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Journal ArticleDOI

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

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TLDR
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.

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Citations
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Seasonality and the dynamics of infectious diseases.

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.
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Unifying the epidemiological and evolutionary dynamics of pathogens.

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.
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Travelling waves and spatial hierarchies in measles epidemics

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.
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When individual behaviour matters: homogeneous and network models in epidemiology

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.
Journal ArticleDOI

Dynamics of measles epidemics: Estimating scaling of transmission rates using a time series sir model

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.
References
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Book

Infectious Diseases of Humans: Dynamics and Control

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.
Book

Stability and Complexity in Model Ecosystems

TL;DR: Preface vii Preface to the Second Edition Biology Edition 1.
Journal ArticleDOI

Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series

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.
Book

Analytical population dynamics

T. Royama
TL;DR: Theoretical bases of population dynamics, Dynamics of a host-parasitoid interaction system: Utida's experimental study, and statistical analysis of population fluctuations.
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