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Journal ArticleDOI

Dynamics of measles epidemics: scaling noise, determinism, and predictability with the tsir model

01 May 2002-Ecological Monographs (Ecological Society of America)-Vol. 72, Iss: 2, pp 185-202
TL;DR: In this paper, the authors used the TSIR model to predict the long-term dynamics of measles and the balance between noise and determinism, as a function of population size.
Abstract: Two key linked questions in population dynamics are the relative importance of noise vs. density-dependent nonlinearities and the limits on temporal predictability of population abundance. We propose that childhood microparasitic infections, notably mea- sles, provide an unusually suitable empirical and theoretical test bed for addressing these issues. We base our analysis on a new mechanistic time series model for measles, the TSIR model, which captures the mechanistic essence of epidemic dynamics. The model, and parameter estimates based on short-term fits to prevaccination measles time series for 60 towns and cities in England and Wales, is introduced in a companion paper. Here, we explore how well the model predicts the long-term dynamics of measles and the balance between noise and determinism, as a function of population size. The TSIR model captures the basic dynamical features of the long-term pattern of measles epidemics in large cities remarkably well (based on time and frequency domain analyses). In particular, the model illustrates the impact of secular increases in birth rates, which cause a transition from biennial to annual dynamics. The model also captures the observed increase in epidemic irregularity with decreasing population size and the onset of local extinction below a critical community size. Decreased host population size is shown to be associated with an increased impact of demographic stochasticity. The interaction between nonlinearity and noise is explored using local Lyapunov exponents (LLE). These testify to the high level of stability of the biennial attractor in large cities. Irregularities are due to the limit cycle evolving with changing human birth rates and not due to complex dynamics. The geometry of the dynamics (sign and magnitude of the LLEs across phase space) is similar in the cities and the smaller urban areas. The qualitative difference in dynamics between small and large host communities is that demographic and extinction-recolonization stochasticities are much more influential in the former. The regional dynamics can therefore only be understood in terms of a core-satellite metapopulation structure for this host-enemy system. We also make a preliminary exploration of the model's ability to predict the dynamic consequences of measles vaccination.
Citations
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Journal ArticleDOI
TL;DR: Examples from human and wildlife disease systems are reviewed to illustrate the challenges inherent in understanding the mechanisms and impacts of seasonal environmental drivers, and to highlight general insights that are relevant to other ecological interactions.
Abstract: Seasonal variations in temperature, rainfall and resource availability are ubiquitous and can exert strong pressures on population dynamics. Infectious diseases provide some of the best-studied examples of the role of seasonality in shaping population fluctuations. In this paper, we review examples from human and wildlife disease systems to illustrate the challenges inherent in understanding the mechanisms and impacts of seasonal environmental drivers. Empirical evidence points to several biologically distinct mechanisms by which seasonality can impact host-pathogen interactions, including seasonal changes in host social behaviour and contact rates, variation in encounters with infective stages in the environment, annual pulses of host births and deaths and changes in host immune defences. Mathematical models and field observations show that the strength and mechanisms of seasonality can alter the spread and persistence of infectious diseases, and that population-level responses can range from simple annual cycles to more complex multiyear fluctuations. From an applied perspective, understanding the timing and causes of seasonality offers important insights into how parasite-host systems operate, how and when parasite control measures should be applied, and how disease risks will respond to anthropogenic climate change and altered patterns of seasonality. Finally, by focusing on well-studied examples of infectious diseases, we hope to highlight general insights that are relevant to other ecological interactions.

1,304 citations


Cites background from "Dynamics of measles epidemics: scal..."

  • ...This is vividly illustrated by the dynamics of measles, a communicable childhood disease that represents one of the most comprehensively studied data sets in population ecology (e.g. Bjørnstad et al. 2002; Grenfell et al. 2002; Fig....

