| 1. |
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| 2. |
- Abril, Jazmine, et al.
(författare)
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Associations between pregnancy-related factors and birth characteristics with risk of rare uterine cancer subtypes : a Nordic population-based case-control study
- 2024
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Ingår i: Cancer Causes and Control. - : Springer. - 0957-5243 .- 1573-7225. ; 35:5, s. 741-747
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Uterine sarcomas are a rare group of uterine malignancies. Due to the low incidence and changes in uterine sarcoma classification, risk factors are not well characterized. Our objective was to evaluate risk factors for uterine sarcoma and compare risk factors between uterine sarcoma, malignant mixed Mullerian tumors (MMMTs), and type I endometrial carcinomas.Methods: This nested case-control study utilized linked data from population-based medical birth and cancer registries in Denmark, Finland, Norway, and Sweden. Up to 10 controls were matched on country and birth year for each uterine cancer case. Using multivariable adjusted multinomial logistic regression, estimates of the associations between pregnancy-related factors and risk of uterine sarcoma, MMMTs, and type I endometrial carcinomas were determined.Results: Having a very-low-birth-weight infant (< 1500 vs. 2500-3999 g: OR [95% CI] 2.83 [1.61-4.96]) was associated with an increased risk of uterine sarcoma. Whereas, having a more recent pregnancy was associated with reduced risks of MMMT (< 10 vs. >= 30 years: 0.66 [0.20-2.23]) and type 1 endometrial carcinomas (0.35 [0.30-0.41]) but not uterine sarcomas (1.33 [0.90-1.98], p-heterogeneity < 0.01).Conclusion: Our study provides evidence that risk factors for uterine sarcoma and MMMT, previously grouped with uterine sarcomas, vary substantially. Additionally, MMMT and type I endometrial carcinomas are more similar than uterine sarcoma in that pregnancy complications like gestational hypertension and preeclampsia were associated with reduced risks of both but not uterine sarcoma, suggesting different etiologies.
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| 3. |
- Alm, Sven Erick, et al.
(författare)
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Stokastik
- 2008
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Bok (populärvet., debatt m.m.)
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| 4. |
- Arner, Erik, et al.
(författare)
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Adipocyte Turnover : Relevance to Human Adipose Tissue Morphology
- 2010
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Ingår i: Diabetes. - : American Diabetes Association. - 0012-1797 .- 1939-327X. ; 59:1, s. 105-109
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE-Adipose tissue may contain few large adipocytes (hypertrophy) or many small adipocytes (hyperplasia). We investigated factors of putative importance for adipose tissue morphology. RESEARCH DESIGN AND METHODS-Subcutaneous adipocyte size and total fat mass were compared in 764 subjects with BMI 18-60 kg/m(2). A morphology value was defined as tire difference between the measured adipocyte volume and the expected volume given by a curved-line fit for a given body fat mass and was related to insulin values. In 35 subjects, in vivo adipocyte turnover was measured by exploiting incorporation of atmospheric C-14 into DNA. RESULTS-Occurrence of hyperplasia (negative morphology value) or hypertrophy (positive morphology value) was independent of sex and body weight but con-elated with fasting plasma insulin levels and insulin sensitivity, independent of adipocyte volume (beta-coefficient = 0.3, P < 0.0001). Total adipocyte number and morphology were negatively related (r = -0.66); i.e., the total adipocyte number was greatest in pronounced hyperplasia and smallest in pronounced hypertrophy. The absolute number of new adipocytes generated each year was 70% lower (P < 0.001) in hypertrophy than in hyperplasia, and individual values for adipocyte generation and morphology were strongly related (r = 0.7, P < 0.001). The relative death rate (similar to 10% per year) or mean age of adipocytes (similar to 10 years) was not correlated with morphology. CONCLUSIONS-Adipose tissue morphology correlates with insulin measures and is linked to the total adipocyte number independently of sex and body fat level. Low generation rates of adipocytes associate with adipose tissue hypertrophy, whereas high generation rates associate with adipose hyperplasia. Diabetes 59:105-109, 2010
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| 5. |
- Ball, Frank, et al.
