On the identification of superspreaders for infectious disease

Kemper, John T.

doi:10.1016/0025-5564(80)90018-8

Cited by 40 publications

(36 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Section 5), it has been thoroughly established that targeted vaccination towards superspreaders can significantly reduce the spread of disease [275,291,344,371,372]. However, the assumption that superspreaders (who typically have many connections and are responsible for the majority of secondary infections [564][565][566][567][568]) always accept vaccination does not capture the realistic transmission dynamics with behavioral response, especially for seasonal influenza-like vaccination. In this regard, Wells et al analyzed how behavioral feedback affects the effectiveness of vaccination strategies [435].…”

Section: How Does Feedback Influence Well-known Passive and Pro-activmentioning

confidence: 99%

Statistical physics of vaccination

et al. 2016

View full text Add to dashboard Cite

Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination-one of the most important preventive measures of modern times-is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research.

show abstract

Section: How Does Feedback Influence Well-known Passive and Pro-activmentioning

confidence: 99%

Statistical physics of vaccination

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Work is under way to explore the consequences, at the population level, of introducing more complex interaction rules at the individual level, where those population-level consequences cannot be predicted with confidence in advance. For example, transmission within spatially heterogeneous populations, transmission with superspreaders (Kemper 1980) and transmission between subgroups generally. It may be expected that as more biological detail is added to the individual level descriptions of the system, the resulting equations will become more complex than the simple equations of the 'Models' section.…”

Section: Resultsmentioning

confidence: 99%

A rigorous approach to investigating common assumptions about disease transmission

et al. 2010

View full text Add to dashboard Cite

“…Kemper [11] proposed an ODE model of a system featuring superspreaders. By removing the terms for birth and death from (2) the following transformation of our parameters:…”

Section: Resultsmentioning

confidence: 99%

“…Superspreaders are infectious individuals who are somehow responsible for more infections in the population than average [11,8,15,16,7,25]. The 80:20 rule is often cited in this regard, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…Does having superspreaders in a population affect the overall epidemiological dynamics, in particular the form of the transmission term? Models of epidemics featuring superspreaders have been addressed to some extent by, for instance, Kemper [11], Lloyd-Smith et al [15], Fujie and Odagaki [7], but here we are rigorously deriving the population level behaviour from individual interactions. Using individual based modelling, we can express the distinctive behaviour of supershedders and supercontacters.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Symbolic Investigation of Superspreaders

et al. 2010

View full text Add to dashboard Cite

Abstract. Superspreaders are an important phenomenon in the spread of infectious disease, accounting for a higher than average number of new infections in the population. We use mathematical models to compare the impact of supershedders and supercontacters on population dynamics.The stochastic, individual based models are investigated by conversion to deterministic, population level Mean Field Equations, using process algebra. The mean emergent population dynamics of the models are shown to be equivalent with and without superspreaders; however, simulations confirm expectations of differences in variability, having implications for individual epidemics.

show abstract

On the identification of superspreaders for infectious disease

Cited by 40 publications

References 7 publications

Statistical physics of vaccination

Statistical physics of vaccination

A rigorous approach to investigating common assumptions about disease transmission

A Symbolic Investigation of Superspreaders

Contact Info

Product

Resources

About