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Journal ArticleDOI
16 Jan 2004-Science
TL;DR: A phylodynamic framework for the dissection of dynamic forces that determine the diversity of epidemiological and phylogenetic patterns observed in RNA viruses of vertebrates is introduced.
Abstract: A key priority for infectious disease research is to clarify how pathogen genetic variation, modulated by host immunity, transmission bottlenecks, and epidemic dynamics, determines the wide variety of pathogen phylogenies observed at scales that range from individual host to population. We call the melding of immunodynamics, epidemiology, and evolutionary biology required to achieve this synthesis pathogen “phylodynamics.” We introduce a phylodynamic framework for the dissection of dynamic forces that determine the diversity of epidemiological and phylogenetic patterns observed in RNA viruses of vertebrates. A central pillar of this model is the Evolutionary Infectivity Profile, which captures the relationship between immune selection and pathogen transmission.

1,248 citations

Journal ArticleDOI
13 Dec 2001-Nature
TL;DR: This work demonstrates recurrent epidemic travelling waves in an exhaustive spatio-temporal data set for measles in England and Wales and uses wavelet phase analysis, which allows for dynamical non-stationarity—a complication in interpreting spatio–temporal patterns in these and many other ecological time series.
Abstract: Spatio-temporal travelling waves are striking manifestations of predator-prey and host-parasite dynamics. However, few systems are well enough documented both to detect repeated waves and to explain their interaction with spatio-temporal variations in population structure and demography. Here, we demonstrate recurrent epidemic travelling waves in an exhaustive spatio-temporal data set for measles in England and Wales. We use wavelet phase analysis, which allows for dynamical non-stationarity--a complication in interpreting spatio-temporal patterns in these and many other ecological time series. In the pre-vaccination era, conspicuous hierarchical waves of infection moved regionally from large cities to small towns; the introduction of measles vaccination restricted but did not eliminate this hierarchical contagion. A mechanistic stochastic model suggests a dynamical explanation for the waves-spread via infective 'sparks' from large 'core' cities to smaller 'satellite' towns. Thus, the spatial hierarchy of host population structure is a prerequisite for these infection waves.

906 citations

Journal ArticleDOI
TL;DR: The homogeneous-mixing compartmental model is appropriate when host populations are nearly homogeneous, and can be modified effectively for a few classes of non-homogeneous networks, and in general, network models are more intuitive and accurate for predicting disease spread through heterogeneous host populations.
Abstract: Heterogeneity in host contact patterns profoundly shapes population-level disease dynamics. Many epidemiological models make simplifying assumptions about the patterns of disease-causing interactions among hosts. In particular, homogeneous-mixing models assume that all hosts have identical rates of disease-causing contacts. In recent years, several network-based approaches have been developed to explicitly model heterogeneity in host contact patterns. Here, we use a network perspective to quantify the extent to which real populations depart from the homogeneous-mixing assumption, in terms of both the underlying network structure and the resulting epidemiological dynamics. We find that human contact patterns are indeed more heterogeneous than assumed by homogeneous-mixing models, but are not as variable as some have speculated. We then evaluate a variety of methodologies for incorporating contact heterogeneity, including network-based models and several modifications to the simple SIR compartmental model. We conclude that the homogeneous-mixing compartmental model is appropriate when host populations are nearly homogeneous, and can be modified effectively for a few classes of non-homogeneous networks. In general, however, network models are more intuitive and accurate for predicting disease spread through heterogeneous host populations.

684 citations


Cites background or methods from "Dynamics of measles epidemics: scal..."

  • ...These extensions of the simple compartmental framework have included age-specific contact patterns and heterogeneities induced by spatial structure (Ball et al. 1997; Bjørnstad et al. 2002; Grenfell et al. 2002), but they do not allow for individual-level resolution....

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  • ...Bjornstad and Grenfell have developed the time-series SIR (TSIR) framework to model seasonal changes in contact patterns that influence the spread of measles (Bjørnstad et al. 2002; Grenfell et al. 2002)....