(författare)
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A network with tunable clustering, degree correlation and degree distribution, and an epidemic thereon
- 2013
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Ingår i: Journal of Mathematical Biology. - : Springer Science and Business Media LLC. - 0303-6812 .- 1432-1416. ; 66:4-5, s. 979-1019
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Tidskriftsartikel (refereegranskat)abstract
- A random network model which allows for tunable, quite general forms of clustering, degree correlation and degree distribution is defined. The model is an extension of the configuration model, in which stubs (half-edges) are paired to form a network. Clustering is obtained by forming small completely connected subgroups, and positive (negative) degree correlation is obtained by connecting a fraction of the stubs with stubs of similar (dissimilar) degree. An SIR (Susceptible Infective Recovered) epidemic model is defined on this network. Asymptotic properties of both the network and the epidemic, as the population size tends to infinity, are derived: the degree distribution, degree correlation and clustering coefficient, as well as a reproduction number , the probability of a major outbreak and the relative size of such an outbreak. The theory is illustrated by Monte Carlo simulations and numerical examples. The main findings are that (1) clustering tends to decrease the spread of disease, (2) the effect of degree correlation is appreciably greater when the disease is close to threshold than when it is well above threshold and (3) disease spread broadly increases with degree correlation when is just above its threshold value of one and decreases with when is well above one.
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| 6. |
- Ball, Frank, et al.
(författare)
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A stochastic SIR network epidemic model with preventive dropping of edges
- 2019
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Ingår i: Journal of Mathematical Biology. - : Springer Science and Business Media LLC. - 0303-6812 .- 1432-1416. ; 78:6, s. 1875-1951
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Tidskriftsartikel (refereegranskat)abstract
- A Markovian Susceptible Infectious Recovered (SIR) model is considered for the spread of an epidemic on a configuration model network, in which susceptible individuals may take preventive measures by dropping edges to infectious neighbours. An effective degree formulation of the model is used in conjunction with the theory of density dependent population processes to obtain a law of large numbers and a functional central limit theorem for the epidemic as the population size N, assuming that the degrees of individuals are bounded. A central limit theorem is conjectured for the final size of the epidemic. The results are obtained for both the Molloy-Reed (in which the degrees of individuals are deterministic) and Newman-Strogatz-Watts (in which the degrees of individuals are independent and identically distributed) versions of the configuration model. The two versions yield the same limiting deterministic model but the asymptotic variances in the central limit theorems are greater in the Newman-Strogatz-Watts version. The basic reproduction number R0 and the process of susceptible individuals in the limiting deterministic model, for the model with dropping of edges, are the same as for a corresponding SIR model without dropping of edges but an increased recovery rate, though, when R0>1, the probability of a major outbreak is greater in the model with dropping of edges. The results are specialised to the model without dropping of edges to yield conjectured central limit theorems for the final size of Markovian SIR epidemics on configuration-model networks, and for the size of the giant components of those networks. The theory is illustrated by numerical studies, which demonstrate that the asymptotic approximations are good, even for moderate N.
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| 7. |
- Ball, Frank, et al.
(författare)
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AN EPIDEMIC IN A DYNAMIC POPULATION WITH IMPORTATION OF INFECTIVES
- 2017
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Ingår i: The Annals of Applied Probability. - 1050-5164 .- 2168-8737. ; 27:1, s. 242-274
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Tidskriftsartikel (refereegranskat)abstract
- Consider a large uniformly mixing dynamic population, which has constant birth rate and exponentially distributed lifetimes, with mean population size n. A Markovian SIR (susceptible -> infective -> recovered) infectious disease, having importation of infectives, taking place in this population is analysed. The main situation treated is where n -> infinity, keeping the basic reproduction number R-0 as well as the importation rate of infectives fixed, but assuming that the quotient of the average infectious period and the average lifetime tends to 0 faster than 1/log n. It is shown that, as n -> infinity, the behaviour of the 3-dimensional process describing the evolution of the fraction of the population that are susceptible, infective and recovered, is encapsulated in a 1-dimensional regenerative process S = {S(t); t >= 0} describing the limiting fraction of the population that are susceptible. The process S grows deterministically, except at one random time point per regenerative cycle, where it jumps down by a size that is completely determined by the waiting time since the start of the regenerative cycle. Properties of the process S, including the jump size and stationary distributions, are determined.
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| 8. |
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| 9. |
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| 10. |
- Ball, Frank, et al.
