted graph. The degree distributions are based on both theoretical constructs and empirical network data. In addition, bivariate standard normal copulas are used to model the dependence between the degrees of the two edge types, allowing for modeling the correlation between edge types over a wide range. Among the results are that the weighted graph produces much richer results than the unweighted graph. Also, whileR0always increases with increasing correlation between the two degrees, this is not necessarily true for the probability of a major outbreak nor for the relative final size of a major outbreak. When using copulas we see that these can produce results that are similar to those of the empirical degree distributions, indicating that in some cases a copula is a viable alternative to using the full empirical data.
- 5 December 2018
- Published by Springer Nature
- p. 315-334
Characterizing the Initial Phase of Epidemic Growth on Some Empirical Networks
- 5 December 2018
- Published by Springer Nature
- p. 315-334
Characterizing the Initial Phase of Epidemic Growth on Some Empirical Networks
- Open Access
Characterizing the Initial Phase of Epidemic Growth on some Empirical Networks
A key parameter in models for the spread of infectious diseases is the basic reproduction number $R_0$, 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 integer lattices in different dimensions and configuration model graphs, for which the early epidemic behaviour is known. We find that for the empirical networks tested in this paper, exponential growth characterizes the early stages of the epidemic, except when the network is restricted by a strong low-dimensional spacial constraint, such as is the case for the two-dimensional square lattice. However, on finite integer lattices of sufficiently high dimension, the early development of epidemics shows exponential growth.Open AccessCharacterizing the Initial Phase of Epidemic Growth on some Empirical Networks
A key parameter in models for the spread of infectious diseases is the basic reproduction number $R_0$, 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 integer lattices in different dimensions and configuration model graphs, for which the early epidemic behaviour is known. We find that for the empirical networks tested in this paper, exponential growth characterizes the early stages of the epidemic, except when the network is restricted by a strong low-dimensional spacial constraint, such as is the case for the two-dimensional square lattice. However, on finite integer lattices of sufficiently high dimension, the early development of epidemics shows exponential growth. - Open Access
- 7 September 2015
- Published by Springer Nature in Journal of Statistical Physics
- Vol. 161 (4) , 965-985
The Configuration Model for Partially Directed Graphs
- 7 September 2015
- Published by Springer Nature in Journal of Statistical Physics
- Vol. 161 (4) , 965-985
Open AccessThe Configuration Model for Partially Directed Graphs
- Open Access
The Configuration Model for Partially Directed Graphs
The configuration model was originally defined for undirected networks and has recently been extended to directed networks. Many empirical networks are however neither undirected nor completely directed, but instead usually partially directed meaning that certain edges are directed and others are undirected. In the paper we define a configuration model for such networks where nodes have in-, out-, and undirected degrees that may be dependent. We prove conditions under which the resulting degree distributions converge to the intended degree distributions. The new model is shown to better approximate several empirical networks compared to undirected and completely directed networks.
Open AccessThe Configuration Model for Partially Directed Graphs
The configuration model was originally defined for undirected networks and has recently been extended to directed networks. Many empirical networks are however neither undirected nor completely directed, but instead usually partially directed meaning that certain edges are directed and others are undirected. In the paper we define a configuration model for such networks where nodes have in-, out-, and undirected degrees that may be dependent. We prove conditions under which the resulting degree distributions converge to the intended degree distributions. The new model is shown to better approximate several empirical networks compared to undirected and completely directed networks.