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Journal ArticleDOI
TL;DR: In this paper, the authors developed a model, the TSIR (Time-series Suscep- tible-Infected-Recovered) model, that can capture both endemic cycles and episodic out-breaks in measles.
Abstract: Before the development of mass-vaccination campaigns, measles exhibited persistent fluctuations (endemic dynamics) in large British cities, and recurrent outbreaks (episodic dynamics) in smaller communities. The critical community size separating the two regimes was ;300 000-500 000. We develop a model, the TSIR (Time-series Suscep- tible-Infected-Recovered) model, that can capture both endemic cycles and episodic out- breaks in measles. The model includes the stochasticity inherent in the disease transmission (giving rise to a negative binomial conditional distribution) and random immigration. It is thus a doubly stochastic model for disease dynamics. It further includes seasonality in the transmission rates. All parameters of the model are estimated on the basis of time series data on reported cases and reconstructed susceptible numbers from a set of cities in England and Wales in the prevaccination era (1944-1966). The 60 cities analyzed span a size range from London (3.3 3 10 6 inhabitants) to Teignmouth (10 500 inhabitants). The dynamics of all cities fit the model well. Transmission rates scale with community size, as expected from dynamics adhering closely to frequency dependent transmission (''true mass action''). These rates are further found to reveal strong seasonal variation, corresponding to high transmission during school terms and lower transmission during the school holidays. The basic reproductive ratio, R0, is found to be invariant across the observed range of host community size, and the mean proportion of susceptible individuals also appears to be constant. Through the epidemic cycle, the susceptible population is kept within a 3% interval. The disease is, thus, efficient in ''regulating'' the susceptible population—even in small cities that undergo recurrent epidemics with frequent extinction of the disease agent. Recolonization is highly sensitive to the random immigration process. The initial phase of the epidemic is also stochastic (due to demographic stochasticity and random immigration). However, the epidemic is nearly ''deterministic'' through most of the growth and decline phase.

555 citations


Cites background or methods from "Dynamics of measles epidemics: scal..."

  • ...In the companion paper (Grenfell et al. 2002), we show that the model is able to describe and regenerate the quantitative and qualitative properties of the different types of dynamics in these 60 cities....

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  • ...We have also checked that the dynamic behavior of the 10-d model corresponds to that of the 14-d model—annual cycles for high birth rates and/or low seasonality and biannual cycles for low birth rates and/or high seasonality (Grenfell et al. 2002)....

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  • ...…of the de-Ï2/I mographic stochasticity (see Materials and methods: The model: Scaling of rates and parameters), indicate that there is a threshold shortly after epidemic take off where the dynamics are approximately ‘‘deterministic’’ (in all but the smallest communities; see Grenfell et al. 2002)....

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  • ...In a companion paper (Grenfell et al. 2002) we show how the TSIR model captures the long-term dynamical behavior of measles, and discuss how the balance between noise and determinism scales in this highly nonlinear ecological system....

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  • ...In the companion paper (Grenfell et al. 2002), we show that this simple modeling framework for measles is capable of reproducing highly predictable fluctuations in large populations, and recurring episodic outbreaks in small populations....

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References
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Book
11 Jul 1991
TL;DR: This book discusses the biology of host-microparasite associations, dynamics of acquired immunity heterogeneity within the human community indirectly transmitted helminths, and the ecology and genetics of hosts and parasites.
Abstract: Part 1 Microparasites: biology of host-microparasite associations the basic model - statics static aspects of eradication and control the basic model - dynamics dynamic aspects of eradication and control beyond the basic model - empirical evidence of inhomogeneous mixing age-related transmission rates genetic heterogeneity social heterogeneity and sexually transmitted diseases spatial and other kinds of heterogeneity endemic infections in developing countries indirectly transmitted microparasites. Part 2 Macroparasites: biology of host-macroparasite associations the basic model - statics the basic model - dynamics acquired immunity heterogeneity within the human community indirectly transmitted helminths experimental epidemiology parasites, genetic variability, and drug resistance the ecology and genetics of host-parasite associations.