(författare)
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Epidemics on networks with preventive rewiring
- 2022
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Ingår i: Random structures & algorithms (Print). - : Wiley. - 1042-9832 .- 1098-2418. ; 61:2, s. 250-297
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Tidskriftsartikel (refereegranskat)abstract
- A stochastic SIR (susceptible infective recovered) model is considered for the spread of an epidemic on a network, described initially by an Erdős–Rényi random graph, in which susceptible individuals connected to infectious neighbors may drop or rewire such connections. A novel construction of the model is used to derive a deterministic model for epidemics started with a positive fraction initially infected and prove convergence of the scaled stochastic model to that deterministic model as the population size . For epidemics initiated by a single infective that take off, we prove that for part of the parameter space, in the limit as , the final fraction infected is discontinuous in the infection rate at its threshold , thus not converging to 0 as . The discontinuity is particularly striking when rewiring is necessarily to susceptible individuals in that jumps from 0 to 1 as passes through .
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| 11. |
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| 12. |
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| 13. |
- Ball, Frank, et al.
(författare)
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Household epidemic models with varying infection response
- 2011
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Ingår i: Journal of Mathematical Biology. - : Springer Science and Business Media LLC. - 0303-6812 .- 1432-1416. ; 63:2, s. 309-337
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Tidskriftsartikel (refereegranskat)abstract
- This paper is concerned with SIR (susceptible -> infected -> removed) household epidemic models in which the infection response may be either mild or severe, with the type of response also affecting the infectiousness of an individual. Two different models are analysed. In the first model, the infection status of an individual is predetermined, perhaps due to partial immunity, and in the second, the infection status of an individual depends on the infection status of its infector and on whether the individual was infected by a within- or between-household contact. The first scenario may be modelled using a multitype household epidemic model, and the second scenario by a model we denote by the infector-dependent-severity household epidemic model. Large population results of the two models are derived, with the focus being on the distribution of the total numbers of mild and severe cases in a typical household, of any given size, in the event that the epidemic becomes established. The aim of the paper is to investigate whether it is possible to determine which of the two underlying explanations is causing the varying response when given final size household outbreak data containing mild and severe cases. We conduct numerical studies which show that, given data on sufficiently many households, it is generally possible to discriminate between the two models by comparing the Kullback-Leibler divergence for the two fitted models to these data.
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| 14. |
- Ball, Frank, et al.
(författare)
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ON EXPECTED DURATIONS OF BIRTH-DEATH PROCESSES, WITH APPLICATIONS TO BRANCHING PROCESSES AND SIS EPIDEMICS
- 2016
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Ingår i: Journal of Applied Probability. - : Cambridge University Press (CUP). - 0021-9002 .- 1475-6072. ; 53:1, s. 203-215
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Tidskriftsartikel (refereegranskat)abstract
- We study continuous-time birth-death type processes, where individuals have independent and identically distributed lifetimes, according to a random variable Q, with E[Q] = 1, and where the birth rate if the population is currently in state (has size) n is alpha(n). We focus on two important examples, namely alpha(n) = lambda n being a branching process, and alpha(n) = lambda n(N-n)/N which corresponds to an SIS (susceptible -> infective -> susceptible) epidemic model in a homogeneously mixing community of fixed size N. The processes are assumed to start with a single individual, i. e. in state 1. Let T, A(n), C, and S denote the (random) time to extinction, the total time spent in state n, the total number of individuals ever alive, and the sum of the lifetimes of all individuals in the birth-death process, respectively. We give expressions for the expectation of all these quantities and show that these expectations are insensitive to the distribution of Q. We also derive an asymptotic expression for the expected time to extinction of the SIS epidemic, but now starting at the endemic state, which is not independent of the distribution of Q. The results are also applied to the household SIS epidemic, showing that, in contrast to the household SIR (susceptible -> infective -> recovered) epidemic, its threshold parameter R-* is insensitive to the distribution of Q.
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| 15. |
- Ball, Frank, et al.
