Time series modelling of childhood diseases: a dynamical systems approach

By Bärbel F. Finkenstädt, Bryan T. Grenfell. 2000

395 citations recorded by the source corpus.

Subfields: Ecology, Modeling and Simulation, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/4598960b13b166750ab84666b3fa8ff2cc95baea

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In Ecology

In Modeling and Simulation

In Public Health, Environmental and Occupational Health

Citations in the corpus

Seasonality and the dynamics of infectious diseases. (2006, background): “Variation driven by the school year can also affect the degree of spatial heterogeneity, as mixing patterns tend to be spatially local during school terms and better-mixed during vacation periods when people visit relatives and friends (Finkenstädt & Grenfell 2000).”
Simple temporal models for ecological systems with complex spatial patterns (2002, methodology): “In this sense, our approach is similar to that used in disease models that incorporate exponents to modify the mass-action transmission term and empirically take into account heterogeneous 2002 Blackwell Science Ltd/CNRS mixing (e.g. Gubbins & Gilligan 1997; Finkenstädt & Grenfell 2000).”
Mathematical models to characterize early epidemic growth: A Review (2016, background): “While these observations were deemed important among experts at the time, developing the classic theory of infectious disease to accommodate the possibility of sub-exponential growth patterns has progressed slowly [5,7,10,24,105,107,108].”
Truncated Importance Sampling (2008, methodology): “We consider a model for cholera dynamics that is a continuous time version of a discrete time compartment model considered by Koelle and Pascual (2004), following a similar discrete time model for measles by Finkenstädt and Grenfell (2000).”
External forcing of ecological and epidemiological systems: a resonance approach (2004, background): “There is now also helps to explain diseases [7,27,28] only bifurcation diagrams for , whooping cough and et al. [7] with δ ∼ 0 .”
POPULATION TIME SERIES: PROCESS VARIABILITY, OBSERVATION ERRORS, MISSING VALUES, LAGS, AND HIDDEN STATES (2004, result): “The reporting rate of 0.495 is slightly lower than the 0.52 estimated using a more elaborate analysis (Finkenstädt and Grenfell 2000, Bjørnstad et al. 2002).”
Plug-and-play inference for disease dynamics: measles in large and small populations as a case study (2009, background): “Accordingly, there has been a great deal of recent interest in techniques that allow one to fit mechanistic models to time-series data (Gibson & Renshaw 1998; Finkenstädt & Grenfell 2000; Gibson & Renshaw 2001; Grenfell et al.”