The Interplay between Determinism and Stochasticity in Childhood Diseases

Rohani,; Keeling,; Grenfell,

doi:10.2307/3078907

Cited by 56 publications

(75 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our results indicate that seasonality in transmission is an important driver of intraannual variation in weekly incidence. The no interaction model (null model) cannot explain the pattern of HPIV serotypes in our data, although the seasonally forced SIR model does exhibit biennial oscillation in a different parameter regime (such as that governing the dynamics of measles) (47).…”

Section: Discussionmentioning

confidence: 66%

Cross-immunity between strains explains the dynamical pattern of paramyxoviruses

Bhattacharyya

Gesteland

Korgenski

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

Viral respiratory tract diseases pose serious public health problems. Our ability to predict and thus, be able to prepare for outbreaks is strained by the complex factors driving the prevalence and severity of these diseases. The abundance of diseases and transmission dynamics of strains are not only affected by external factors, such as weather, but also driven by interactions among viruses mediated by human behavior and immunity. To untangle the complex out-ofphase annual and biennial pattern of three common paramyxoviruses, Respiratory Syncytial Virus (RSV), Human Parainfluenza Virus (HPIV), and Human Metapneumovirus (hMPV), we adopt a theoretical approach that integrates ecological and immunological mechanisms of disease interactions. By estimating parameters from multiyear time series of laboratory-confirmed cases from the intermountain west region of the United States and using statistical inference, we show that models of immune-mediated interactions better explain the data than those based on ecological competition by convalescence. The strength of cross-protective immunity among viruses is correlated with their genetic distance in the phylogenetic tree of the paramyxovirus family.paramyxoviruses | respiratory syncytial viruses | human parainfluenza | pathogen interactions | cross-immunity

show abstract

Section: Discussionmentioning

confidence: 66%

Cross-immunity between strains explains the dynamical pattern of paramyxoviruses

Bhattacharyya

Gesteland

Korgenski

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

show abstract

“…Individuality, and the associated stochastic integer-based approach, has long been understood to be epidemiologically important, especially when dealing with small infectious populations and matters of persistence (30,31). However, here we have extended this notion to show that we must not only recognize the individual-based nature of the population but also maintain the identity of individuals, especially when behavior (such as movements) is highly structured.…”

Section: Discussionmentioning

confidence: 98%

Individual identity and movement networks for disease metapopulations

Keeling

Danon

Vernon

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

138

151

View full text Add to dashboard Cite

The theory of networks has had a huge impact in both the physical and life sciences, shaping our understanding of the interaction between multiple elements in complex systems. In particular, networks have been extensively used in predicting the spread of infectious diseases where individuals, or populations of individuals, interact with a limited set of others-defining the network through which the disease can spread. Here for such disease models we consider three assumptions for capturing the network of movements between populations, and focus on two applied problems supported by detailed data from Great Britain: the commuter movement of workers between local areas (wards) and the permanent movement of cattle between farms. For such metapopulation networks, we show that the identity of individuals responsible for making network connections can have a significant impact on the infection dynamics, with clear implications for detailed public health and veterinary applications.etworks are now a well-understood and powerful scientific tool. When the number of interactions between elements or individuals is relatively low, then networks offer an intuitive means of capturing and describing the structure of such interactions. Examples abound, from computer and Internet connections (1) to metabolic networks (2) and food webs (3), from transportation patterns (4) to actors within the same movies (5)-in each of these, the theory of networks has provided valuable insights into how such interactions are structured and has hinted at deeper underlying patterns. When interactions describe connections between people, then network theory is often used to explore the implications of disease spread through the population (6). However, data on human-to-human contacts through which infections can spread are rare, generally being isolated to small populations that have been sampled with detailed questionnaires-of these, networks of sexual encounters, such as the studies in Colorado Springs (7) and Manitoba (8), are probably the best examples. Another common source of network interaction comes from the movement of individuals between populations, who could potentially carry infection with them. With concerns over novel or reemerging infections, such networks of movements often form the core of mathematical models that examine the spatiotemporal spread of infections and the associated public health implications. Examples include work movements for the spread of seasonal influenza in the United States (9, 10); aviation traffic for the spread of smallpox (11), sudden acute respiratory syndrome (12), and general epidemics (13); commuting movements for deliberate release of smallpox (14); and trade for the 1918-1919 influenza pandemic (15); a more systematic review of techniques and applications is available from Riley (16). The sensitivity of the epidemic to the topological structure of the movement network has already been shown (13, 17); in contrast here, through detailed simulation, we address how such movement networks should be model...

show abstract

“…Ideally, one should seek a stochastic model that matches the observed behaviour at a range of population sizes, birth rates and vaccination levels. For diseases such as whooping cough, where stochasticity is fundamental to the observed dynamics Rohani et al 2001), such a comprehensive search may be vital, although computationally intensive.…”

Section: Discussionmentioning

confidence: 99%

Understanding the persistence of measles: reconciling theory, simulation and observation

Keeling

Grenfell

2002

Proc. R. Soc. Lond. B

135

166

View full text Add to dashboard Cite

Ever since the pattern of localized extinction associated with measles was discovered by Bartlett in 1957, many models have been developed in an attempt to reproduce this phenomenon. Recently, the use of constant infectious and incubation periods, rather than the more convenient exponential forms, has been presented as a simple means of obtaining realistic persistence levels. However, this result appears at odds with rigorous mathematical theory; here we reconcile these differences. Using a deterministic approach, we parameterize a variety of models to fit the observed biennial attractor, thus determining the level of seasonality by the choice of model. We can then compare fairly the persistence of the stochastic versions of these models, using the 'best-fit' parameters. Finally, we consider the differences between the observed fade-out pattern and the more theoretically appealing 'first passage time'.

show abstract

The Interplay between Determinism and Stochasticity in Childhood Diseases

Cited by 56 publications

References 0 publications

Cross-immunity between strains explains the dynamical pattern of paramyxoviruses

Cross-immunity between strains explains the dynamical pattern of paramyxoviruses

Individual identity and movement networks for disease metapopulations

Understanding the persistence of measles: reconciling theory, simulation and observation

Contact Info

Product

Resources

About