(författare)
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Seven challenges for metapopulation models of epidemics, including households models
- 2015
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Ingår i: Epidemics. - : Elsevier BV. - 1755-4365 .- 1878-0067. ; 10, s. 63-67
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Tidskriftsartikel (refereegranskat)abstract
- This paper considers metapopulation models in the general sense, i.e. where the population is partitioned into sub-populations (groups, patches,...), irrespective of the biological interpretation they have, e.g. spatially segregated large sub-populations, small households or hosts themselves modelled as populations of pathogens. This framework has traditionally provided an attractive approach to incorporating more realistic contact structure into epidemic models, since it often preserves analytic tractability (in stochastic as well as deterministic models) but also captures the most salient structural inhomogeneity in contact patterns in many applied contexts. Despite the progress that has been made in both the theory and application of such metapopulation models, we present here several major challenges that remain for future work, focusing on models that, in contrast to agent-based ones, are amenable to mathematical analysis. The challenges range from clarifying the usefulness of systems of weakly-coupled large sub-populations in modelling the spread of specific diseases to developing a theory for endemic models with household structure. They include also developing inferential methods for data on the emerging phase of epidemics, extending metapopulation models to more complex forms of human social structure, developing metapopulation models to reflect spatial population structure, developing computationally efficient methods for calculating key epidemiological model quantities, and integrating within- and between-host dynamics in models.
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| 16. |
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| 17. |
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| 18. |
- Bergström, Fanny, 1988-, et al.
(författare)
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Bayesian nowcasting with leading indicators applied to COVID-19 fatalities in Sweden
- 2022
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Ingår i: PloS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 18:12
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Tidskriftsartikel (refereegranskat)abstract
- The real-time analysis of infectious disease surveillance data is essential in obtaining situational awareness about the current dynamics of a major public health event such as the COVID-19 pandemic. This analysis of e.g., time-series of reported cases or fatalities is complicated by reporting delays that lead to under-reporting of the complete number of events for the most recent time points. This can lead to misconceptions by the interpreter, for instance the media or the public, as was the case with the time-series of reported fatalities during the COVID-19 pandemic in Sweden. Nowcasting methods provide real-time estimates of the complete number of events using the incomplete time-series of currently reported events and information about the reporting delays from the past. In this paper we propose a novel Bayesian nowcasting approach applied to COVID-19-related fatalities in Sweden. We incorporate additional information in the form of time-series of number of reported cases and ICU admissions as leading signals. We demonstrate with a retrospective evaluation that the inclusion of ICU admissions as a leading signal improved the nowcasting performance of case fatalities for COVID-19 in Sweden compared to existing methods.
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| 19. |
- Björnberg, Jakob E., et al.
(författare)
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A Stochastic Model for Virus Growth in a Cell Population
- 2014
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Ingår i: Journal of Applied Probability. - : Cambridge University Press (CUP). - 0021-9002 .- 1475-6072. ; 51:3, s. 599-612
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Tidskriftsartikel (refereegranskat)abstract
- In this work we introduce a stochastic model for the spread of a virus in a cell population where the virus has two ways of spreading: either by allowing its host cell to live and duplicate, or by multiplying in large numbers within the host cell, causing the host cell to burst and thereby let the virus enter new uninfected cells. The model is a kind of interacting Markov branching process. We focus in particular on the probability that the virus population survives and how this depends on a certain parameter A which quantifies the 'aggressiveness' of the virus. Our main goal is to determine the optimal balance between aggressive growth and long-term success. Our analysis shows that the optimal strategy of the virus (in terms of survival) is obtained when the virus has no effect on the host cell's life cycle, corresponding to lambda = 0. This is in agreement with experimental data about real viruses.
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| 20. |
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| 21. |
- Britton, Tom, et al.
(författare)
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A dynamic network in a dynamic population: asymptotic properties
- 2011
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Ingår i: Journal of Applied Probability. - : Cambridge University Press (CUP). - 1475-6072 .- 0021-9002. ; 48:4, s. 1163-1178
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Tidskriftsartikel (refereegranskat)abstract
- We derive asymptotic properties for a stochastic dynamic network model in a stochastic dynamic population. In the model, nodes give birth to new nodes until they die, each node being equipped with a social index given at birth. During the life of a node it creates edges to other nodes, nodes with high social index at higher rate, and edges disappear randomly in time. For this model, we derive a criterion for when a giant connected component exists after the process has evolved for a long period of time, assuming that the node population grows to infinity. We also obtain an explicit expression for the degree correlation rho (of neighbouring nodes) which shows that rho is always positive irrespective of parameter values in one of the two treated submodels, and may be either positive or negative in the other model, depending on the parameters.