7,675 citations


"Dynamics of measles epidemics: scal..." refers background or methods in this paper

  • ...The characteristic time scale of the chain is 2 wk (corresponding roughly to the sum of incubation and infectious periods for the infection; Anderson and May 1991)....

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  • ...…closely match the well known biennial cycles of measles in the UK (and many other developed countries) during the 1950s and 1960s (Fine and Clarkson 1982, Anderson et al. 1984, Black 1984, Schenzle 1984, Anderson and May 1991, Cliff et al. 1993, Bolker and Grenfell 1995, Grenfell and Harwood 1997)....

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  • ...This form of prediction can have an important applied dimension since such shifts are often directly or indirectly anthropogenic in origin; relating, for example, to the effects of global warming (Fan et al. 1998) or vaccination (Anderson and May 1991)....

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  • ...This is a discrete-time nonlinear stochastic analogue of the well-known SIR (Susceptible–Infected–Recovered) model (Dietz and Schenzle 1985, Anderson and May 1991, Finkenstädt and Grenfell 2000, Bjørnstad et al. 2002)....

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  • ...1 follows directly from the extensive theoretical literature on childhood-disease dynamic modeling (see reviews in Anderson and May 1991, Grenfell and Dobson 1995, Mollison 1995)....

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Book
21 Aug 1973
TL;DR: Preface vii Preface to the Second Edition Biology Edition 1.
Abstract: Preface vii Preface to the Second Edition Biology Edition 1. Intoduction 3 2. Mathematical Models and Stability 13 3. Stability versus Complexity in Multispecies Models 4. Models with Few Species: Limit Cycles and Time Delays 79 5. Randomly Fluctuating Environments 109 6. Niche Overlap and Limiting Similarity 139 7. Speculations 172 Appendices 187 Afterthoughts for the Second Edition 211 Bibliography to Afterthoghts 234 Bibliography 241 Author Index 259 Subject Index 263

5,083 citations


"Dynamics of measles epidemics: scal..." refers background in this paper

  • ...A key issue in population dynamics is the relative importance of low-dimensional nonlinear deterministic forces and the, often high-dimensional, irregularities which we label as stochasticity (May 1973, Royama 1992, Ellner and Turchin 1995, Sugihara 1995, Stenseth et al. 1996, Leirs et al. 1997)....

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Journal ArticleDOI
19 Apr 1990-Nature
TL;DR: An approach is presented for making short-term predictions about the trajectories of chaotic dynamical systems, applied to data on measles, chickenpox, and marine phytoplankton populations, to show how apparent noise associated with deterministic chaos can be distinguished from sampling error and other sources of externally induced environmental noise.
Abstract: An approach is presented for making short-term predictions about the trajectories of chaotic dynamical systems. The method is applied to data on measles, chickenpox, and marine phytoplankton populations, to show how apparent noise associated with deterministic chaos can be distinguished from sampling error and other sources of externally induced environmental noise.

1,652 citations

Book
01 Jan 1992
TL;DR: Theoretical bases of population dynamics, Dynamics of a host-parasitoid interaction system: Utida's experimental study, and statistical analysis of population fluctuations.
Abstract: Part I: Theoretical bases of population dynamics. Basic properties and structure of population processes. Structures and patterns of population processes. Statistical analysis of population fluctuations. Population process models. Part II: Analysis of classic cases. Analysis of lynx 10-year cycle. Snowshoe hare demography. Density effects on the dynamics of a single-species population: Utida's classic experiments on the azuki bean weevil. Dynamics of a host-parasitoid interaction system: Utida's experimental study. Dynamics of the spruce budworm outbreak processes. Epilogue. Bibliography. Index.

1,199 citations


"Dynamics of measles epidemics: scal..." refers background in this paper

  • ...A key issue in population dynamics is the relative importance of low-dimensional nonlinear deterministic forces and the, often high-dimensional, irregularities which we label as stochasticity (May 1973, Royama 1992, Ellner and Turchin 1995, Sugihara 1995, Stenseth et al. 1996, Leirs et al. 1997)....

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