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| 22. |
- Britton, Tom, et al.
(författare)
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A General Two-Level Mixing Model
- 2019
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Ingår i: Stochastic Epidemic Models with Inference. - Cham : Springer. - 9783030308995 - 9783030309008 ; , s. 159-213
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Bokkapitel (refereegranskat)abstract
- In the households model analysed in Chapter 2, infectives make two types of infectious contacts: local contacts with individuals in their household and global contacts with individuals chosen uniformly at random from the whole population. This is an example of an epidemic in a population with two levels of mixing.
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| 23. |
- Britton, Tom, et al.
(författare)
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A mathematical model reveals the influence of population heterogeneity on herd immunity to SARS-CoV-2
- 2020
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Ingår i: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 369:6505, s. 846-849
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Tidskriftsartikel (refereegranskat)abstract
- Despite various levels of preventive measures, in 2020, many countries have suffered severely from the coronavirus 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Using a model, we show that population heterogeneity can affect disease-induced immunity considerably because the proportion of infected individuals in groups with the highest contact rates is greater than that in groups with low contact rates. We estimate that if R-0 = 2.5 in an age-structured community with mixing rates fitted to social activity, then the disease-induced herd immunity level can be similar to 43%, which is substantially less than the classical herd immunity level of 60% obtained through homogeneous immunization of the population. Our estimates should be interpreted as an illustration of how population heterogeneity affects herd immunity rather than as an exact value or even a best estimate.
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| 24. |
- Britton, Tom, et al.
(författare)
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A Network Epidemic Model with Preventive Rewiring : Comparative Analysis of the Initial Phase
- 2016
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Ingår i: Bulletin of Mathematical Biology. - : Springer Science and Business Media LLC. - 0092-8240 .- 1522-9602. ; 78:12, s. 2427-2454
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Tidskriftsartikel (refereegranskat)abstract
- This paper is concerned with stochastic SIR and SEIR epidemic models on random networks in which individuals may rewire away from infected neighbors at some rate (and reconnect to non-infectious individuals with probability or else simply drop the edge if ), so-called preventive rewiring. The models are denoted SIR- and SEIR-, and we focus attention on the early stages of an outbreak, where we derive the expressions for the basic reproduction number and the expected degree of the infectious nodes using two different approximation approaches. The first approach approximates the early spread of an epidemic by a branching process, whereas the second one uses pair approximation. The expressions are compared with the corresponding empirical means obtained from stochastic simulations of SIR- and SEIR- epidemics on Poisson and scale-free networks. Without rewiring of exposed nodes, the two approaches predict the same epidemic threshold and the same for both types of epidemics, the latter being very close to the mean degree obtained from simulated epidemics over Poisson networks. Above the epidemic threshold, pairwise models overestimate the value of computed from simulations, which turns out to be very close to the one predicted by the branching process approximation. When exposed individuals also rewire with (perhaps unaware of being infected), the two approaches give different epidemic thresholds, with the branching process approximation being more in agreement with simulations.
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| 25. |
- Britton, Tom, et al.
(författare)
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A Spatial Epidemic Model with Site Contamination
- 2018
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Ingår i: Markov Processes and Related Fields. - 1024-2953. ; 24:1, s. 25-38
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Tidskriftsartikel (refereegranskat)abstract
- We introduce the effect of site contamination in a model for spatial epidemic spread and show that the presence of site contamination may have a strict effect on the model in the sense that it can make an otherwise subcritical process supercritical. Each site on Z(d) is independently assigned a random number of particles and these then perform random walks restricted to bounded regions around their home locations. At time 0, the origin is infected along with all its particles. The infection then spread in that an infected particle that jumps to a new site causes the site along with all particles located there to be infected. Also, a healthy particle that jumps to a site where infection is present, either in that the site is infected or in the presence of infected particles, becomes infected. Particles and sites recover at rate lambda and gamma, respectively, and then become susceptible to the infection again. We show that, for each given value of lambda, there is a positive probability that the infection survives indefinitely if gamma is sufficiently small, and that, for each given value of gamma, the infection dies out almost surely if lambda is large enough. Several open problems and modifications of the model are discussed, and some natural conjectures are supported by simulations.
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